Download presentation

Published byJuliette Wiswall Modified over 3 years ago

1
**Chi Squared Tests Hypothesis Tests for Linear Regression**

AP Statistics Topic 7 Chi Squared Tests Hypothesis Tests for Linear Regression

2
**These are the last 2 things we’ll study**

Chi-squared tests Goodness of Fit Independence and Homogeneity Hypothesis tests for linear regression The significance of the linear relationship We’ll spend one week on each

3
**Chi-Squared Tests Analysis of categorical data**

The tests we’ll study are Goodness of Fit test tests for homogeneity and independence These tests are performed exactly the same way For homogeneity, we look at two samples and one characteristic For independence, we look at one sample and two characteristics

4
Goodness of Fit Test Measures the extent to which some empirical distribution “fits” the distribution expected under the null hypothesis 20 30 40 50 60 Fork length 10 Frequency

5
**For example Time % 12-3AM 17 3-6AM 8 6-9AM 8 9AM-noon 6 Noon-3PM 10**

A GEICO Direct magazine had an interesting article concerning the percentage of teenage motor vehicle deaths and the time of day. The following percentages were given from a sample. Time % 12-3AM 17 3-6AM 8 6-9AM 8 9AM-noon 6 Noon-3PM 10 3-6 PM 16 6-9 PM 15 9PM-12AM 19

6
**The Distribution and Hypothesis Statements**

Is the percentage of teenage motor vehicle deaths the same for each time period? Conduct a hypothesis test at the 1% level. Ho: The percent of teenage motor vehicle deaths is the same for each time period. Ha: The percent of teenage motor vehicle deaths is not the same for each time period.

7
**Let’s look at this more closely**

In this problem, what type of data are we considering? Categorical data – that is, the time of day How many classes is our data divided into? 8 different classes

8
Put another way …. We want to see if the distribution of our data is consistent with the hypothesized distribution In this case, we want to see if the distribution of accidents is uniform – about 12.5% per period

9
**So how can we do this? We have our observed occurrences**

Are these consistent with our hypothesis? What should we compare with these? Expected values -- Time 12-3 3-6 6-9 9-12 Count 17 8 6 10 16 15 19 Time 12-3 3-6 6-9 9-12 Observed 17 8 6 10 16 15 19 Expected 12.38

10
Test Statistic Our test statistic is

11
**Chi-squared Distribution**

Family of curves identified by deg of freedom (k-1) Mean = degrees of freedom Variance = 2(degrees of freedom) As deg of freedom increases, curves approach normal

12
**How we’ll use the chi-squared distribution**

We’ll use the chi-squared distribution to determine our p-value If our test statistic is large, then we’ll reject the null hypothesis P-value

13
**How can we find p-values?**

Calculate the chi-squared statistic Use the chi-squared table Use the chi-squared cdf function on your TI-83

14
The Chi-square table

15
Graphing Calculator 2nd DIST

16
**Graphing Calculator STAT Inputs Observed data list Expected data list**

Degrees of freedom

17
**Assumptions We have 2 assumptions for this test**

First, the observed cell counts are based on a random sample (our sample is random) Our sample is large. How do we determine large? Expected cell counts must all be greater than or equal to 5

18
**Our conclusions? The same as we’ve always done**

We reject or fail to reject the null based on a comparison of the p-value and our significance level We interpret our conclusion in the context of our alternative hypothesis

19
**Let’s summarize Use the same 9 steps for hypothesis testing**

Identify the parameter Null Alternative Choose significance level Test Statistic Assumptions Calculate Test Statistic Determine P-value Make your conclusion

20
**Let’s finish the Geico Problem**

Let’s identify the parameter Proportion of teenage accidents Null Hypothesis Ho: The percent of teenage motor vehicle deaths is the same for each time period. Alternative Hypothesis Ha: The percent of teenage motor vehicle deaths is not the same for each time period. Significance level

21
**Continuing … Test Statistic Assumptions: The sample is random**

The sample is large

22
**Continuing … Calculate the Test Statistic Time 12-3 3-6 6-9 9-12**

Observed 17 8 6 10 16 15 19 Expected 12.38

23
Conclusion We fail to reject the null hypothesis because the p-value (.056) is greater than the significance level (.01). The data does not suggest that the distribution of accidental deaths is not distributed differently among the time periods.

24
Homework 7-1 Read section 12.1 in the textbook 12.10 12.12 12.14

25
**Let’s try this for some practice**

Using a test, investigate whether it’s reasonable to assume the random number table is random. Use a significance level of .05.

26
**Tests for Homogeneity and Independence**

In these tests we’ll be taking n samples and looking at one characteristic. Take samples of 1000 people from 4 different countries and ask how they feel about whether the use of torture against suspected terrorist is justified. In this case we’d like to see if the responses are distributed equally (homogenous) among the countries. Or, we’ll take one sample and look at two characteristics. Take a sample of 300 adults and determine each person’s political philosophy and what television news station they watch In this case we’d like to see if political philosophy and news station are independent.

27
**Let’s do an example of each**

First, let’s do a test for independence Big Office is a chain of large office supply stores that sell an extensive line of desktop and laptop computers. Company executives want to know whether the demands for these types of computers are related in any way. They might act as complementary products or sales may not be related. Big Office randomly selected 250 business days categorized demand for each type of computer as Low, MedLow, MedHi and Hi. Desktops Low MedLow MedHi Hi 4 17 5 43 8 23 22 27 80 16 20 14 70 10 19 11 57 38 77 72 63 250 Laptops

28
**So how many samples do we have**

So how many samples do we have? Is the data we are collecting categorical or numerical? How many characteristics are we investigating? How many classes within those characteristics?

29
**Hypotheses Statements**

Ho: The two variables are independent. Ha: The two variables are not independent.

30
**How do we test for independence?**

Low MedLow MedHi Hi 4 17 5 43 8 23 22 27 80 16 20 14 70 10 19 11 57 38 77 72 63 250 Recall that if events A and B are independent So, we’ll assume the two variables are independent. Then we’ll determine expected cell counts for each cell. We’ll look at the differences between the expected and observed counts for out test.

31
Test Statistic

32
**Assumptions The sample is random. The sample is large**

Each expected cell count is at least 5

33
At this point … We can calculate the p-value using the chi-squared table The chi-squared CDF function on our calculator Or, use the chi-squared test

34
**Let’s look at the Chi-Squared Test**

This test is a piece of cake …. First, put your observed matrix into the calculator STAT – TEST – Now just select Calculate The calculator creates the Expected matrix Output : value of your test statistic and p-value

35
**So let’s do this problem using our 9 steps of hypothesis testing**

36
**Chi-Squared Test for Homogeniety**

The paper “No Evidence of Impaired Neurocognitive Performance in Collegiate Soccer Players’ compared collegiate soccer players, athletes in sports other than soccer, and a group of students who were not involved in collegiate sports with respect to head injuries. Three independent random samples were chosen and each person in the sample was asked to complete a medical history survey. The following 2-way contingency table was created based on reported concussions. 1 2 3+ Soccer 45 25 11 10 91 Other 68 15 8 5 96 Non 3 53 158 22 240

37
**So how many samples do we have?**

Is the data we are collecting categorical or numerical? How many characteristics are we investigating? How many classes within those characteristics?

38
**Hypotheses Statements**

Ho: The populations are homogenous or, The category proportions are the same for all populations. Ha: The populations are not homogenous. or, the category proportions are not the same for all populations.

39
Test Statistic

40
**Assumptions The samples are random and independent.**

The sample is large The expected cell counts are at least 5

41
**Everything else is the same**

Let’s finish this test using our 9 steps. 1 2 3+ Soccer 45 25 11 10 91 Other 68 15 8 5 96 Non 3 53 158 22 240

42
**To summarize … Test for Independence Test for Homogeneity One sample**

Two characteristics Assumptions: Sample is random Sample is large Test for Homogeneity Multiple samples One characteristic Samples are independent and random Samples are large

43
Homework 7-2 Read Section 12.2 12.18 12.22

Similar presentations

OK

Chapter 11: Categorical Data n Chi-square goodness of fit test allows us to examine a single distribution of a categorical variable in a population. n.

Chapter 11: Categorical Data n Chi-square goodness of fit test allows us to examine a single distribution of a categorical variable in a population. n.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google