regression.Rmd

---
title: "Regression"
# output: html_notebook
---

## 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)
```

```{r}
evals_ch5 %>% 
  get_correlation(score ~ bty_avg)
```

Get correlation

```{r}

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


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

### weak correlation

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

```{r}
evals_ch5 %>% 
  get_correlation(age ~ score)
```

## summary 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)


glance(score_model)
get_regression_table(score_model)

```

### more

```{r}
get_regression_table(score_model)
```


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

```{r}
get_regression_summaries(score_model)
```


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



### More model data

```{r}
get_regression_points(score_model)
```

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