Recap: Statistical Inference with infer

Explore tactile and virtual sampling exercises to estimate unknown population proportions and parameters. Understand how sample size affects the precision of estimates through the central limit theorem. Learn to quantify sampling variation and the concept of bootstrap resampling for single samples.

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Sampling scenarios
Central limit theorem

Sampling scenarios

We performed both tactile and virtual sampling exercises to infer about an unknown proportion. We also presented a case study of sampling in real life with polls. In each case, we used the sample proportion $\hat{p}$ to estimate the population proportion $p$ . However, we aren’t just limited to scenarios related to proportions. In other words, we can use sampling to estimate other population parameters using other point estimates as well. We present four more such scenarios in the table below.

Scenario	Population Parameter	Notation	Point Estimate	Symbol(s)
1	Population proportion	𝑝	Sample proportion	𝑝̂
2	Population mean	𝜇	Sample mean	x̄ or 𝜇̂
3	Difference in population proportions	𝑝₁ − 𝑝₂	Difference in sample proportions	𝑝̂₁ - 𝑝̂₂
4	Difference in population means	𝜇₁ − 𝜇₂	Difference in sample mean	x̄₁ - x̄₂ or 𝜇̂₁ - 𝜇̂₂
5	Population regression slope	β₁	Fitted regression slope	b₁or 𝛽̂₁

1.Getting Started with Data in R

2.Data Visualization

3. Data Wrangling

4.Data Importing and “Tidy” Data

5.Basic Regression

6.Multiple Regression

7.Statistical Inference with the infer Package

8.Bootstrapping and Confidence Intervals

9.Hypothesis Testing

10.Inference for Regression

Project

11. Tell a Story with Data

12.Appendix

Project

Recap: Statistical Inference with infer

Sampling scenarios

Scenarios of Sampling for Inference