https://github.com/tysonstanley/educ7610

Functions and data for Darlington's and Hayes' "Regression Analysis and Linear Models" book used in Utah State University's EDUC 7610 course
https://github.com/tysonstanley/educ7610

learning-by-doing regression regression-diagnostics

Last synced: about 2 months ago
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Functions and data for Darlington's and Hayes' "Regression Analysis and Linear Models" book used in Utah State University's EDUC 7610 course

• Host: GitHub
• URL: https://github.com/tysonstanley/educ7610
• Owner: TysonStanley
• License: gpl-3.0
• Created: 2018-07-26T00:21:18.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-09-17T19:08:26.000Z (over 5 years ago)
• Last Synced: 2024-10-13T14:14:20.892Z (3 months ago)
• Topics: learning-by-doing, regression, regression-diagnostics
• Language: R
• Homepage:
• Size: 66.4 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# `educ7610`

The goal of `educ7610` is to make several aspects of the Regression course (EDUC/PSY 7610) at Utah State University more accessible. Specifically, it provides the data from Darlington and Hayes' book "Regression Analysis and Linear Models", a syntax to perform diagnostics and Johnson-Neyman, odds ratios from logistic regression, among other things. In conjunction with packages like `interactions` and the `easystats` group of packages, this package can make regression analyses more straightforward.

## Installation

You can install the development version from GitHub with:

``` r

# install.packages("remotes")

remotes::install_github("tysonstanley/educ7610")

```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example}

library(educ7610)

data("poverty")  ## load the poverty data set

model <- lm(TeenBirth ~ ViolentCrime + poverty_pct,

           data = poverty)

diagnostics(model) %>% head()

```