regression.qmd

---
title: "Regression"

date-modified: 'today'
date-format: long

format: 
  html:
    footer: "CC BY 4.0 John R Little"

license: CC BY   
---

## Load library packages

```{r}
#| message: false
#| warning: false
library(dplyr)
library(ggplot2)
#library(gapminder)
library(moderndive)
library(broom)
```

## Data

data are from the `moderndive` package. [*Modern dive*](https://moderndive.com/) by Ismay and Kim.

```{r}
evals_ch5 <- evals %>% 
  select(ID, score, bty_avg, age)

evals

evals_ch5
```

```{r}
evals_ch5 %>% 
  summary()
```

```{r}
skimr::skim(evals_ch5)
```

## Correlation

```{r}

starwars %>% 
  filter(mass < 500) %>% 
  summarise(cor(mass, height))
```

### weak correlation

```{r}
evals_ch5 %>% 
  ggplot(aes(score, age)) +
  geom_jitter() +
  geom_smooth(method = lm)
```


## Linear model

> For every increase of 1 unit increase in bty_avg, there is an associated increase of, on average, 0.067 units of score. from [*ModenDive*](https://moderndive.com/5-regression.html)

```{r}
# Fit regression model:
score_model <- lm(score ~ bty_avg, data = evals_ch5)

score_model
```

```{r}
summary(score_model)
```


## the tidy way

### broom

tidy the model fit with broom::tidy()

```{r}
broom::tidy(score_model)
```

get evaluative measure into a data frame

```{r}
broom::glance(score_model)
```

### More model data


```{r}
broom::augment(score_model)
```