Chapter 15 - Nonparametric methods: Goodness-of-fit tests. After completing this chapter, students will be able to: Conduct a test of hypothesis comparing an observed set of frequencies to an expected distribution, list and explain the characteristics of the chi-square distribution, compute a goodness-of-fit test for unequal expected frequencies,. | Nonparametric Methods: Goodness-of-Fit Tests Chapter 15 Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin LEARNING OBJECTIVES LO 15-1 Conduct a test of hypothesis comparing an observed set of frequencies to an expected distribution. LO 15-2 List and explain the characteristics of the chi-square distribution. LO 15-3 Compute a goodness-of-fit test for unequal expected frequencies. LO 15-4 Conduct a test of hypothesis to verify that data grouped into a frequency distribution are a sample from a normal distribution. LO 15-5 Use graphical and statistical methods to determine whether a set of sample data is from a normal distribution. LO 15-6 Perform a chi-square test for independence on a contingency table. 15- In this chapter we’re going to learn the characteristics and the uses of the Chi square distribution. We’re going to conduct a test of hypothesis comparing the observed frequencies to an expected distribution. This is also known as goodness of fit test. Using graphical methods we will determine if a set of sample data is from a normal distribution and using hypothesis testing procedure, we will verify that data grouped into a frequency distribution is a sample from a normal distribution. We will also see how the Chi square distribution is used to test whether two classification variables are related. Characteristics of the Chi-Square Distribution The major characteristics of the chi-square distribution: It is positively skewed. It is non-negative. It is based on degrees of freedom. When the degrees of freedom change a new distribution is created. LO 15-2 List and explain the characteristics of the chi-square distribution. 15- Goodness-of-Fit Test: Equal Expected Frequencies Let f0 and fe be the observed and expected frequencies, respectively. H0: There is no difference between the observed and the expected frequencies. H1: There is a difference between the observed and the expected frequencies. The . | Nonparametric Methods: Goodness-of-Fit Tests Chapter 15 Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin LEARNING OBJECTIVES LO 15-1 Conduct a test of hypothesis comparing an observed set of frequencies to an expected distribution. LO 15-2 List and explain the characteristics of the chi-square distribution. LO 15-3 Compute a goodness-of-fit test for unequal expected frequencies. LO 15-4 Conduct a test of hypothesis to verify that data grouped into a frequency distribution are a sample from a normal distribution. LO 15-5 Use graphical and statistical methods to determine whether a set of sample data is from a normal distribution. LO 15-6 Perform a chi-square test for independence on a contingency table. 15- In this chapter we’re going to learn the characteristics and the uses of the Chi square distribution. We’re going to conduct a test of hypothesis comparing the observed frequencies to an expected distribution. This is also known
