(BQ) Part 2 book "Essentials of business statistics" has contents: Hypothesis testing, statistical inferences based on two samples, experimental design and analysis of variance, simple linear regression analysis, multiple regression and model building, Chi-Square tests. | CHAPTER 9 Hypothesis Testing Learning Objectives After mastering the material in this chapter, you will be able to: LO9-1 Set up appropriate null and alternative hypotheses. LO9-2 Describe Type I and Type II errors and their probabilities. LO9-3 Use critical values and p-values to perform a z test about a population mean when s is known. LO9-4 Use critical values and p-values to perform LO9-6 Calculate Type II error probabilities and the power of a test, and determine sample size (Optional). LO9-7 Describe the properties of the chi-square distribution and use a chi-square table. LO9-8 Use the chi-square distribution to make statistical inferences about a population variance (Optional). a t test about a population mean when s is unknown. LO9-5 Use critical values and p-values to perform a large sample z test about a population proportion. Chapter Outline The Null and Alternative Hypotheses and Errors in Hypothesis Testing z Tests about a Population Mean: s Known t Tests about a Population Mean: s Unknown z Tests about a Population Proportion Type II Error Probabilities and Sample Size Determination (Optional) The Chi-Square Distribution Statistical Inference for a Population Variance (Optional) H ypothesis testing is a statistical procedure used to provide evidence in favor of some statement (called a hypothesis). For instance, hypothesis testing might be used to assess whether a population parameter, such as a population mean, differs from a specified standard or previous value. In this chapter we discuss testing hypotheses about population means and proportions. In order to illustrate how hypothesis testing works, we revisit several cases introduced in previous chapters and also introduce some new cases: C The e-billing Case: The consulting firm uses hypothesis testing to provide strong evidence that the new electronic billing system has reduced the mean payment time by more than 50 percent. The Cheese Spread Case: The .
