# Nick Seewald's Slide Repository

A catch-all repository for Nick's HTML slides

# Slides for Nick’s STATS 250 Labs - Fall 2020

PDF versions of the slides are available on our Lab Resources page on Canvas.

All materials © 2020 Nicholas J. Seewald

### Introduction Video

Welcome to STATS 250 Lab! Please watch this video for details on lab policy and logistics.

Lab videos are available on Youtube! [Video Playlist]

## Lab 1: Getting Started with R

Lab materials for the week of August 31, 2020

### Statistical Learning Goals

1. Learn how to visualize categorical data in a bar chart
2. Learn how to summarize quantitative and categorical data

### R Learning Goals

1. Learn the difference between R, RStudio, and R Markdown
2. Become familiar with the RStudio interface
3. Understand key components of an R Markdown document
4. Become familiar with R functions and arguments

## Lab 2: Basics of Data with R

Lab materials for the week running Friday 9/4 - Friday 9/11. All labs are asynchronous due to Labor Day.

### Statistical Learning Objectives

1. Understand the structure of data (observations and variables)
2. Think about the scope of a data set: what questions can and cannot be answered with a particular data set?

### R Learning Objectives

1. Learn how to “assign” information to “objects” in R
2. See how R “reads in” a data set from a file
3. Be able to identify the names of variables contained in a data set
4. Make a frequency table for one or two variables

## Lab 4: Probability and Scatterplots

Lab materials for the week running Friday 9/18 - Friday 9/25.

### Statistical Learning Objectives

1. Sampling with replacement versus sampling without replacement
2. The Law of Large Numbers and expected values
3. Scatterplots with linear associations
4. The correlation coefficient

### R Learning Objectives

1. Creating a sequence of integers between two values.
2. Learning how to randomly sample from a set, with replacement or without replacement.
3. Creating a plot of (x,y) quantitative values.
4. Finding the correlation coefficient between two quantitative variables.

## Lab 5: Plotting and Linear Regression

Lab materials for the week running Friday 9/25 - Friday 10/2.

### Statistical Learning Objectives

1. Interpret a correlation matrix
2. Interpret a fitted linear regression model
3. Check the fit of the linear regression model using \(R^2\)
4. Explore the dangers of extrapolation

### R Learning Objectives

1. Dive deeper into R plotting
2. Create a correlation matrix
3. Use R to fit a linear regression model

## Lab 6: Simulation Basics

Lab materials for the week running Friday 10/2 - Friday 10/9

### Statistical Learning Objectives

1. Explore sample-to-sample variation
2. Investigate probability using long-run proportions

### R Learning Objectives

1. Learn about reproducible randomness by “setting seeds”
2. Functions within functions: `table(sample())`
3. Line graphs in R

## Lab 7: Simulation-Based Hypothesis Testing

Lab materials for the week running Friday 10/9 - Friday 10/16

### Statistical Learning Objectives

1. Get experience with randomization under an independence model
2. Explore hypothesis testing and p-values

### R Learning Objectives

1. Learn how to perform simulations under an independence model

## Lab 8: Simulation-Based Hypothesis Testing

Lab materials for the week running Friday 10/16 - Friday 10/23

### Statistical Learning Objectives

1. Discover the central limit theorem for proportions

## Lab 9: Normal Distribution

Lab materials for the week running Friday 10/23 - Friday 10/30

### Statistical Learning Objectives

1. Understand Z scores and percentiles
2. Get experience with the normal distribution

### R Learning Objectives

1. Learn how to use R to work with the normal distribution

## Lab 10: Confidence Intervals and Hypothesis Tests for Proportions

Lab materials for the week running Friday 10/30 - Friday 11/6.

All labs are asynchronous this week due to the election.

### Statistical Learning Objectives

1. Understand how confidence intervals are constructed
2. Understand what a confidence level means
3. Consider the relationship between confidence intervals and hypothesis testing

### R Learning Objectives

1. Interpret R output providing confidence intervals and hypothesis tests for inference on population proportions.
2. Use R as a calculator to compute a confidence interval

## Lab 11: Confidence Intervals and Hypothesis Tests for One Mean

Lab materials for the week running Friday 11/6 - Friday 11/13.

### Statistical Learning Objectives

1. Get experience making confidence intervals for population means
2. Understand hypothesis tests for population means

### R Learning Objectives

1. Interpret R output providing confidence intervals and hypothesis tests for inference on population means.

## Lab 12: Paired Data and Difference of Two Means

Lab materials for the week running Friday 11/16 - Friday 11/20.

### Statistical Learning Objectives

1. Continue discussing quantitative data, this week in regards to a paired mean and a difference in two means scenario.
2. Understand whether data is considered paired or from two independent samples.

### R Learning Objectives

1. Create a “difference” variable in a data.frame
2. Learn how to use R to perform paired t-tests and t-tests for two independent samples.

## Lab 13: Linear Regression Inference

Lab materials for the week running Monday 11/30 - Friday 12/4.

### Statistical Learning Objectives

1. Learn about how to make inference for linear regression parameters
2. Learn about conditions needed for valid inference in regression

### R Learning Objectives

1. Learn how to interpret output from `lm()` to make inference in regression
2. Learn how to use R to check conditions for valid inference in regression