Loading in 5 sec....

Chris Morgan, MATH G160 csmorgan@purdue April 13, 2012 Lecture 30PowerPoint Presentation

Chris Morgan, MATH G160 csmorgan@purdue April 13, 2012 Lecture 30

Download Presentation

Chris Morgan, MATH G160 csmorgan@purdue April 13, 2012 Lecture 30

Loading in 2 Seconds...

- 106 Views
- Uploaded on
- Presentation posted in: General

Chris Morgan, MATH G160 csmorgan@purdue April 13, 2012 Lecture 30

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 - - - - - - - - - - - - - - - - - - - - - - - - - -

Chris Morgan, MATH G160

csmorgan@purdue.edu

April 13, 2012

Lecture 30

Chapter 2.4:

Chi-Squared (χ2) Test and Independence between two Categorical Variables

- Any table which allows you to observe multiple pieces of information to help find conditional, joint, and marginal probabilities
- Expected Counts: the expected count in any cell of a two-way table when the null hypothesis is true
- The null hypothesis is what you think to be true given previous research, outside readings, or personal opinion based on an educated guess

- Above is a sample of students in the College of Business. They were asked their chosen major and their sex.
- What is the probability that a student is a Finance Major?
- What is the probability that a student is Female?

- Above is a sample of students in the College of Business. They were asked their chosen major and their sex.
- 3. What is the probability that a student is female given that the person is in Administration?

- Above is a sample of students in the College of Business. They were asked their chosen major and their sex.
- 4. What is the probability that a student is an Administration major given that the student is female?

- Our null hypothesis is what we expect to see given no interaction between variables
- Our alternative hypothesis is some improvement or change on the null hypothesis
- Never accept the Ha
- Always “reject the Ho” or “fail to reject the Ho”
- Why?

- For the chi-square test:
- Ho: there is no association between two categorical variables, and we conclude they’re independent
- Ha: there is an association between two categorical variables, and we conclude there is a relationship

- Denoted χ2
- The observed count is whatever value we see in the table
- The expected count for each cell in the table can be found by taking:

Note: We can safely use the χ² test under two important conditions:

1. when no more than 20% of the expected counts are less than five

2. when all individual expected counts are one or greater

- I can compare the calculated chi-square test-statistic to a critical value to see if my variables do in fact have a relationship
- We will denote the test statistic as χ²* and the critical value as χ²α, (r-1)(c-1) where r is the number of rows, c is the number of columns, and the degrees of freedom is found by: df = (r-1)*(c-1). I can then look up the critical value in the table (see next slide) using the alpha level and df
- If: | χ²*| > χ²α, (r-1)(c-1)
…then we will reject the null hypothesis and conclude the alternative hypothesis, that the observed values were sufficiently far away from the expected value, meaning it is a significant result and there exists a relationship between the two variables

- If: | χ²*| ≤ χ²α, (r-1)(c-1)
…then we fail to reject the null hypothesis and the two variables are independent (meaning no relationship exists)

Chi-Square (χ²) Distribution Critical Values

The first row is the alpha level

The first column is the number of df

- Returning to example one, is there a relationship between gender and major?
- Find expected counts
- Compare expected counts to observed counts
- Calculate χ²
- Compare chi-squaretest statistic (χ²*) to chi-square critical value (χ²α, (r-1)(c-1) )

Recall the equation for expected counts:

Recall the equation for chi-square:

Recall the equation for chi-square:

Now we just have to add them all together:

and compare the chi-square value to the critical value…

To compare the chi-square value to the critical value I look up in the table the value for the chi-squared critical value when alpha = 0.05 and df = 3:

Therefore, since the absolute value of the test statistic is less than or equal to the critical value we (circle one):

reject the Ho fail to reject the Hoaccept the Ho accept the Ha

And conclude….what?:

- Is there a relationship between favorite soda and favorite ice cream?
- Find expected counts
- Compare expected counts to observed counts
- Calculate χ²
- Compare chi-squaretest statistic (χ²*) to chi-square critical value (χ²α, (r-1)(c-1) )

Recall the equation for expected counts:

Recall the equation for chi-square:

Recall the equation for chi-square:

Now we just have to add them all together:

and compare the chi-square value to the critical value…

To compare the chi-square value to the critical value I look up in the table the value for the chi-squared critical value when alpha = 0.05 and df = ____:

Therefore, since the absolute value of the test statistic is less than or equal to the critical value we (circle one):

reject the Ho fail to reject the Hoaccept the Ho accept the Ha

And conclude….what?:

When calculating a chi-squared value:

1. Find expected counts

2. Compare expected counts to observed counts

3. Calculate a χ² test statistic

4. Compare test statistic to critical value using table

5. Make a conclusion

If | χ²*| > χ²α, (r-1)(c-1) REJECT THE NULL: relationship exists

If | χ²*| ≤ χ²α, (r-1)(c-1) FAIL TO REJECT THE NULL: independent, no relationships exists

NEVER SAY ACCEPT THE NULL!!!!