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Chapter 1

Chapter 1. 1.1 An Overview of Statistics 1.2 Data Classification 1.3 Experimental Design. Introduction to Statistics. Section 1.1 and 1.2. An Overview of Statistics Classifying Data Critical Thinking. What is Statistics?. Statistics

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Chapter 1

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  1. Chapter 1 • 1.1 An Overview of Statistics • 1.2 Data Classification • 1.3 Experimental Design Introduction to Statistics Larson/Farber 4th ed.

  2. Section 1.1 and 1.2 An Overview of Statistics Classifying Data Critical Thinking Larson/Farber 4th ed.

  3. What is Statistics? Statistics The science of collecting, organizing, analyzing, and interpreting data in order to make decisions. Larson/Farber 4th ed.

  4. Definitions Population The collection of alloutcomes, responses, measurements, or counts that are of interest. Sample The collection of data from a subset of the population. Census The collection of data from every member of the population.

  5. Example: Identify the population, and whether a census or sample would be done. 1. HCC is doing a study on how many credit hours a HCC student is taking. 2. HCC is doing a study on many hours a week a HCC student is working. • A fashion magazine gathers data on the price of women’s jeans.

  6. What is Data? 1. Qualitative Data 2. Quantitative Data Data The responses, counts, measurements, or observations that have been collected. Data can be classified as one of 2 types: Larson/Farber 4th ed. 6

  7. Qualitative Data Major Place of birth Eye color Common statistic calculated: percentages Qualitative Data: Consists of non-numeric, categorical attributes or labels

  8. Quantitative Data Temperature Weight of a letter Age Common statistic calculated: averages Quantitative data: Numerical measurements or counts. Larson/Farber 4th ed. 8

  9. Quantitative Data:Discrete vs. Continuous Continuous data:infinite number of possible data values on a continuous scale ex: Weight of a baby Discrete data: finite number of possible data values: 0, 1, 2, 3, 4…. ex: Number of classes a student is taking Larson/Farber 4th ed. 9

  10. Parameters and Statistics Parameter A number that describes some characteristic of an entire population. Average age of all people in the United States Statistic A number that describes some characteristic from a sample. Average age of people from a sample of three states

  11. Ex: Parameters vs. Statistics Decide whether the numerical value describes a population parameter or a sample statistic. The average credit load of all HCC full-time students is 14.2 credit hours. From a sample of 300 HCC full-time students showed the average work hours a week is 18.3 hours. A gallup poll of 1012 adults nationwide showed 34% owned a handgun.

  12. Branches of Statistics Descriptive Statistics:Involves organizing, summarizing, and displaying data. Describes the important characteristics of the data. e.g. Tables, charts, averages, percentages Inferential Statistics:Involves using sample datato draw conclusions or make inferences about an entire population.

  13. Example: Descriptive and Inferential Statistics Decide which part of the study represents the descriptive branch of statistics. What conclusions might be drawn from the study using inferential statistics? A sample of Illinois adults showed that 22.7% of those with a high school diploma were obese, and 16.7% of college graduates were obese. (Source: Illinois BRFSS, 2004) Larson/Farber 4th ed.

  14. Example: Descriptive and Inferential Statistics A sample of 471 registered republicans showed that 22% would pick John McCain as the republican nominee for president. (Margin of error: 5%). (Source: USA Today/CNN poll) Decide which part of the study represents the descriptive branch of statistics. What conclusions might be drawn from the study using inferential statistics? Larson/Farber 4th ed. 16

  15. Uses of Statistics Almost all fields of study benefit from the application of statistical methods Statistics often lead to change

  16. Bad Samples Small Samples Misleading Graphs Pictographs Loaded Questions Correlation & Causality Self Interest Study Misuses of Statistics

  17. Misuse: Bad Samples • Samples must be unbiased and fairly represent the entire population. • If the data is not collected appropriately, the data may be completely useless. “Garbage in, garbage out” • Voluntary response sample : Respondents themselves decide whether to be included in the sample • Ex. Online surveys • Ex. Ratemyprofessor.com

  18. Misuse: Misleading Graphs

  19. CNN/USA Today Gallup poll on Terri Schiavo (March 2005)

  20. CNN/USA Today Gallup poll on Terri Schiavo (March 2005) Reprinted

  21. Misuse: Pictographs

  22. Misuse: Loaded Questions “Should the President have the line item veto to eliminate waste?” (97% said yes: ) “Should the President have the line item veto?” (57% said yes: )

  23. Misuse: Loaded Questions

  24. Misuse: Correlation does not imply Cause and Effect

  25. Misuses: Self Interest and Deliberate Distortions

  26. Section 1.3 Experimental Design Larson/Farber 4th ed.

  27. Designing a Statistical Study • What is it you want to study? • What is the population to gather data from? • *Collect data. If you use a sample, it must be representative of the population. • Descriptive Statistics – organize, present, summarize data • Inferential Statistics – draw conclusions about the population based on sample data Larson/Farber 4th ed.

  28. Things to Consider with Samples • The sample must be unbiased and fairly represent the entire population. • If the data is not collected appropriately, the data may be completely useless. “Garbage in, garbage out” • Want the maximum information at the minimum cost. What sample size is needed? Larson/Farber 4th ed. 32

  29. Methods of Collecting Data • Observational study • Survey • Experiment • Simulation Larson/Farber 4th ed. 33

  30. Methods of Collecting Data Observational study • A researcher observes or measures characteristics of interest of part of a population but does not change any existing conditions. Experiment • A treatment is applied to part of a population and responses are observed. Larson/Farber 4th ed.

  31. Methods of Collecting Data Survey • An investigation of one or more characteristics of a population, usually be asking people questions. • Commonly done by interview, mail, or telephone. Simulation • Uses a mathematical or physical model to reproduce the conditions of a situation or process. Often involves the use of computers. Larson/Farber 4th ed.

  32. Example: Methods of Data Collection Consider the following studies. Which method of data collection would you use to collect data for each study? A study of salaries of NFL players. A study of the emergency response times during a terrorist attack. A study of whether changing teaching techniques improves FCAT scores. A study of whether Tampa residents support a mass transit system.

  33. Sampling Techniques • Random versus Non-Random Samples • Convenience Samples • Simple Random Samples • Systematic Samples • Cluster Samples Larson/Farber 4th ed. 37

  34. Random and Non-Random Sampling Random Sampling Every member of the population has an equal change of being selected. Non-Random Sampling Some members of the population have no chance of being picked. Often leads to biased samples. Larson/Farber 4th ed. 38

  35. Convenience Samples • Data is collected that is readily available and easy to get. • Self-selected surveys or voluntary response surveys (online surveys, magazine surveys, 1-800-Verdict, Ratemyprofessor.com)

  36. x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Simple Random Sample • A random sample where every member of the population and every group of the same size has an equal chance of being selected. • Usually involves using a random number generator.

  37. Simple Random Sampling • Number each element of the population from 1 to N. • Use a random number generator (table, calculator, computer) to randomly selected a sample of size “n”. • TI-83/4: randint (1,N,n), or: • Table 1 in text. Pick a random start.

  38. Systematic Sampling Choose a starting value at random. Then choose every kth member of the population. • example: Select every 3rd patient who enters the ER.

  39. Stratified Sampling • Divide a population into at least 2 different subgroups (strata) that share the same characteristics (age, gender, ethnicity, income, etc) and select a random sample from each group. • Advantages: More information

  40. Cluster Sampling • Divide the population into many like subgroups (clusters); randomly select some of those clusters, and then select all of the members of those clusters to be in the sample. • Advantage: geographically separately populations

  41. Sources of Error in Sampling Non-Sampling Error * • sample data is incorrectly gathered, collected, or recorded. Selection Bias - bad sample Response Bias- bad data: incorrect responses, inaccurate measurements, etc.) Sampling Error the expected difference between a sample result and the true population result. (e.g. “Margin of error”).

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