1. QUICK: Review of confidence intervals Inference: provides methods for drawing conclusions about a population from sample data. Confidence Intervals estimate a population parameter (mean or proportion) with some level of confidence. Because of what we know of Sampling Distributions (μ xbar = μ AND μ phat = p), we express our confidence in being able to capture the true μ or p in our own ONE sample.

2. Here is the idea of a Sampling Distribution

4. Inference Toolbox for Confidence Intervals 1.State Population and parameter of interest. 2.Check Conditions. 3.Calculate actual confidence interval. 4.Interpret results: I am ___% confident that the true mean μ ____(context)_______ is between ______ and _______.

5. Conditions for Inference Procedures SRS: The data are a SRS from the population of interest. Normality: Confidence Intervals with Means: 1. Use CLT when n>30. 2. Otherwise boxplot, stemplot, dotplot for symmetry, no extreme skewness or large outliers. Check Normal Probability Plot for linearity. Confidence Intervals with Proportions: 1. Check normality with n*phat > 10 and n*qhat >10 Independence: When sampling without replacement, check that n*10 < population size

6. One Sample z interval (σ known) One Sample t interval (σ unknown) MEANS: PROPORTIONS: One Sample z interval Proportion (1-phat) = qhat Formulas for Confidence Intervals

7. Vocabulary for Confidence Intervals Confidence Level Confidence Interval Margin of Error Standard Error