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

Getting Started with Data in R

Data Visualization

Data Wrangling

Data Importing and “Tidy” Data

Basic Regression

Multiple Regression

Statistical Inference with the infer Package

Bootstrapping and Confidence Intervals

Hypothesis Testing

Inference for Regression

Price Prediction With Regression Analysis in R

Tell a Story with Data

Appendix

Uber Data Analysis Using the R Language

Recap: Statistical Inference with infer

Sampling scenarios

Scenarios of Sampling for Inference