Unit 7: Sampling Distributions
Duration of Days: 16
The Central Limit Theorem (CLT) and how it guarantees Normality for large samples.
The difference between a parameter (population) and a statistic (sample).
Describe the shape, center, and variability of sampling distributions for proportions and means.
Calculate the probability of obtaining a specific sample result given a population claim.
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HSS-IC.A.1: Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
HSS-IC.B.4: Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling.
HSS-ID.A.4: Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages.
HSS-IC.A.2: Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.
| Lesson # | Lesson Title | Duration of Days |
|---|---|---|
| 1 | 7.1 What is a Sampling Distribution? | 2 |
| 2 | 7.2 Sampling Distributions: Center and Variability | 3 |
| 3 | 7.3 The Sampling Distribution of a Sample Proportion | 3 |
| 4 | 7.4 The Sampling Distribution of a Sample Mean | 3 |
| 5 | 7.5 The Central Limit Theorem | 3 |
| 6 | Review and Assessment | 2 |