slide1
Download
Skip this Video
Download Presentation
Sampling Analysis

Loading in 2 Seconds...

play fullscreen
1 / 27

Sampling Analysis - PowerPoint PPT Presentation


  • 110 Views
  • Uploaded on

Sampling Analysis . Sampling Analysis . Statisticians collect information about specific groups through surveys . . The entire group of objects or people that you want information about in a survey is called a population . .

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Sampling Analysis ' - umeko


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide2

Sampling Analysis

Statisticians collect information aboutspecific groups through surveys.

The entire group of objects or peoplethat you want information about in asurvey is called a population.

Sampling is a part of the populationthat we examine in order to gatherinformation about the entire population.

slide3

Sampling Analysis

If conclusions about a population are tobe valid, then the method used to choose the sample from the population must be sound.

Poor samples can lead to incorrectconclusions about a population

slide4

Random Sampling Method

A random sample is a sample that is chosen by chance. This gives all individuals in a population an equal chance to be selected.

Today most random sampling is done using computer software since the software can quickly select a specific number of names from a population.

slide5

Random Sampling Example

A random sample can be selected by putting the names of all the people in a population in a hat and then drawing a number of the names from the hat.

slide6

Stratified RandomSampling Method

A stratifiedrandom sample is a sample that first divides a population into groups of individuals that are similar in some way that is important to the survey.

Then it selects samples from each one of the groups and combines them to make up the actual sample.

slide7

Stratified Random Example

A college has 30,000 students, of whom 3,000 are graduate students.

Separate the students into graduate and undergraduate groups, and then select a sample of graduate students and a sample of undergraduate students.

These two samples make upthe stratified sample.

slide8

Systematic RandomSampling Method

A systematicrandom sample selects smaller groups within the populations in stages, resulting in a sample consisting of clusters of individuals.

For example, select a number n then survey every nth person.

slide9

Systematic Random Sample

You want to conduct a system at the local mall. You select the number 7. Then you survey every 7th personwho enters the mall.

This is an example ofsystematicrandom sample.

slide10

Biased Questions

When designing a survey, having a sample that represents the general population is very important. In addition, the questions in the survey must be worded carefully.

Biasedquestions make assumptions that may or may not be true. They also can make one answer choice seem better than another.

slide12

Objectives

Determine the experimental probability of an event.

Use experimental probability to make predictions.

slide13

An experiment is an activity involving chance. Each repetition or observation of an experiment is a trial, and each possible result is an outcome. The sample space of an experiment is the set of all possible outcomes.

slide14

Example 1: Identifying Sample Spaces and Outcomes

Identify the sample space and the outcome shown for each experiment.

A. Rolling a number cube

Sample space:{1, 2, 3, 4, 5, 6}

Outcome shown: 4

B. Spinning a spinner

Sample space:{red, green, orange, purple}

Outcome shown: green

slide15

Try 1

Identify the sample space and the outcome shown for the experiment: rolling a number cube.

Sample space: {1, 2, 3, 4, 5, 6}

Outcome shown: 3

slide16

An event is an outcome or set of outcomes in an experiment. Probability is the measure of how likely an event is to occur. Probabilities are written as fractions or decimals from 0 to 1, or as percents from 0% to 100%.

slide17

You can estimate the probability of an event by performing an experiment. The experimental probability of an event is the ratio of the number of times the event occurs to the number of trials. The more trials performed, the more accurate the estimate will be.

slide19

Example 3A: Finding Experimental Probability

An experiment consists of spinning a spinner. Use the results in the table to find the experimental probability of the event.

Spinner lands on orange

slide20

Example 3B: Finding Experimental Probability

An experiment consists of spinning a spinner. Use the results in the table to find the experimental probability of the event.

Spinner does not land on green

slide21

Try 3a

An experiment consists of spinning a spinner. Use the results in the table to find the experimental probability of each event.

Spinner lands on red

slide22

Try 3b

An experiment consists of spinning a spinner. Use the results in the table to find the experimental probability of each event.

Spinner does not land on red

slide23

You can use experimental probability to make predictions. A prediction is an estimate or guess about something that has not yet happened.

slide24

Example 4A: Quality Control Application

A manufacturer inspects 500 strollers and finds that 498 have no defects.

What is the experimental probability that a stroller chosen at random has no defects?

Find the experimental probability that a stroller has no defects.

= 99.6%

The experimental probability that a stroller has no defects is 99.6%.

slide25

Example 4B: Manufacturing Application

A manufacturer inspects 500 strollers and finds that 498 have no defects.

The manufacturer shipped 3500 strollers to a distribution center. Predict the number of strollers that are likely to have no defects.

Find 99.6% of 3500.

0.996(3500) = 3486

The prediction is that 3486 strollers will have no defects.

slide26

Try 4a

A manufacturer inspects 1500 electric toothbrush motors and finds 1497 have no defects.

What is the experimental probability that a motor chosen at random will have no defects?

Find the experimental probability that a motor has no defects.

= 99.8%

slide27

Try 4b

A manufacturer inspects 1500 electric toothbrush motors and finds 1497 have no defects.

There are 35,000 motors in a warehouse. Predict the number of motors that are likely to have no defects.

Find 99.8% of 35,000.

0.998(35000) = 34930

The prediction is that 34,930 motors will have no defects.

ad