# Multiple Linear Regression

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### Multiple Linear Regression

Multiple linear regression is an extension of simple linear regression used to model the relationship between a quantitative response variable and two or more predictors, which may be quantitative, categorical, or a mix of both. This allows us to control for confounding variables, which may distort the perceived relationship between two variables if not accounted for.

### Multiple Regression Coefficient

Instead of a single slope, the multiple linear regression equation has a “slope,” called a partial regression coefficient, for each predictor. For example, a multiple linear regression equation predicting sales from the predictors temperature and day might look like:

$sales = -256.8 + 31.5*temperature - 22.3*day$

The “slopes” are 31.5 and -22.3, while -256.8 is the intercept.

### Multiple Regression in Python

Multiple linear regression models can be implemented in Python using the statsmodels function OLS.from_formula() and adding each additional predictor to the formula preceded by a +. For example, the example code shows how we could fit a model predicting income from variables for age, highest education completed, and region.

import statsmodels.api as sm

model = sm.OLS.from_formula('income ~ age + highest_edu + region', data).fit()

### Binary Predictors in Multiple Regression

Suppose that we fit a multiple regression model and calculate the following regression equation:

$sales = 200 - 50*rain + 2*temperature$

If rain is a binary categorical variable that is equal to 1 when it rains and 0 when it does not rain, we can write the following two regression equations:

When it rains:

$sales = 200 + 2*temperature$

When it doesn’t rain:

$sales = 250 + 2*temperature$

Therefore, the coefficient on rain (50) is the difference in expected sales for rain days compared to non-rain days.

### Multiple Linear Regression Interpretation

In multiple linear regression:

• The intercept is the expected value of the response variable when all predictors equal zero.
• The slope coefficient on each predictor is the expected difference in the outcome variable for a one-unit increase of the predictor, holding all other predictors constant.

### Visualizing a Multiple Regression Model

We can visualize and understand multiple linear regression as creating a new regression equation for each value of a predictor. For example, the provided image shows a visualization of the following regression model: 'score = hours_studied + assignments'. In the plot, there are three regression lines, each for a different value of assignments. ### Coefficients in Multiple Regression

Suppose that we fit a regression model to predict sales using temperature as a predictor. If we fit another model predicting sales using both temperature and rain as predictors, the coefficient on temperature will likely be different in the two models.

### Multicollinearity Assumption

The assumptions for multiple regression are the same as for simple linear regression, except for the additional assumption that the predictors are not highly correlated with one another (no multicollinearity).

### Multicollinearity in Python

Multicollinearity can be checked visually in Python using seaborn’s heatmap() function. The provided code shows how we can create a heat map of quantitative variable correlations for a dataset called flowers.

import seaborn as sns
# get table of variable correlations
var_corr = flowers.corr()
# plot the heatmap
sns.heatmap(var_corr, xticklabels=var_corr.columns, yticklabels=var_corr.columns, annot=True)
plt.show()

### Interaction Terms

In multiple linear regression, we can use an interaction term when the relationship between two variables is moderated by a third variable. This allows the slope coefficient for one variable to vary depending on the value of the other variable.

For example, this scatter plot shows happiness level on the y-axis against stress level on the x-axis. If we fit a model to predict happy using stress, exercise, and stress:exercise (an interaction between stress and exercise) as predictors, we can calculate the pictured regression lines (one for exercise = 0 and one for exercise = 1), which each have a different slope to model the relationship between stress and happy. ### Polynomial Terms

In multiple linear regression, we can use a polynomial term to model non-linear relationships between variables. For example, this plot shows a curved relationship between sleep and happy, which could be modeled using a polynomial term.

The coefficient on a polynomial term can be difficult to interpret directly; however, the picture is useful. In this example, we see that more sleep is associated with higher happiness levels up to some point, after which more sleep is associated with lower happiness. ### Interactions in Python

In the Python library statsmodels.api, interaction terms can be added to a multiple regression model formula by adding a term that has both predictors with a colon between them. For example, to fit a multiple regression model predicting income from the variables age, region, and the interaction of age and region, we could use the example code shown here. This creates a new predictor, which is the product of age and religion.

import statsmodels.api as sm
model = sm.OLS.from_formula('income ~ age + region + age:region', data).fit()

### Polynomial Terms in Python

In the Python library statsmodels.api, polynomial terms can be added to a multiple linear regression model formula by adding a term with the predictor of interest raised to a higher power. To do this, we use the NumPy function np.power() and specify the predictor name and degree. For example, the example code shows how to fit a model predicting nights from the variable trip_length with a quadratic (squared) term for trip_length.

import statsmodels.api as sm
import numpy as np

model = sm.OLS.from_formula('nights ~ trip_length + np.power(trip_length,2)', data).fit()

### Interactions with Binary and Quantitative

Consider the following multiple regression equation, where rain is equal to 1 if it rained and 0 otherwise:

$sales = 300 + 34*temperature - 49*rain + 2*temperature*rain$

On days where rain = 0, the regression equation becomes:

$sales = 300 + 34*temperature$

On days where rain = 1, the regression equation becomes:

$sales = 251 + 36*temperature$

Therefore, the coefficient on rain (-49) means that the intercept for rain days is 49 units lower than for non-rain days. Meanwhile, the slope on temperature:rain (2) means that the slope on temperature is 2 units higher for rain days than for non-rain days.

### Interactions with Two Quantitative

Consider the following multiple regression equation:

$sales = 300 + 4*temp + 3*humidity + 2*temp*humidity$

On days where humidity = 0, the regression equation becomes:

$sales = 300 + 4*temp$

On days where humidity = 1, the regression equation becomes:

$sales = 303 + 6*temp$

Therefore, the coefficient on humidity (3) means that the intercept increases by 3 for every additional unit of humidity. Meanwhile, the slope on temp:humidity (2) means that the slope on temp is 2 units higher for every additional unit of humidity.

### Polynomials in Regression Equation

In a multiple linear regression equation, a polynomial term will appear as the predictor raised to a higher exponent (such as squared, cubed, to the 4th, etc.). The output of a multiple linear regression predicting nights from the variable trip_length and the square of trip_length is shown.

# Output:
# Intercept                 4.6
# trip_length               6.3
# np.power(trip_length,2)  -0.4

Based on this output, the regression equation would have a term that raises trip_length to the second power:

$nights = 4.6 + 6.3*trip\_length - 0.4*trip\_length^2$