The summaries are always in the form of a data frame

`conf_interval()`

--- displays coefficients and their confidence intervals`R2()`

--- R-squared of a model together with related measures such as F, adjusted R-squared, the p-value, and degrees of freedom used in calculating the p-value.`regression_summary()`

-- A regression report in data-frame format.`anova_summary()`

--- An ANOVA report in data-frame format. If only one model is passed as an argument, the data frame will have one line per model term. If multiple models are given as arguments, the ANOVA report will show the increments from one model to the next.

## Usage

```
conf_interval(model, level = 0.95, show_p = FALSE)
R2(model)
regression_summary(model)
anova_summary(...)
```

## Arguments

- model
A model as produced by

`model_train()`

,`lm()`

,`glm()`

, and so on- level
Confidence level to use in

`conf_interval()`

(default: 0.95)- show_p
For

`conf_interval()`

, append the p-value to the report.- ...
One or more models (for ANOVA)

## Details

Many of these are wrappers around `broom::tidy()`

used to
emphasize to students that the results are a summary in the form of a regression
report, similar to the summaries produced by `stats::confint()`

, `stats::coef()`

, etc.

## Examples

```
Model <- FEV |> model_train(FEV ~ age + smoker)
Model |> conf_interval()
#> # A tibble: 3 × 4
#> term .lwr .coef .upr
#> <chr> <dbl> <dbl> <dbl>
#> 1 (Intercept) 0.207 0.367 0.527
#> 2 age 0.215 0.231 0.247
#> 3 smokersmoker -0.368 -0.209 -0.0504
Model |> R2()
#> n k Rsquared F adjR2 p df.num df.denom
#> 1 654 2 0.5765875 443.2539 0.5752867 0 2 651
Model |> anova_summary()
#> # A tibble: 3 × 6
#> term df sumsq meansq statistic p.value
#> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 age 1 281. 281. 880. 5.54e-123
#> 2 smoker 1 2.14 2.14 6.70 9.86e- 3
#> 3 Residuals 651 208. 0.319 NA NA
```