蜜桃传媒破解版下载

In this module, we will define a hypothesis test and develop the intuition behind designing a test. We will learn the language of hypothesis testing, which includes definitions of a null hypothesis, an 听alternative hypothesis, and the level of significance of a test. We will walk through a very simple test.

In this module, we will expand the lessons of Module 1 to composite hypotheses for both one and two-tailed tests. We will define the 鈥減ower function鈥� for a test and discuss its interpretation and how it can lead to the idea of a 鈥渦niformly most powerful鈥� test. We will discuss and interpret 鈥減-values鈥� as an alternate approach to hypothesis testing.

In this module, we will learn about the chi-squared and t distributions and their relationships to sampling distributions. We will learn to identify when hypothesis tests based on these distributions are appropriate. We will review the concept of sample variance and derive the 鈥渢-test鈥�. Additionally, we will derive our first two-sample test and apply it to make some decisions about real data.

In this module, we will consider some problems where the assumption of an underlying normal distribution is not appropriate and will expand our ability to construct hypothesis tests for this case. We will define the concept of a 鈥渦niformly most powerful鈥� (UMP) test, whether or not such a test exists for specific problems, and we will revisit some of our earlier tests from Modules 1 and 2 through the UMP lens. We will also introduce the F-distribution and its role in testing whether or not two population variances are equal.

In this module, we develop a formal approach to hypothesis testing, based on a 鈥渓ikelihood ratio鈥� that can be more generally applied than any of the tests we have discussed so far. We will pay special attention to the large sample properties of the likelihood ratio, especially Wilks鈥� Theorem, that will allow us to come up with approximate (but easy) tests when we have a large sample size. We will close the course with two chi-squared tests that can be used to test whether the distributional assumptions we have been making throughout this course are valid.

You will complete a proctored exam worth 21% of your grade made up of multiple choice and free response questions. You must attempt the final in order to earn a grade in the course. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.