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 4 hours.
4.1: Point Estimators and Their Characteristics
4.1.1: Sample Statistics and Parameters
4.1.2: Bias and Sampling Variability
4.2: Confidence Intervals
4.2.1: Confidence Intervals for Mean
4.2.2: Confidence Intervals for Correlation and Proportion
Unit 4 Assessment
- Receive a grade