PPT - What Tools Help Farmers Make Data-Driven Growing Decisions_compressed PowerPoint Presentation

What Tools Help Farmers Make Data-Driven Growing Decisions_compressed

Dec 03, 2025

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This document presents a detailed overview of how digital technologies support farmers in making informed, data-driven growing decisions. It explains the role of remote sensing, IoT tools, predictive analytics, and farm management platforms in improving accuracy, efficiency, and sustainability across agricultural operations. Ideal for readers seeking insights into the evolution of modern farming tools and decision-making systems.

What Tools Help Farmers Make Data-Driven Growing Decisions_compressed

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QNT 561 Motivated Minds /newtonhelp.com

QNT 561 Entire Course (With Final Guide) For more course tutorials visit www.newtonhelp.com QNT 561 Final Exam Guide (New, 2017) QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Financial Data) QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Consumer Food) QNT 561 Week 2 Case Study MBA Schools in Asia Pacific (2 Papers) QNT 561 Week 3 Case Study SuperFun Toys (2 Papers) QNT 561 Week 3 Assignment Expansion Strategy and Establishing a Re-Order Point QNT 561 Week 4 Case the Payment Time QNT 561 Week 5 Spicy Wings Case Study QNT 561 Week 5 Team One-Sample Hypothesis Testing (Election Results, SpeedX) QNT 561 Week 6 Signature Assignment (Hospital) QNT 561 Week 6 Signature Assignment (Consumer Food) =============================================== QNT 561 Entire Course (Without Final Guide) For more course tutorials visit www.newtonhelp.com QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Financial Data) QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Consumer Food) QNT 561 Week 2 Case Study MBA Schools in Asia Pacific (2 Papers) QNT 561 Week 3 Case Study SuperFun Toys (2 Papers) QNT 561 Week 3 Assignment Expansion Strategy and Establishing a Re-Order Point QNT 561 Week 4 Case the Payment Time QNT 561 Week 5 Spicy Wings Case Study QNT 561 Week 5 Team One-Sample Hypothesis Testing (Election Results, SpeedX) QNT 561 Week 6 Signature Assignment (Hospital) QNT 561 Week 6 Signature Assignment (Consumer Food) =============================================== QNT 561 Final Exam Guide (Score 29/30 New, 2019) For more course tutorials visit www.newtonhelp.com 1. James Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB banks. His staff randomly selected personnel files for 100 tellers in the Southeast Region and determined that their mean training time was 25 hours. Assume that the population standard deviation is 5 hours. The 95% confidence interval for the population mean of training times is 2. If x is a binomial random variable with n=10 and p=0.8, the mean value of x is______ 3. According to the central limit theorem, for samples of size 64 drawn from a population with µ =800 and σ = 56, the standard deviation of the sampling distribution of sample means would equal ______ 4. Life tests performed on a sample of 13 batteries of a new model indicated: (1) an average life of75 months, and (2) a standard deviation of 5 months. Other battery models, produced by similar processes, have normally distributed life spans. The 98% confidence interval for the population mean life of the new model is _________ 5. A large national company is considering negotiating cellular phone rates for its employees Human Resource department would like to estimate the proportion of its employee population who own an Apple iPhone. A random sample of size 250 is taken and 40% of the sample own and iPhone.. The 95% confidence interval to estimate the population proportion is _______ 6. The number of bags arriving on the baggage claim conveyor belt in a 3 minute time period would best be modeled with the ________ 7. The weight of a USB flash drive is 30 grams and is normally distributed. Periodically, quanlity control inspectors at Dallas Flash Drives randomly select a sample of 17 USB flash drive. If the mean weight of the USB flash drives is too heavy or too light the machinery is shut down for adjustment; otherwise, the production process continues. The last sample showed a meanand standard deviation of 31.9 and 1.8 grams, respectively. Using a = 0.10, theappropriate decision is_______ 8. Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. He plans to use a 95% confidence interval estimate of the proportion of e-mail messages that are non-business; he will accept a 0.05 error. Previous studies indicate that approximately 30% of employee e-mail is not business related. Elwin should sample _______ e-mail messages 9. The following frequency distribution was constructed for the wait times in the emergency room The frequency distribution reveals that the wait times in the emergency room are _______ 10. The number of cars arriving at a toll booth in five-minute intervals is Poisson distributed with a mean of 3 cars arriving in five-minute time intervals. The probability of 5 cars arriving over a five-minute interval is ________ 11. The number of finance majors within the School of Business is an example of _______ 12. According to the central limit theorem, for samples of size 64 drawn from a population with µ = 800 and σ = 56, the mean of the sampling distribution of sample means would equal _______ 13. Consider the following null and alternative hypotheses Ho: m ≤ 67 Ha: m > 67 These hypotheses ___________ 14. A market research team compiled the following discrete probability distribution on the numberof sodas the average adult drinks each day. In this distribution, x represents the number of sodas which an adult drinks x P(x) 0 0.30 1 0.10 2 0.50 3 0.10 The mean (average) value of x is ______________ 15. A researcher wants to determine the sample size necessary to adequately conduct a study to estimate the population mean to within 5 points. The range of population values is 80 and the researcher plans to use a 90% level of confidence. The sample size should be at least ______ 16. The mean life of a particular brand of light bulb is 1200 hours. If you know that at about 95% of this brand of bulbs will last between 1100 and 1300 hours, then what is the standard deviation of the light bulbs’ life? 17. Completion time (from start to finish) of a building remodeling project is normally distributed with a mean of 200 work-days and a standard deviation of 10 work-days. To be 99% sure that we will not be late in completing the project, we should request a completion time of ______ work-day. 18. A large industrial firm allows a discount on any invoice that is paid within 30 days. Of all invoices, 10% receive the discount. In a company audit, 10 invoices are sampled at random. The probability that fewer than 3 of the 10 sampled invoices receive the discount is approximately_______________. 19. Suppose a population has a mean of 400 and a standard deviation of 24. If a random sample of size 144 is drawn from the population, the probability of drawing a sample with a mean less than 402 is _______ 20. If x is a binomial random variable with n=10 and p=0.8, what is the probability that x is equal to 4 ? 21. The normal distribution is used to test about a population mean for large samples if the population standard deviation is known. "Large" is usually defined as _______ 22. Lucy Baker is analyzing demographic characteristics of two television programs, Americandol (population 1) and 60 Minutes (population 2). Previous studies indicate no difference in the ages of the two audiences (The mean age of each audience is the same.) Lucy plans to test this hypothesis using a random sample of 100 from each audience. Her null hypothesis is 23. Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the East Coast Warehouse has consistently out-performed the West Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 372 orders within 24 hours. Maureen's alternate hypothesis is _______ 24. Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), monitors the default rate on personal loans at the AFB member banks. One of her standards is "no more than 5% of personal loans should be in default." On each Friday, the default rate is calculated for a sample of 500 personal loans. Last Friday's sample contained 30 defaulted loans. Ophelia's null hypothesis is _______. 25. Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of a new toothpaste package. Her staff reports that 17% of a random sample of 200 households prefers the new package to all other package designs. If Catherine concludes that 17% of all households prefer the new package, she is using _______. 26. The empirical rule says that approximately what percentage of the values would be within 2 standard deviations of the mean in a bell shaped set of data 27. Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children’s cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. Kathleen's null hypothesis is _____________ 28. The expected (mean) life of a particular type of light bulb is 1,000 hours with a standard deviation of 50 hours. The life of this bulb is normally distributed. What is the probability that a randomly selected bulb would last fewer than 940 hours 29. The mean life of a particular brand of light bulb is 1200 hours and the standard deviation is 75 hours. Tests show that the life of the bulb is approximately normally distributed. It can be concluded that approximately 68% of the bulbs will last between _______. 30. A market researcher is interested in determining the average income for families in San Mateo County, California. To accomplish this, she takes a random sample of 300 families from the county and uses the data gathered from them to estimate the average income for families of the entire county. This process is an example of _______. =============================================== QNT 561 Final Exam Guide (Score 29/30 New, 2019) For more course tutorials visit www.newtonhelp.com 1. James Desreumaux, VP of Human Resources of American First Banks (AFB), is reviewing the employee training programs of AFB banks. His staff randomly selected personnel files for 100 tellers in the Southeast Region and determined that their mean training time was 25 hours. Assume that the population standard deviation is 5 hours. The 95% confidence interval for the population mean of training times is 2. If x is a binomial random variable with n=10 and p=0.8, the mean value of x is______ 3. According to the central limit theorem, for samples of size 64 drawn from a population with µ =800 and σ = 56, the standard deviation of the sampling distribution of sample means would equal ______ 4. Life tests performed on a sample of 13 batteries of a new model indicated: (1) an average life of75 months, and (2) a standard deviation of 5 months. Other battery models, produced by similar processes, have normally distributed life spans. The 98% confidence interval for the population mean life of the new model is _________ 5. A large national company is considering negotiating cellular phone rates for its employees Human Resource department would like to estimate the proportion of its employee population who own an Apple iPhone. A random sample of size 250 is taken and 40% of the sample own and iPhone.. The 95% confidence interval to estimate the population proportion is _______ 6. The number of bags arriving on the baggage claim conveyor belt in a 3 minute time period would best be modeled with the ________ 7. The weight of a USB flash drive is 30 grams and is normally distributed. Periodically, quanlity control inspectors at Dallas Flash Drives randomly select a sample of 17 USB flash drive. If the mean weight of the USB flash drives is too heavy or too light the machinery is shut down for adjustment; otherwise, the production process continues. The last sample showed a meanand standard deviation of 31.9 and 1.8 grams, respectively. Using a = 0.10, theappropriate decision is_______ 8. Elwin Osbourne, CIO at GFS, Inc., is studying employee use of GFS e-mail for non-business communications. He plans to use a 95% confidence interval estimate of the proportion of e-mail messages that are non-business; he will accept a 0.05 error. Previous studies indicate that approximately 30% of employee e-mail is not business related. Elwin should sample _______ e-mail messages 9. The following frequency distribution was constructed for the wait times in the emergency room The frequency distribution reveals that the wait times in the emergency room are _______ 10. The number of cars arriving at a toll booth in five-minute intervals is Poisson distributed with a mean of 3 cars arriving in five-minute time intervals. The probability of 5 cars arriving over a five-minute interval is ________ 11. The number of finance majors within the School of Business is an example of _______ 12. According to the central limit theorem, for samples of size 64 drawn from a population with µ = 800 and σ = 56, the mean of the sampling distribution of sample means would equal _______ 13. Consider the following null and alternative hypotheses Ho: m ≤ 67 Ha: m > 67 These hypotheses ___________ 14. A market research team compiled the following discrete probability distribution on the numberof sodas the average adult drinks each day. In this distribution, x represents the number of sodas which an adult drinks x P(x) 0 0.30 1 0.10 2 0.50 3 0.10 The mean (average) value of x is ______________ 15. A researcher wants to determine the sample size necessary to adequately conduct a study to estimate the population mean to within 5 points. The range of population values is 80 and the researcher plans to use a 90% level of confidence. The sample size should be at least ______ 16. The mean life of a particular brand of light bulb is 1200 hours. If you know that at about 95% of this brand of bulbs will last between 1100 and 1300 hours, then what is the standard deviation of the light bulbs’ life? 17. Completion time (from start to finish) of a building remodeling project is normally distributed with a mean of 200 work-days and a standard deviation of 10 work-days. To be 99% sure that we will not be late in completing the project, we should request a completion time of ______ work-day. 18. A large industrial firm allows a discount on any invoice that is paid within 30 days. Of all invoices, 10% receive the discount. In a company audit, 10 invoices are sampled at random. The probability that fewer than 3 of the 10 sampled invoices receive the discount is approximately_______________. 19. Suppose a population has a mean of 400 and a standard deviation of 24. If a random sample of size 144 is drawn from the population, the probability of drawing a sample with a mean less than 402 is _______ 20. If x is a binomial random variable with n=10 and p=0.8, what is the probability that x is equal to 4 ? 21. The normal distribution is used to test about a population mean for large samples if the population standard deviation is known. "Large" is usually defined as _______ 22. Lucy Baker is analyzing demographic characteristics of two television programs, Americandol (population 1) and 60 Minutes (population 2). Previous studies indicate no difference in the ages of the two audiences (The mean age of each audience is the same.) Lucy plans to test this hypothesis using a random sample of 100 from each audience. Her null hypothesis is 23. Maureen McIlvoy, owner and CEO of a mail order business for wind surfing equipment and supplies, is reviewing the order filling operations at her warehouses. Her goal is 100% of orders shipped within 24 hours. In previous years, neither warehouse has achieved the goal, but the East Coast Warehouse has consistently out-performed the West Coast Warehouse. Her staff randomly selected 200 orders from the West Coast Warehouse (population 1) and 400 orders from the East Coast Warehouse (population 2), and reports that 190 of the West Coast Orders were shipped within 24 hours, and the East Coast Warehouse shipped 372 orders within 24 hours. Maureen's alternate hypothesis is _______ 24. Ophelia O'Brien, VP of Consumer Credit of American First Banks (AFB), monitors the default rate on personal loans at the AFB member banks. One of her standards is "no more than 5% of personal loans should be in default." On each Friday, the default rate is calculated for a sample of 500 personal loans. Last Friday's sample contained 30 defaulted loans. Ophelia's null hypothesis is _______. 25. Catherine Chao, Director of Marketing Research, is evaluating consumer acceptance of a new toothpaste package. Her staff reports that 17% of a random sample of 200 households prefers the new package to all other package designs. If Catherine concludes that 17% of all households prefer the new package, she is using _______. 26. The empirical rule says that approximately what percentage of the values would be within 2 standard deviations of the mean in a bell shaped set of data 27. Medical Wonders is a specialized interior design company focused on healing artwork. The CEO, Kathleen Kelledy claims that artwork has healing effects for patients staying in a hospital, as measured by reduced length of stay. Her current client is a children’s cancer hospital. Kathleen is interested in determining the effect of three different pieces of healing artwork on children. She chooses three paintings (a horse photo, a bright abstract, and a muted beach scene) and randomly assigns six hospital rooms to each painting. Kathleen's null hypothesis is _____________ 28. The expected (mean) life of a particular type of light bulb is 1,000 hours with a standard deviation of 50 hours. The life of this bulb is normally distributed. What is the probability that a randomly selected bulb would last fewer than 940 hours 29. The mean life of a particular brand of light bulb is 1200 hours and the standard deviation is 75 hours. Tests show that the life of the bulb is approximately normally distributed. It can be concluded that approximately 68% of the bulbs will last between _______. 30. A market researcher is interested in determining the average income for families in San Mateo County, California. To accomplish this, she takes a random sample of 300 families from the county and uses the data gathered from them to estimate the average income for families of the entire county. This process is an example of _______. . =============================================== QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Consumer Food) For more course tutorials visit www.newtonhelp.com Purpose of Assignment The purpose of this assignment to orient students to the key concepts in statistics. This assignment will introduce students to the language of statistics. Students will also get a chance to warm-up on evaluating some basic descriptive statistics using Excel® prior to the course start. Assignment Steps This assignment has an Excel dataset spreadsheet attached. Resource: Microsoft Excel, Statistics Concepts and Descriptive Measures Data Set Download the Statistics Concepts and Descriptive Measures Data Set. Choose: • Financial Answer each of the following in a total of 90 words: • For each column, identify whether the data is qualitative or quantitative. • Identify the level of measurement for the data in each column. • For each column containing quantitative data: • Evaluate the mean and median • Interpret the mean and median in plain non-technical terms • Use the Excel =AVERAGE function to find the mean • Use the Excel =MEDIAN function to find the median • For each column containing quantitative data: • Evaluate the standard deviation and range • Interpret the standard deviation and range in plain non-technical terms • Use the Excel =STDEV.S function to find the standard deviation • For range (maximum value minus the minimum value), find the maximum value using the Excel =MAX function and find the minimum value using the Excel's =MIN function Annual Food Spending ($) Annual Household Income ($) Non mortgage household debt ($) 8909 56697 23180 5684 35945 7052 10706 52687 16149 14112 74041 21839 13855 63182 18866 15619 79064 21899 2694 25981 8774 9127 57424 15766 13514 72045 27685 6314 38046 8545 7622 52408 28057 4322 41405 6998 3805 29684 4806 6674 49246 13592 7347 41491 4088 2911 26703 15876 8026 48753 16714 8567 55555 16783 10345 71483 21407 8694 50980 19114 8821 46403 7817 8678 51927 14415 14331 84769 17295 9619 59062 16687 9286 57952 14161 8206 58355 19538 16408 81694 15187 12757 69522 14651 17740 96132 0 =============================================== QNT 561 Week 1 Assignment Statistics Concepts and Descriptive Measures Instructions (Financial Data) For more course tutorials visit www.newtonhelp.com Purpose of Assignment The purpose of this assignment to orient students to the key concepts in statistics. This assignment will introduce students to the language of statistics. Students will also get a chance to warm-up on evaluating some basic descriptive statistics using Excel® prior to the course start. Assignment Steps This assignment has an Excel dataset spreadsheet attached. Resource: Microsoft Excel, Statistics Concepts and Descriptive Measures Data Set Download the Statistics Concepts and Descriptive Measures Data Set. Choose: • Financial Answer each of the following in a total of 90 words: • For each column, identify whether the data is qualitative or quantitative. • Identify the level of measurement for the data in each column. • For each column containing quantitative data: • Evaluate the mean and median • Interpret the mean and median in plain non-technical terms • Use the Excel =AVERAGE function to find the mean • Use the Excel =MEDIAN function to find the median • For each column containing quantitative data: • Evaluate the standard deviation and range • Interpret the standard deviation and range in plain non-technical terms • Use the Excel =STDEV.S function to find the standard deviation • For range (maximum value minus the minimum value), find the maximum value using the Excel =MAX function and find the minimum value using the Excel's =MIN function Company Type Total Revenues AFLAC 6 7251 Albertson's 4 14690 Allstate 6 20106 Amerada Hess 7 8340 American General 6 3362 American Stores 4 19139 Amoco 7 36287 Arco Chemical 2 3995 Ashland 7 14319 Atlantic Richfield 7 19272 Bausch & Lomb 5 1916 Baxter International 5 6138 Bristol-Myers Squibb 5 16701 Burlington Coat 1 1777 Central Maine Power 3 954 Chevron 7 41950 CIGNA 6 14935 Cinergy 3 4353 Dayton Hudson 1 27757 Dillard's 1 6817 Dominion Resources 3 7678 Dow Chemical 2 20018 DPL 3 1356 E. I. DuPont DeNemours 2 46653 Eastman Chemical 2 4678 Edison International 3 9235 Engelhard 2 3631 Entergy 3 9562 Equitable 6 9666 =============================================== QNT 561 Week 1 DQ 1 For more course tutorials visit www.newtonhelp.com How may variance and standard deviation be applied to a real-world business-related problem? Provide a specific application in which these measures are useful. =============================================== QNT 561 Week 1 DQ 2 For more course tutorials visit www.newtonhelp.com When would you use Chebyshev’s theorem and the empirical rule in business? How are they calculated? Provide one real-life example that requires Chebyshev’s theorem and one that requires the empirical rule. =============================================== QNT 561 Week 1 Individual My Statslab Problem Set For more course tutorials visit www.newtonhelp.com 1. What is statistics? 2. Explain the difference between descriptive and inferential statistics. 3. Explain the difference between qualitative and quantitative data. 4. Explain how populations and variables differ. 5. Explain how populations and samples differ. 6. What is a representative sample? 7. Explain the difference between a population and a process. 8. Define statistical thinking. 9. Suppose you’re given a data set that classifies each sample unit into one of four categories: A, B, C or D. You plan to create a computer database consisting of these data, and you decide to code the data as A = 1, B = 2, C = 3 and D = 4. Are the data consisting of the classifications A, B, C and D qualitative or quantitative? After the data are in out as 1, 2, 3, or 4, are they qualitative or quantitative? 10. Identify each of the following variables as qualitative or quantitative. 11. Each month interviewers visit about 69,000 of the 93 million households in the region and question the occupants over 18 years of age about their educational status. Their responses enable the interviewers to estimate the percentage of people in the labor force who are college educated. Compare parts a through c. 12. Complete the table to the right? 13. In one university, language professors incorporated a 10-week extensive program to improve students’ Japanese reading comprehension. The professors collected 283 books originally written for Japanese children and required their students to read at least 40 of them as part of the grade in the course. The books were categorized into reading levels (color-coded for easy selection) according to length and complexity. Complete parts a through c. 14. A group of marketing professors asked every fourth adult entrant to a mall to participate in a study. A total of 119 shoppers agreed to answer the question, “Made locally” means what percentage of local labor and materials?” The responses of the 119 shoppers are summarized in the table to the right. Complete parts a through c below. 15. Graph the relative frequency histogram for the 300 measurements summarized in the relative frequency table to the right. 16. If jobs arrive at a particular work center at a faster rate than they depart, the work center impedes the overall production process and is referred to as a bottleneck. The data in the table were collected by an operations manager for use in investigating a potential bottleneck work center. 17. A data set contains the observations 3, 5, 4, 2, 3. Find the following values. 18. Calculate the mean and Median of the following grade point averages. 2.5 2.9 3.6 2.6 3.2 3.7 19. Five banks have been ranked by the amount charged to credit and debit cards issued by the banks. The table to the right gives the total amount charged in 2007 for the top ranked banks. 20. The data on the age (in years) of each of the 20 most powerful women in a region are shown below. 49 62 52 ……………………………………….64 21. The salaries of superstar professional athletes receive much attention in the media. The multimillion-dollar long-term contract is now commonplace among this elite group. Nevertheless, rarely does a season pass without negotiations between one or more of the players’ associations and team owners for additional salary and fringe benefits for all players in their particular sports. Complete parts a and b below. 22. Calculate the range, variance, and standard deviation for the following sample. 3, -3,2,……………….4 23. A university’s language professors incorporated a 10-week extensive redaing program into a second-semester Japanese language course in an effort to improve students’ Japanese reading comprehension. Fourteen students participated in this reading program. Complete parts a through c. 24. A country’s Energy Information Administration monitors all nuclear power plants operating in that country. The table to the right lists the number of active nuclear power plants operating in each of a sample of 10 states. 25. A study of 100,000 first-time candidates for the CPA exam found that the mean number of semester hours of college credit taken by the candidates was 144.58 hours. The standard deviation was reported to be 15.73 hours. Complete parts a through c. 26. Compute the z-score corresponding to each of the values of x below. 27. Compare the z-scores to decide which of the x values below lie the greatest above the mean and the greatest distance below the mean. 28. A sample data set has a mean of 74 and a standard deviation of 10. Determine whether each of the following sample measurements are outliers. 29. Consider the horizandal box shown to the right. 30. Educators are constantly evaluating the efficacy of public schools in the education and training of students. One quantitative assessment of change over time is the difference in scores on the SAT. The table below contains the average SAT scores for 10 states for the years 1988 and 2005. 31. Data on annual rainfall, maximum daily temperature, percentage of planet cover, and number of anti species recorded at each of 11 study sites are given in the accompanying table. Complete parts a through c. 32. Determine whether the random variable is discrete or continuous. 33. The random variable x has the following discrete probability distribution. Complete parts a through f. 34. X intercept, y intercept 35. If x is a binomial random variable, compute p(x) for each of the cases below. 36. According to a business magazine, 30% all small businesses owned by non-Hispanic whites nationwide are women-owned firms. 37. According to a certain golf association, the weight of the golf ball ball shall not be greater than 1.620 ounces (45.93 grams). The velocity of the ball shall not be greater than 250 feet per second. The golf association periodically checks the specifications of golf balls using sampling. Five dozen of each kind are sampled, and if more than three do not meet size or velocity requirements, that kind of ball is removed from the golf association’s approved list. Complete parts a and b. 38. Find the area under the standard normal probability distribution between the following pairs of z-scores. 39. Suppose the random variable x is the best described by a normal distribution with µ = 32 and = 5. Find the z-score that corresponds to each of the following x-values. 40. The mean gas mileage for a hybrid car is 56 miles per gallon. Suppose that the gasoline mileage is approximately normally distributed with a standard deviation of 3.2 miles per gallon. 41. Personnel tests are designed to test a job applicant’s cognitive and/or physical abilities. A particular dexterity test is administered nationwide by a private testing service. It is known that for all tests administered last year, the distribution of scores was approximately normal with mean 76 and standard deviation 7.8. 42. Determine evidence to support or contradict the assumption that the data to the right come from an approximately normal distribution. 43. An airport terminal handles an average of 3,000 international passengers an hour, but is capable of handling twice that number. Also after scanning all luggage, 20% arriving international passengers are detained for intrusive luggage inspection. The inspection facility can handle 500 passengers an hour without unreasonable delays for the travelers. Complete parts a through c. 44. Will the sampling distribution of always be approximately normally distributed? Explain. 45. The number of semester hours of college credit taken by first-time candidates for a certain professional exam has a distribution with a mean of 127 hours and a standard deviation of 14 hours. Consider a random sample of 100 first-time candidates for the exam and let represent the mean number of hours of college credit taken for the sample. Complete parts a through e below. =============================================== QNT 561 Week 1 Lab Work (New) For more course tutorials visit www.newtonhelp.com Chapter 2: Ex 4) Two Thousand three hundred frequent business travelers are asked which Midwestern city they prefer: Indianapolis, Saint Louis, Chicago or Milwaukee. 388 liked Indianapolis best, 450 liked Saint Louis, 1212 liked Chicago and the remainder prefers Milwaukee. Develop a frequency table and a relative frequency table to summarize this information (Round relative frequency to 3 decimal places.) Ex 6) A small business consultant is investigating the performance of several companies. The fourth-quarter sales for last year (in thousands of dollars) for the selected companies were: The consultant wants to include a chart in his report comparing the sales of six companies. Identify a bar chart that compares the fourth-quarter sales of these corporations. Ex 12) the quick change oil company has a number of outlets in the metropolitan Seattle area. The daily number of oil changes at the Oak Street outlet in the past 20 days is: a. How many classes would you recommend? d. Organize the number of oil changes into a frequency distribution. Ex 14) the food services division of Cedar River Amusement Park Inc, is studying the amount that families who visit the amusement park spend per day on food and drink. A sample of 40 families who visited the park yesterday revealed they spend the following amounts: a. Organize the data into a frequency distribution, using seven classes and 15 as the lower limit of the first class. What class interval did you select? b. Where do the data tend to cluster? Ex 18) Ecommerce.com, a large Internet retailer, is studying the lead time (elapsed time between when an order is placed and when it is filled) for a sample of recent orders. The lead time are reported in days. a. How many orders were studied? b. What is the midpoint of the first class? c. What are the coordinates of the first class for a frequency olygon assuming we draw a frequency polygon using the midpoints? Ex 20) The following cumulative frequency polygon shows the selling price ($000) of house sold in the Billings, Montana, area a. How many orders were studied? b. What is the class interval? c. One hundred homes sold for less than what amount? d. About 75% of the homes sold for less than what amount? e. Estimate the no of homes $150,000 up to $200,000 class. f. About how many homes sold for less than $225,000? =============================================== QNT 561 Week 2 Case Study MBA Schools in Asia Pacific (2 Papers) For more course tutorials visit www.newtonhelp.com Review the Case Study: MBA Schools in Asia-Pacific and the Case Study: MBA Schools in Asia-Pacific data set. Prepare a 1,050-word managerial report for your boss. Use the following questions for guidelines and directions on what to include in the report: 1. What is the type of data (Quantitative or Qualitative) for each of the columns (variables) in the dataset? If quantitative, is the data discrete or continuous? Neatly summarize your response in a table for all the columns (variables). 2. Using Excel, find the mean, median, standard deviation, minimum, maximum, and the three quartiles for each of the quantitative variables identified in part 1 above. Neatly summarize in a table on this document. Comment on what you observe. 3. What are the minimum and maximum full-time enrollments? Which schools have the minimum and maximum full-time enrollments? 4. What is the average number of students per faculty member? Is this low or high? What does this mean to prospective applicants who are interested in pursuing an MBA in one of the leading international business schools? 5. What are the mean, median, and modal ages? What does this mean to prospective applicants? 6. What is the mean percentage of foreign students? How many and which schools have 1% and 0% foreign students? Which schools have highest percentage of foreign students? Please state these percentages. 7. What percentage of schools require the GMAT test? 8. What percentage of schools require English tests such as Test of English as a Foreign Language (TOEFL)? 9. What percentage of schools require work experience? From this percentage, does this appear to be a significant factor in gaining admissions? 10. What are the mean and median starting salaries? Which schools have the minimum and maximum starting salaries? How much are these minimum and maximum salaries? 11. What are the mean tuition for foreign students and for local students? Does there appear to be a significant difference? What is the difference between the two means? 12. How many schools require work experience and how many of them don't? What is the mean starting salary for schools requiring work experience? What is the mean starting salary for schools requiring no work experience? 13. How many schools require English tests and how many don't? What is the mean starting salary for schools requiring English tests? What is the mean starting salary for schools requiring no English tests? 14. Does there appear to be a correlation between age and starting salaries? Comment on the strength and the direction of the correlation. 15. Comment on the skewness for the data on starting salaries: 1. Plot a histogram and determine the skewness. 2. Find the skewness coefficient. 3. Find the mean, median, and mode for starting salaries and compare the three measures to determine skewness. 16. Finally, use Empirical Rule on the starting salaries and determine whether the salaries follow the Empirical Rule. The pursuit of a higher education degree in business is now international. A survey shows more and more Asians choose the master of business administration (MBA) degree route to corporate success. As a result, the number of applicants for MBA courses at Asia-Pacific schools continues to increase. Across the region, thousands of Asians show an increasing willingness to temporarily shelve their careers and spend two years in pursuit of a theoretical business qualification. Courses in these schools are notoriously tough and include statistics, economics, banking, marketing, behavioral sciences, labor relations, decision making, strategic thinking, business law, and more. After your MBA, you get a job at Bloomberg in its media division, Bloomberg Business. Your division publishes reviews and rankings for business schools in the US and internationally. Because of your strong analytical education from University of Phoenix, your boss assigns you to work on preparing an analysis for data gathered for leading business schools in the Asia-Pacific. The data set in the Excel® file shows some of the characteristics of the leading Asia-Pacific business schools. =============================================== QNT 561 Week 2 DQ 1 For more course tutorials visit www.newtonhelp.com What are some examples of operational definitions in research design within your profession? For example, in the education field, graduation rate and retention rate are important operational definitions to measure progress of students. Likewise other professions have common metrics and definitions. Identify some metrics and operational definitions from your own career or a profession that you know well. Tell us why you think it is important! =============================================== QNT 561 Week 2 DQ 2 For more course tutorials visit www.newtonhelp.com What is the purpose of sampling? What are some concerns and dangers of sampling? How important is the sample design to data validity? Explain. Provide an example where a sample might misrepresent data validity. For example, reflect on the current political campaign and the pollsters! =============================================== QNT 561 Week 2 Individual My Statslab Problem Set For more course tutorials visit www.newtonhelp.com 1. A random sample of 87 observations produced a mean = 25.7 and a standard deviation s = 2.6. 2. Health care workers who use latex gloves with glove powder on a daily basis are particularly susceptible to developing a latex allergy. Each in a sample of 50 hospital employees who were diagnosed with a latex allergy based on a skin-prick test reported on their exposure to latex gloves. Summary statistics for thr number of latex gloves used per week are = 19.4 and s = 11.6. Complete parts (a) – (d). 3. Each child in a sample of 62 low-income children was administered a language and communication exam. The sentence complexity scores had a mean of 7.63 and a standard deviation of 8.92. Complete parts a through d. 4. The random sample shown below was selected from a normal distribution. 4, 10, 7,….2. Complete parts a and b. 5. Periodically, a town water department tests the drinking water of homeowners such as lead. The lead levels in water specimens collected for a sample of 10 residents of the town had a mean of 3.1 mg/L and a standard deviation of 1.2 mg/L. Complete parts a through c. 6. A random sample of size n = 250 yielded = 0.20. 7. A newspaper reported that 50% of people say that some coffee shops are overpriced. The source of this information was a telephone survey of 40 adults. 8. An accounting firm annually monitors a certain mailing service’s performance. One parameter of interest is the percentage of mail delivered on time. In a sample of 303,000 items mailed between Dec. 10 and Mar. 3__ the most difficult delivery season due to bad weather and holidays__ the accounting firm determined that 245,200 items were delivered on time. Use this information to make a statement about the likelihood of an item being on time by that mailing service. 9. Suppose oyu’re given a data set that classifies each sample unit into one of four categories: A, B, C, or D. You plan to create a computer database consisting of these data, and you decide to code the data as A = 1, B = 2, C = 3, and D = 4. Are the data consisting of the classifications A, B, C and D qualitative or Quantitative? After the data are input as 1, 2, 3, or 4, are they qualitative or Quantitative? 10. In one university, language professors incorporated a 10-week extensive program to improve students’ Japanese reading comprehension. The professors collected 262 books originally written for Japanese children and required their students to read at least 40 of them as part of the grade in the course. The books were categorized into reading levels (color-coded for easy selection) according to length and complexity. Complete parts a through c. 11. Use the relative frequency table shown to the right to calculate the number of the 400 measurements failing into each of the measurements classes. Then graph a frequency histogram for these data. 12. Five banks have been ranked by the amount charged to credit and debit cards issued by the banks. The table to the right gives the total amount charged in 2007 for the top ranked banks. 13. Compare the z-scores to decide which of the x values below lie the greatest above the mean and the greatest distance below the mean. 14. Consider the horizandal box plot shown to the right. 15. Educators are constantly eveluating the efficacy of public schools in the education and training of tudents. One quantitative assessment of change over time is the difference in scores on the SAT. The table below contains the average SAT scores for 10 states for the years 1988 and 2005. 16. The mean gas mileage for a hybrid car is 56 miles per gallon. Suppose that the gasoline mileage is approximately normally distributed with a standard deviation of 3.2 miles per gallon. =============================================== QNT 561 Week 2 Lab Work (New) For more course tutorials visit www.newtonhelp.com Chapter 5: Ex 4) A large company must hire a new president. The Board of Directors prepares a list of five candidates, all of whom are equally qualified. Two of these candidates are members of a minority group. To avoid bias in the selection of the candidate, the company decides to select the president by lottery. a. What is the probability one of the minority candidate is hired? b. Which concept of probability did you use to make this estimate? Ex 14) The chair of the board of directors says, “ There is a 50% chance this company will earn a profit, a 30% chance it will break even, and a 20% chance it will lose money next quarter”: a. Use an addition rule to find the probability the company will not lose money next quarter b. Use the complement rule to find the probability it will not lose money next quarter. EX 22 ) A National Park Service survey of visitors to the Rocky Mountain region revealed that 50% visit Yellowstone park, 40% visit the Tetons, and 35% visit both. a) What is the probability a vacationer will visit at least one of these attractions? b) What is the probability .35 called? c) Are the events mutually exclusive? Ex 40) Value : 10.00 Points Solve the following: a) 20!/17! b) 9P3 c) 7C2 Ex 34) Use Bayes’ theorem to determine P(A3| B1) Chapter 6: Ex 4) Which of these variables are discrete and which are continuous random variables? a. The number of new accounts established by a salesperson b. The time between customer arrivals to a bank ATM c. The number of customers in Big Nick’s barber shop d. The amount of fuel in your car’s gas tank EX. 14) The U.S postal service reports 95% of first-class mail within the same city is delivered within 2 days of the time of mailing. Six letters are randomly sent to different locations. a) What is the probability that all six arrive within 2 days? b) What is the probability that will arrive within 2 days. c) Compute the variance of the number that will arrive within 2 days. d) Compute the standard deviation of the number that will arrive within 2 days. Ex 20) Binomial Distribution EX. 26) A Population consists of 15 items, 10 of which are acceptable. In a sample of four items, what is the probability that exactly three are acceptable? Assume the samples are drawn without replacement. Chapter 7: Ex 4) According to the insurance institute of America, a family of four spends between $400 and $3,800 per year on all type of insurance. Suppose the money spent is uniformly distributed between these amounts. a. What is the mean amount spent on insurance? b. What is S.D of the amount spent? c. If we select a family at random, What is the probability they spend less than $2,000 per year on insurance per year? d. What is the probability a family spends more than $3,000 per year? EX.10)The mean of a normal probability distribution is 60; the standard deviation is 5. a) About what percent of the observations lie between 55 and 65? b) About what percent of the observations lie between 50 and 70? c) About what percent of the observations lie between 45 and 75? Ex 14) A normal population has a mean of 12.2 and a standard deviation of 2.5 a. Complete the z value associated with 14.3 b. What proportion of the population is between 12.2 and 14.3? c. What proportion of the population less than 10? Ex 18) A normal population has a mean of 80.0 and a standard deviation of 14.0 a. Compute the probability of a value between 75.0 and 9.0 b. Compute the probability of a value of 75.0 or less c. Compute the probability of a value between 55.0 and 70.0 EX. 28) For the most recent year available, the mean annual cost to attend a private university in the united States was $26,889. Assume the distribution of annual costs follows the normal probability distribution and the standard deviation is $4,500. Ninety-five percent of all students at private universities pay less than what amount? =============================================== QNT 561 Week 2 Team Assignment Business Research Project Part 1 Business Problem and Research Questions For more course tutorials visit www.newtonhelp.com Identify an organization or business for your Learning Team research project. Describe the products or services it provides. Identify a problem or dilemma faced by the organization that could be addressed by research. Discuss the problem as a team. Discuss your selected problem or dilemma with your faculty member to ensure that it is at an appropriate scope for the course. Develop a purpose statement for your research project. Create a draft of the research questions addressing the problem and purpose statements. Format your paper consistent with APA guidelines. Click the Assignment Files tab to submit your assignment. =============================================== QNT 561 Week 2 Team Assignment Business Research Project Part 1 Formulation of the Research Problem For more course tutorials visit www.newtonhelp.com Identify an organization from any member in your Learning Team or an organization with which your team is familiar. If an actual company is used, disguise its name with a pseudonym. Identify one independent variable and one dependent variable based on the business. Operationalize these variables if they are too abstract to measure. Develop a real or realistic research question for the company you chose and the two variables. Include a background, a business problem and the team's role of no more than 500 words. Develop a research question from the two variables. Keep you research question simple, easy to understand and able to be quantified with research data. Use the Research Question Two Variable Handout for guidance. Create hypothesis statements based on the research question. Format your paper consistent with APA guidelines. Click the Assignment Files tab to submit your assignment. =============================================== QNT 561 Week 3 Assignment Expansion Strategy and Establishing a Re-Order Point For more course tutorials visit www.newtonhelp.com Learning team paper Purpose of Assignment This assignment has two cases. The first case is on expansion strategy. Managers constantly have to make decisions under uncertainty. This assignment gives students an opportunity to use the mean and standard deviation of probability distributions to make a decision on expansion strategy. The second case is on determining at which point a manager should re-order a printer so he or she doesn't run out-of-stock. The second case uses normal distribution. The first case demonstrates application of statistics in finance and the second case demonstrates application of statistics in operations management. Assignment Steps Resources: Microsoft Excel®, Bell Computer Company Forecasts data set, Case Study Scenarios Write a 1,050-word report based on the Bell Computer Company Forecasts data set and Case Study Scenarios. Include answers to the following: Case 1: Bell Computer Company Compute the expected value for the profit associated with the two expansion alternatives. Which decision is preferred for the objective of maximizing the expected profit? Compute the variation for the profit associated with the two expansion alternatives. Which decision is preferred for the objective of minimizing the risk or uncertainty? Case 2: Kyle Bits and Bytes What should be the re-order point? How many HP laser printers should he have in stock when he re-orders from the manufacturer? Format your assignment consistent with APA format. =============================================== QNT 561 Week 3 Case Study SuperFun Toys (2 Papers) For more course tutorials visit www.newtonhelp.com Individual Paper: Purpose of Assignment The purpose of this assignment is for students to learn how to make managerial decisions using a case study on Normal Distribution. This case uses concepts from Weeks 1 and 2. It provides students an opportunity to perform sensitivity analysis and make a decision while providing their own rationale. This assignment also shows students that statistics is rarely used by itself. It shows tight integration of statistics with product management. Assignment Steps Develop a 1,050-word case study analysis including the following: • Use the sales forecaster’s prediction to describe a normal probability distribution that can be used to approximate the demand distribution. • Sketch the distribution and show its mean and standard deviation. Hint: To find the standard deviation, think Empirical Rule covered in Week 1. • Compute the probability of a stock-out for the order quantities suggested by members of the management team (i.e. 15,000; 18,000; 24,000; 28,000). • Compute the projected profit for the order quantities suggested by the management team under three scenarios: pessimistic in which sales are 10,000 units, most likely case in which sales are 20,000 units, and optimistic in which sales are 30,000 units. One of SuperFun’s managers felt the profit potential was so great the order quantity should have a 70% chance of meeting demand and only a 30% chance of any stock- outs. What quantity would be ordered under this policy, and what is the projected profit under the three sales scenarios? SuperFun Toys, Inc., sells a variety of new and innovative children’s toys. Management learned the pre-holiday season is the best time to introduce a new toy because many families use this time to look for new ideas for December holiday gifts. When SuperFun discovers a new toy with good market potential, it chooses an October market entry date. To get toys in its stores by October, SuperFun places one-time orders with its manufacturers in June or July of each year. Demand for children’s toys can be highly volatile. If a new toy catches on, a sense of shortage in the marketplace often increases the demand to high levels and large profits can be realized. However, new toys can also flop, leaving SuperFun stuck with high levels of inventory that must be sold at reduced prices. The most important question the company faces is deciding how many units of a new toy should be purchased to meet anticipated sales demand. If too few are purchased, sales will be lost; if too many are purchased, profits will be reduced because of low prices realized in clearance sales. This is where SuperFun feels that you, as an MBA student, can bring value. For the coming season, SuperFun plans to introduce a new product called Weather Teddy. This variation of a talking teddy bear is made by a company in Taiwan. When a child presses Teddy’s hand, the bear begins to talk. A built-in barometer selects one of five responses predicting the weather conditions. The responses range from “It looks to be a very nice day! Have fun” to “I think it may rain today. Don’t forget your umbrella.” Tests with the product show even though it is not a perfect weather predictor, its predictions are surprisingly good. Several of SuperFun’s managers claimed Teddy gave predictions of the weather that were as good as many local television weather forecasters. As with other products, SuperFun faces the decision of how many Weather Teddy units to order for the coming holiday season. Members of the management team suggested order quantities of 15,000, 18,000, 24,000, or 28,000 units. The wide range of order quantities suggested indicates considerable disagreement concerning the market potential. Having a sound background in statistics and business, you are required to perform statistical analysis and the profit projections which is typically done by the product management group. You want to provide management with an analysis of the stock-out probabilities for various order quantities, an estimate of the profit potential, and to help make an order quantity recommendation. SuperFun expects to sell Weather Teddy for $24 based on a cost of $16 per unit. If inventory remains after the holiday season, SuperFun will sell all surplus inventories for $5 per unit. After reviewing the sales history of similar products, SuperFun’s senior sales forecaster predicted an expected demand of 20,000 units with a 95% probability that demand would be between 10,000 units and 30,000 units. One of SuperFun's managers felt the profit potential was so great the order quantity should have a 70% chance of meeting demand and only a 30% chance of any stock- outs. What quantity would be ordered under this policy, and what is the projected profit under the three sales scenarios? =============================================== QNT 561 Week 3 DQ 1 For more course tutorials visit www.newtonhelp.com In your organization’s management development program, there was a heated discussion between people who claimed that theory is impractical and not effective, and others who claimed that effective theory is the most practical approach to problems. What position would you take and why? =============================================== QNT 561 Week 3 DQ 2 For more course tutorials visit www.newtonhelp.com You observe female sales representatives having lower customer defections than male sales representatives. What concepts and constructs would you use to study this phenomenon? How might the concepts or constructs relate to explanatory hypotheses? Explain. =============================================== QNT 561 Week 3 Individual Mystatslab Problem Set For more course tutorials visit www.newtonhelp.com 1. Which hypothesis, the null or the alternative, is the status-quo hypothesis? 2. A university economist conducted a study of elementary school lunch menus. During the state-mandated testing period, school lunches averaged 890 calories. The economist claimed that after the testing period ended, the average caloric content of the school lunches increased/dropped significantly. Set up the null and alternative hypothesis to test the economist’s claim. 3. Suppose the mean GPA of all students graduating from a particular university in 1975 was 2.40. The register plans to look at records of graduating last year to see if the mean GPA has decreased. Define notation and state the null and alternative hypothesis for this investigation. 4. A random sample of 100 observations from a population with standard deviation 58 yielded a sample mean of 111. Complete parts a through c. 5. A final scores of games of a certain sport were compared against the final point spreads established by oddsmakers. The difference between the game outcome and point spread (called a point-spread error) was calculated for 250 games. The mean and standard deviation of the point-spread errors are = 1.7 and s = 13.1. Use this information to test the hypothesis that the true mean point-spread error for all games differs from 0. Conduct the test at α = 0.10 and interpret the result. 6. If a hypothesis test were conducted using α = 0.025, for which of the following p-values would the null hypothesis be rejected? 7. For the α and observed significance level (p-value) pair, indicate whether the null hypothesis would be rejected. α = 0.10, p-value = 0.001 8. In a test of the hypothesis H0:µ = 40 versus Ha: µ ≠ 40, a sample of n = 50 observations possessed mean = 40.7 and standard deviation s = 3.8. Find the p-value for this test. 9. In a study it was found that the averge age of cable TV shoppers was 55 years. Suppose you want to test the null hypothesis, H0:µ = 55, using a sample of n = 60 cable TV shoppers. 10. A sample of seven mesurements, randomly selected from a normally distributed population, resulted in the summary statistics = 4.6 and s = 1.2. Complete parts athrough c. 11. A study analysis recent incidents involving terrorist attacks. Data on the number of individual suicide bombings that occurred in each of 20 sampled terrorist group attcks against a country is reproduced in the data table below. An Excel/DDXL printout is shown to the right. Complete parts a through e. 12. When planning for a new forest road to be used for tree harvesting, planners must select the location to minimize tractor skidding distance. The skidding distances (in meters) were measured at 20 randomly selected road sites. The data are given below. A logger working on the road claims the mean skidding distance is atleast 424 meters. Is there sufficient evidence to refute this claim? Use α = 0.10 / α = 0.01. 13. For the binomial sample sizes and null hypothesized values of p in each part, determine whether the sample size is large enough to meet the required conditions for using the normal approximation to conduct a valid large-sample hypothesis test of the null hypothesis H0: p = p0. Complete parts a through e. 14. Suppose a consumer group rated 49 brands of toothpaste based on whether or not the brand carries an American Dental Association (ADA) seal verifying effective decay prevention. The results of a hypothesis test for the proportion of brands with the seal are shown to the right. Complete parts a through c. 15. In order to compare the means of two populations, independent random samples of 410 observations are selected from each population, with the results found in the table to the right. Complete parts a through e. 16. To use the t-statistic to test for a difference between the means of two populations, what assumptions must be made about the two populations? About the two samples? 17. 18. Independent random samples are selected from two populations and are used to test the hypothesis H0: (µ1 - µ2) = 0 against the alternative Ha: (µ1 - µ2) ≠ 0. An analysis of 234 observations from population 1 and 310 from population 2 yielded a p-value of 0.113. Complete parts a and b below. 19. A study was done to examine whether the perception of service quality at hotels differd by gender. Hotel guests were randomly selected to rate service items on a 5-point scale. The sum of the items for each guest was determined and a summary of the guest scores are provided in the table. Complete parts a and b. 20. To determine if winning a certain award leads to a challenge in life expectancy, researches sampled 748 award winners and matched each one with another person of the same sex who was in the same profession and was born in the same era. The lifespan of each pair was compared. Complete parts a through c below. 21. A new testing method was developed to reduce a certain ratio. The data in the table show the ratios that resulted from testing six components using the standard method and the new method. Compare the two methods with a 90% confidence interval. Which method has the smaller mean ratio? 22. Consider making an interference about p1 – p2 , where there are x1 successes in n1 binomial trails and x2 succeseses in n2 binomial trails. 23. Construct a 90% confidence interval for (p1 – p2) in each of the following situations. 24. In auction bidding the “winner’s curse” is the phenomenon of the winning (or highest) bid price being above the expected value of the item being auctioned. A study was conducted to see if less-experienced bidders were more likely to be impacted by the curse than super-experienced biders. The study showed that of the 180 bids by super-experienced bidders, 26 winning bids were above the item’s expected value, and of the bids by the 140 less-experienced bidders, 31 winning bids were above the item’s expected value. Complete parts athrough d. 25. School buying is a form of aggressive behavior that occurs when a student is exposed repeatedly to negative actions from another student. In order to study the effectivenss of an antibullying policy at elementary schools, a survey of over 2,000 elementary school children was conducted. Each student was asked if he or she ever bullied another student. In a sample of 1358 boys, 745 Claimed th

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video annotation 24x7offshoring

Skip to content Welcome to 24x7Offshoring 130/L, Sudarshan Cinema Rd, Gautam Nagar, Yusuf Sarai, New Delhi, Delhi 110049 info@24x7offshoring.com Facebook Twitter Youtube 24x7offshoring.com Home Services Blog Clients About Contact GET A QUOTE Image and Video Annotation | Best in 2022 adminOffshoring October 30, 2021 No Comments Here are Important things about Image and Video Annotation that you should know for machine learning and to make your annotation project well & good your vision our thoughts. Important About Image and Video Annotation That You Should Know video annotation 24x7offshoring video annotation 24x7offshoring What Is Image and video Annotation And How Does It Work? The technique of labeling or tagging video clips to train Computer Vision models to recognize or identify objects is known as video annotation. By labeling things frame-by-frame and making them identifiable to Machine Learning models, Image and video Annotation aids in the extraction of intelligence from movies. Accurate video annotation comes with several difficulties. Accurate video annotation comes with several difficulties. Because the item of interest is moving, precisely categorizing things to obtain exact results is more challenging. Essentially, video and image annotation is the process of adding information to unlabeled films and pictures so that machine learning algorithms may be developed and trained. This is critical for the advancement of artificial intelligence. Labels or tags refer to the metadata attached to photos and movies. This may be done in a variety of methods, such as annotating pixels with semantic meaning. This aids in the preparation of algorithms for various tasks such as tracking objects via video segments and frames. This can only be done if your movies are properly labeled, frame by frame. This dataset can have a significant impact on and enhance a range of technologies used in a variety of businesses and occupations, such as automated manufacturing. Global Technology Solutions has the ability, knowledge, resources, and capacity to provide you with all of the video and image annotation you require. Our annotations are of the highest quality, and they are tailored to your specific needs and problems. We have people on our team that have the expertise, abilities, and qualifications to collect and give annotation for any circumstance, technology, or application. Our numerous quality checking processes constantly ensure that we offer the best quality annotation. more like this, just click on: https://24x7offshoring.com/blog/ What Kinds Of Image and video Annotation Services Are There? Bounding box annotation, polygon annotation, key point annotation, and semantic segmentation are some of the video annotation services offered by GTS to meet the demands of a clientu2019s project. As you iterate, the GTS team works with the client to calibrate the jobu2019s quality and throughput and give the optimal cost-quality ratio. Before releasing complete batches, we recommend running a trial batch to clarify instructions, edge situations, and approximate work timeframes. video annotation 24x7offshoring video annotation 24x7offshoring Image and Video Annotation Services From GTS Boxes For Bounding In Computer Vision, it is the most popular sort of video and image annotation. Rectangular box annotation is used by GTS Computer Vision professionals to represent things and train data, allowing algorithms to detect and locate items during machine learning processes. Annotation of Polygon Expert annotators place points on the target objectu2019s vertices. Polygon annotation allows you to mark all of an objectu2019s precise edges, independent of form. Segmentation By Keywords The GTS team segments videos into component components and then annotates them. At the frame-by-frame level, GTS Computer Vision professionals discover desirable things inside the movie of video and image annotation. Annotation Of Key points By linking individual points across things, GTS teams outline items and create variants. This sort of annotation recognizes bodily aspects, such as facial expressions and emotions. video annotation 24x7offshoring video annotation 24x7offshoring What is the best way to Image and Video Annotation? A person annotates the image by applying a sequence of labels by attaching bounding boxes to the appropriate items, as seen in the example image below. Pedestrians are designated in blue, taxis are marked in yellow, and trucks are marked in yellow in this example. The procedure is then repeated, with the number of labels on each image varying based on the business use case and project in video and image annotation. Some projects will simply require one label to convey the full imageu2019s content (e.g., image classification). Other projects may necessitate the tagging of many items inside a single photograph, each with its label (e.g., bounding boxes). What sorts of Image and Video Annotation are there? Data scientists and machine learning engineers can choose from a range of annotation types when creating a new labeled dataset. Letu2019s examine and contrast the three most frequent computer vision annotation types: 1) categorizing Object identification and picture segmentation are the next steps. The purpose of whole-image classification is to easily determine which items and other attributes are present in a photograph. With picture object detection, you may go one step further and determine the location of specific items (bounding boxes). The purpose of picture segmentation is to recognize and comprehend whatu2019s in the image down to the pixel level in video and image annotation. video annotation 24x7offshoring video annotation 24x7offshoring Unlike object detection, where the bounding boxes of objects might overlap, every pixel in a picture belongs to at least one class. It is by far the easiest and fastest to annotate out of all of the other standard alternatives. For abstract information like scene identification and time of day, whole-image classification is a useful solution. In contrast, bounding boxes are the industry standard for most object identification applications and need a greater level of granularity than whole-image categorization. Bounding boxes strike a compromise between speedy video and image annotation and focusing on specific objects of interest. Picture segmentation was selected for specificity to enable use scenarios in a model where you need to know absolutely whether or not an image contains the item of interest, as well as what isnu2019t an object of interest. This contrasts with other sorts of annotations, such as categorization or bounding boxes, which are faster but less precise. Identifying and training annotators to execute annotation tasks is the first step in every image annotation effort. Because each firm will have distinct needs, annotators must be extensively taught the specifications and guidelines of each video and image annotation project. How do you annotate a video? image and video annotation Video annotation, like picture annotation, is a method of teaching computers to recognize objects. Both annotation approaches are part of the Computer Vision (CV) branch of Artificial Intelligence (AI), which aims to teach computers to replicate the perceptual features of the human eye. A mix of human annotators and automated tools mark target items in video footage in a video annotation project. The tagged film is subsequently processed by an AI-powered computer to learn how to recognize target items in fresh, unlabeled movies using machine learning (ML) techniques. The AI model will perform better if the video labels are correct. With automated technologies, precise video annotation allows businesses to deploy with confidence and grow swiftly. Video and picture annotation has a lot of similarities. We discussed the typical image annotation techniques in our image annotation article, and many of them are applicable for applying labels to video. However, there are significant variations between the two methods that may assist businesses in determining which form of data to work with when they choose. The data structure of the video is more sophisticated than that of a picture. Video, on the other hand, provides more information per unit of data. Teams may use it to determine an objectu2019s location and whether it is moving, and in which direction. As previously said, annotating video datasets is quite similar to preparing image datasets for computer vision applicationsu2019 deep learning models. However, videos are handled as frame-by-frame picture data, which is the main distinction. For example, A 60-second video clip with a 30 fps (frames per second) frame rate has 1800 video frames, which may be represented as 1800 static pictures. Annotating a 60-second video clip, for example, might take a long time. Imagine doing this with a dataset containing over 100 hours of video. This is why most ML and DL development teams choose to annotate a single frame and then repeat the process after many structures have passed. Many people look for particular clues, such as dramatic shifts in the current video sequenceu2019s foreground and background scenery. They use this to highlight the most essential elements of the document; if frame 1 of a 60-second movie at 30 frames per second displays car brand X and model Y. Several image annotation techniques may be employed to label the region of interest to categorize the automotive brand and model. Annotation methods for 2D and 3D images are included. However, if annotating background objects is essential for your specific use case, such as semantic segmentation goals, the visual sceneries, and things in the same frame are also tagged. video annotation 24x7offshoring video annotation 24x7offshoring Types of image annotations Image annotation is often used for image classification, object detection, object recognition, image classification, machine reading, and computer vision models. It is a method used to create reliable data sets for the models to be trained and thus useful for supervised and slightly monitored machine learning models. For more information on the differences between supervised and supervised machine learning models, we recommend Introduction to Internal Mode Learning Models and Guided Reading: What It Is, Examples, and Computer Visual Techniques. In those articles, we discuss their differences and why some models need data sets with annotations while others do not. Annotation objectives (image classification, object acquisition, etc.) require different annotation techniques in order to develop effective data sets. 1. Classification of Images Photo segmentation is a type of machine learning model that requires images to have a single label to identify the whole image. The annotation process for image classification models aims to detect the presence of similar objects in databases. It is used to train the AI model to identify an object in an unmarked image that looks similar to the image classes with annotations used to train the model. Photography training is also called tagging. Therefore, classification of images aims to automatically identify the presence of an object and to indicate its predefined category. An example of a photo-sharing model is where different animals are u201cfoundu201d among the included images. In this example, an annotation will be provided for a set of pictures of different animals and we will be asked to classify each image by label based on a specific type of animal. Animal species, in this case, will be the category, and the image is the inclusion. Providing images with annotations as data in a computer vision model trains a model of a unique visual feature of each animal species. That way, the model will be able to separate images of new animals that are not defined into appropriate species. video annotation 24x7offshoring video annotation 24x7offshoring 2. Object Discovery and Object Recognition Object detection or recognition models take a step-by-step separation of the image to determine the presence, location, and number of objects in the image. In this type of model, the process of image annotation requires parameters to be drawn next to everything found in each image, which allows us to determine the location and number of objects present in the image. Therefore, the main difference is that the categories are found within the image rather than the whole image is defined as a single category (Image Separation). Class space is a parameter above a section, and in image classification, class space between images is not important because the whole image is identified as one category. Items can be defined within an image using labels such as binding boxes or polygons. One of the most common examples of object discovery is human discovery. It requires a computer device to analyze frames continuously in order to identify features of an object and to identify existing objects as human beings. Object discovery can also be used to detect any confusion by tracking changes in features over a period of time. 3. Image Separation Image subdivision is a type of image annotation that involves the division of an image into several segments. Image classification is used to find objects and borders (lines, curves, etc.) in images. Made at pixel level, each pixel is assigned within the image to an object or class. It is used for projects that require high precision in classifying inputs. The image classification is further divided into the following three categories: Semantic semantics shows boundaries between similar objects. This method is used when greater precision regarding the presence, location, and size or shape of objects within an image is required. Separate model indicates the presence, location, number, size or shape of objects within the image. Therefore, segmentation helps to label the presence of a single object within an image. Panoptic classification includes both semantic and model separation. Ideally, panoptic separation provides data with background label (semantic segmentation) and object (sample segmentation) within an image. 4. Boundary Recognition This type of image annotation identifies the lines or borders of objects within an image. Borders may cover the edges of an object or the topography regions present in the image. Once the image is well defined, it can be used to identify the same patterns in unspecified images. Border recognition plays an important role in the safe operation of self-driving vehicles. Annotations Conditions In an image description, different annotations are used to describe the image based on the selected program. In addition to shapes, annotation techniques such as lines, splines, and location marking can also be used for image annotation. The following are popular image anchor methods that are used based on the context of the application. 1. Tie Boxes The binding box is an annotation form widely used in computer recognition. Rectangular box binding boxes are used to define the location of an object within an image. They can be two-dimensional (2D) or three-dimensional (3D). 2. Polygons Polygons are used to describe abnormal objects within an image. These are used to mark the vertices of the target object and to define its edges. 3. Marking the place This is used to identify important points of interest within an image. Such points are called landmarks or key points. Location marking is important for facial recognition. 4. Lines and Splines Lines and splines define the image with straight or curved lines. This is important in identifying the boundary to define side roads, road mark How To Get Started With Image and Video Annotation? Annotation is a function of interpreting an image with data labels. Annotation work usually involves manual labor with the help of a computer. Picture annotation tools such as the popular Computer Vision Annotation CVAT tool help provide information about the image that can be used to train computer vision models. If you need a professional image annotation solution that provides business capabilities and automated infrastructure, check out Viso Suite. End-to-End Computer Vision Fields include not only an image annotation, but also an uphill and downstream related activities. That includes data collection, model management, application development, DevOps, and Edge AI capabilities. Contact here. Types of video annotations Depending on the application, there are various ways in which video data can be translated. They include: 2D & 3D Cuboid Annotations: These annotations form a 2D or 3D cube in a specified location, allowing accurate annotations for photos and video frames. Polygon Lines: This type of video annotation is used to describe objects in pixels u2013 and only includes those for a specific object. Tie Boxes: These annotations are used in photographs and videos, as the boxes are marked at the edges of each object. Semantic paragraphs and annotations: Made at pixel level, semantic annotations are the precise segment in which each pixel in an image or video frame is assigned to a class. Trademark annotations: Used most effectively in facial recognition, local symbols select specific parts of the image or video to be followed. Tracking key points: A strategy that predicts and tracks the location of a person or object. This is done by looking at the combination of the shape of the person / object. Object detection, tracking and identification: This annotation gives you the ability to see an item on the line and determine the location of the item: feature / non-component (quality control on food packages, for example). video annotation 24x7offshoring video annotation 24x7offshoring In the Real World: Examples of Video Annotations and Terms of Use Transportation: Apart from self-driving cars, the video annotation is used in computer vision systems in all aspects of the transportation industry. From identifying traffic situations to creating smart public transport systems, the video annotation provides information that identifies cars and other objects on the road and how they all work together. Production: Within production, the video annotation assists computer-assisted models with quality control functions. AI can detect errors in the production line, resulting in surprisingly cost savings compared to manual tests. A computer scanner can also perform a quick measure of safety, check that people are wearing the right safety equipment, and help identify the wrong equipment before it becomes a safety hazard. Sports Industry: The success of any sports team goes beyond winning and losing u2013 the secret to knowing why. Teams and clubs throughout the game use computer simulations to provide next level statistics by analyzing past performance to predict future results. And the video annotation helps to train these models of computer ideas by identifying individual features in the video u2013 from the ball to each player on the field. Other sports applications include the use of sports broadcasters, companies that analyze crowd engagement and improve the safety of high-speed sports such as NASCAR racing. Security: The primary use of computer vision in security revolves around face recognition. When used carefully, facial expressions can help in opening up the world, from opening a smartphone to authorizing financial transactions. How you describe the video While there are many tools out there that organizations can use to translate video, this is hard to measure. Using the power of the crowd through crowdsourcing is an effective way to get a large number of annotations needed to train a computer vision model, especially when defining a video with a large amount of internal data. In crowdsourcing, annotations activities are divided into thousands of sub-tasks, completed by thousands of contributors. The crowd video clip works in the same way as other resource-rich data collections. Eligible members of the crowd are selected and invited to complete tasks during the collection process. The client identifies the type of video annotation required in the list above and the members of the crowd are given task instructions, completing tasks until a sufficient amount of data has been collected. Annotations are then tested for quality. Defined Crowd Quality At Defined Crowd, we apply a series of metrics at activity level and crowd level and ensure quality data collection. With quality standards such as standard gold data sets, trust agreements, personal procedures and competency testing, we ensure that each crowd provider is highly qualified to complete the task, and that each task produces a quality video annotation. required results. The Future of Computer Vision Computer visibility makes your product across the industry in new and unexpected ways. There will probably be a future when we begin to rely on computer vision at different times throughout our days. To get there, however, we must first train equipment to see the world through the human eye. What is the meaning of annotation in YouTube? image and video annotation Weu2019re looking at YouTubeu2019s Annotation feature in-depth as part of our ongoing YouTube Brand Glossary Series (see last weeku2019s piece on u201cYouTube End Cardsu201d). YouTube annotations are a great way to add more value to a video. When implemented correctly, clickable links integrated into YouTube video content may enhance engagement, raise video views, and offer a continuous lead funnel. Annotations enable users to watch each YouTube video longer and/or generate traffic to external landing pages by incorporating more information into videos and providing an interactive experience. Annotations on YouTube are frequently used to boost viewer engagement by encouraging them to watch similar videos, offering extra information to investigate, and/or include links to the sponsored brandu2019s website. Merchandising or other sponsored material that consumers may find appealing. YouTube Annotations are a useful opportunity for marketers collaborating with YouTube Influencers to communicate the brand message and/or include a short call-to-action (CTA) within sponsored videos. In addition, annotations are very useful for incorporating CTAs into YouTube videos. YouTube content makers may improve the possibility that viewers will u201cExplore More,u201d u201cBuy This Product,u201d u201cSee Related Videos,u201d or u201cSubscribeu201d by providing an eye-catching commentary at the correct time. In addition, a well-positioned annotation may generate quality leads and ensure improved brand exposure for businesses. What is automatic video annotation? This is a procedure that employs machine learning and deep learning models that have been trained on datasets for this computer vision application. Sequences of video clips submitted to a pre-trained model are automatically classified into one of many categories. A video labeling model-powered camera security system, for example, may be used to identify people and objects, recognize faces, and categorize human movements or activities, among other things. Automatic video labeling is comparable to image labeling techniques that use machine learning and deep learning. Video labeling applications, on the other hand, process sequential 3D visual input in real-time. Some data scientists and AI development teams, on the other hand, process each frame of a real-time video feed. Using an image classification model, label each video sequence (group of structures). This is because the design of these automatic video labeling models is similar to that of image classification tools and other computer vision applications that employ artificial neural networks. Similar techniques are also engaged in the supervised, unsupervised, and reinforced learning modes in which these models are trained. Although this method frequently works successfully, considerable visual information from video footage is lost during the pre-processing stage in some circumstances. The technique of labeling or tagging video clips to train Computer Vision models to recognize or identify objects is known as video annotation. By labeling things frame-by-frame and making them identifiable to Machine Learning models, Image and video Annotation aids in the extraction of intelligence from movies. Accurate video annotation comes with several difficulties. Image Annotation Tools Weu2019ve all heard of Image annotation Tools.image annotation tools Any supervised deep learning project, including computer vision, uses it. Annotations are required for each image supplied into the model training method in popular computer vision tasks such as image classification, object recognition, and segmentation. The data annotation process, as important as it is, is also one of the most time-consuming and, without a question, the least appealing components of a project. As a result, selecting the appropriate tool for your project can have a considerable Image annotation Tools impact on both the quality of the data you produce and the time it takes to finish it. With that in mind, itu2019s reasonable to state that every part of the data annotation process, including tool selection, should be approached with caution. We investigated and evaluated five annotation tools, outlining the benefits and drawbacks of each. Hopefully, this has shed some light on your decision-making process. You simply must invest in a competent picture annotation tool. Throughout this post, weu2019ll look at a handful of my favorite deep learning tools that Iu2019ve used in my career as a deep learning Image Annotation Tools. Data Annotation Tools Some data annotation tools will not work well with your AI or machine learning project. When evaluating tool providers, keep these six crucial aspects in mind. Do you need assistance narrowing down the vast, ever-changing market for data annotation tools? We built an essential reference to annotation tools after a decade of using and analyzing solutions to assist you to pick the perfect tool for your data, workforce, QA, and deployment needs. In the field of machine learning, data annotation tools are vital. It is a critical component of any AI modelu2019s performance since an image recognition AI can only recognize a face in a photo if there are numerous photographs previously labeled as u201cface.u201d Annotating data is mostly used to label data. Furthermore, the act of categorizing data frequently results in cleaner data and the discovery of new opportunities. Sometimes, after training a model on data, youu2019ll find that the naming convention wasnu2019t enough to produce the type of data annotation tools predictions or machine learning model you wanted. Video Annotation vs. Picture Annotation There are many similarities between video annotation and image. In our article an annotation title, we have included some common annotation techniques, many of which are important when using labels on video. There are significant differences between these two processes, however, which help companies determine which type of data they will use when selecting one or the other. Data Video is a more complex data structure than an image. However, for information on each data unit, the video provides greater insight. Teams can use it to not only identify the location of an object, but also the location of the object and its orientation. For example, it is not clear in the picture when a person is in the process of sitting or standing. The video illustrates this. The video may also take advantage of information from previous frames to identify something that may be slightly affected. Image does not have this capability. By considering these factors, a video can produce more information per unit of data than an image. Annotation Process The video annotation has an extra layer of difficulty compared to the image annotation. Annotations should harmonize and trace elements of different situations between frames. To make this work, many teams have default components of the process. Computers today can track objects in all frames without the need for human intervention and all video segments can be defined by a small human task. The result is that the video annotation is usually a much faster process than the image annotation. Accuracy When teams use the default tools in the video description, they reduce the chance of errors by providing greater continuity for all frames. When defining a few images, it is important to use the same labels on the same objects, but consistent errors can occur. When a video annotation, the computer can automatically track the same object in all frames, and use context to remember that object throughout the video. This provides greater flexibility and accuracy than the image annotation, which leads to greater speculation for your AI model. Given the above factors, it often makes sense for companies to rely on video over images where selection is possible. Videos require less human activity and therefore less time to explain, are more accurate, and provide more data per unit. Application In fact, video and image annotations record metadata for videos and images without labels so that they can be used to develop and train machine learning algorithms, this is important for the development of practical skills. Metadata associated with images and videos can be called labels or tags, this can be done in a variety of ways such as defining semantic pixels. This helps to adjust the algorithms to perform various tasks such as tracking items in segments and video frames. This can only be done if your videos are well tagged, frame by frame, this database can have a huge impact and improve the various technologies used in various industries and life activities such as automated production. We at Global Technology Solutions have the ability, knowledge, resources, and power to provide you with everything you need when it comes to photo and video data descriptions. Our annotations are of the highest quality and are designed to meet your needs and solve your problems. We have team members with the knowledge, skills, and qualifications to find and provide an explanation for any situation, technology, or use. We always ensure that we deliver the highest quality of annotation through our many quality assurance systems Click to learn, u201cHow to integrate image and video annotation with text annotation for faster machine learning: Continue Reading, just click on: https://24x7offshoring.com/blog/ To visualize Image annotation with more briefly do have a watch at this YouTube video from Good Annotations. To visualize Video annotation more briefly do have a watch at this YouTube video from V7. Computer Vision: https://www.ibm.com/topics/computer-vision#:~:text=Computer vision is a field,recommendations based on that information. 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