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Goodness of fit

chapter 14 chi square

Subject: 
Statistics
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Statistics
Statistical tests
Categorical data
Hypothesis testing
Non-parametric statistics
Statistical inference
Pearson's chi-squared test
Statistical hypothesis testing
Goodness of fit
P-value
Null hypothesis
Student's t-test

AP Statistics ? Chapter 14 Practice Test: The Chi-Square Distributions Part II, Free Response ? Show all work and communicate completely and clearly. 1. Computer software generated 500 random numbers that should look like they are from the uniform distribution on the interval 0 to 1. They are categorized into five groups: (1) less than or equal to 0.2 (2) greater than 0.2 and less than or equal to 0.4, (3) greater than 0.4 and less than or equal to 0.6, (4) greater than 0.6 and less than or equal to 0.8, and (5) greater than 0.8. The counts in the five groups are 113, 95, 108, 99, and 85, respectively. a. The probabilities for these five intervals are all the same. What is this probability?

chapter 11

Subject: 
Statistics
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Statistics
Statistical tests
Categorical data
Pearson's chi-squared test
Goodness of fit
Chi-squared distribution
Chi-squared test
Student's t-test
Degrees of freedom
Probability distribution
Statistic

AP Statistics Chapter 11 Outline 11.1 Chi-Square Goodness-Of-Fit Tests Chi-Square Statistic The chi-square statistic is a measure of how far the observed counts are from the expected counts: Where the sum is over all possible values of the categorical variable The Chi-Square Distribution The chi-square distributions are a family of distributions that take only positive values and are skewed to the right. A particular chi-square distribution is specified by giving its degrees of freedom. The chi-square goodness-of-fit test uses the chi-square distribution with degrees of freedom = the number of categories - 1. The Chi-Square Goodness-of-Fit Test
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