New code for Section 3 on Multiple Regression

SDS220_new_section_3_multiple_regression.Rmd

# Multiple linear regression

## Regression table

Instructions:

- Indicate which model you selected: parallel slopes or interaction.
- Fit your selected regression model and display the regression table in the code 
block below
- Modify the template formula below from ModernDive 6.1.2 to match your model (this formula is written in the LaTeX typesetting language for printing mathematical formulas)

```{r}
# Insert code to fit your regression model and show the regression table. 

```

$$
\widehat{y} = 
4.883 
- 0.018 \cdot \text{age}  
- 0.446 \cdot \mathbb{1}_{\text{is male}}(x)
+ 0.014 \cdot \text{age} \cdot \mathbb{1}_{\text{is male}}(x)
$$


## Interpreting regression coefficients

Interpret all regressions coefficients in the `estimate` column in the following list:

1. Interpretation 1
1. Interpretation 2
1. ...


## Inference for multiple regression

**You will complete the rest of Section 3 at the project resubmission phase due the last day of exams.**

From the above regression table, interpret the confidence interval and p-value for at least two non-intercept terms:

1. Interpretation 1
1. Interpretation 2
1. (optional) Interpretation 3
1. (optional) ...


## Conditions for inference for regression

Verify the **LINE** conditions for inference for regression for all confidence intervals and p-values from
your regression table to have valid interpretation

### Linearity of relationship

### Independence of residuals

### Normality of residuals

```{r, fig.cap = "Figure 6: INSERT TITLE", fig.align = "center"}
# Insert code to visualize the distribution of the residuals:

```

### Equality of variance

```{r, fig.cap = "Figure 6: INSERT TITLE", fig.align = "center"}
# Insert code to visualize the relationship between the residuals and your
# numerical explanatory/categorical variable:

```

```{r, fig.cap = "Figure 7: INSERT TITLE", fig.align = "center"}
# Insert code to visualize the relationship between the residuals and your
# categorical explanatory/categorical variable:

```


***