• Unit 4: Estimation with Confidence Intervals

    In this unit, you will learn how to use the central limit theorem and confidence intervals, the latter of which enables you to estimate unknown population parameters. The central limit theorem provides us with a way to make inferences from samples of non-normal populations. This theorem states that given any population, as the sample size increases, the sampling distribution of the means approaches a normal distribution. This powerful theorem allows us to assume that given a large enough sample, the sampling distribution will be normally distributed. 

    You will also learn about confidence intervals, which provide you with a way to estimate a population parameter. Instead of giving just a one-number estimate of a variable, a confidence interval gives a range of likely values for it. This is useful, because point estimates will vary from sample to sample, so an interval with certain confidence level is better than a single point estimate. After completing this unit, you will know how to construct such confidence intervals and the level of confidence. 

    Completing this unit should take you approximately 10 hours.

    • 4.1: Point Estimators and Their Characteristics

    • 4.2: Confidence Intervals

    • End of Unit Assessment

      • Unit 4 Assessment Quiz

        Please take this assessment to check your understanding of the materials presented in this unit.

        Notes:

        • There is no minimum required score to pass this assessment, and your score on this assessment will not factor into your overall course grade.
        • This assessment is designed to prepare you for the Final Exam that will determine your course grade. Upon submission of your assessment you will be provided with the correct answers and/or other feedback meant to help in your understanding of the topics being assessed.
        • You may attempt this assessment as many times as needed, whenever you would like.