Suppose we have a dataset named
measurements with columns
weight. If we want to fit a model that can predict height based on weight, we would use the formula
'height ~ weight' as shown in the example code.
import statsmodels.api as smmodel = sm.OLS.from_formula('height ~ weight', data = measurements)results = model.fit()print(results.summary())
In order to use a simple linear regression model to make a prediction, we need to plug in the slope and intercept to the equation for a line (y=mx+b). For example, suppose we fit a linear model to predict weight based on height and calculate an intercept of -200 and slope of 5. The equation is:
weight = 5*height - 200
Therefore, a person who is 60 inches tall would be expected to weigh 100 pounds:
weight = 5*60-200 = 100
In simple linear regression the intercept is the expected (or average) value of the outcome variable when the predictor variable is equal to zero. The slope is the expected difference in the outcome variable for a one unit increase in the predictor. For example, if we fit a linear regression with weight as the outcome variable and height as the predictor, then:
The assumptions of simple linear regression are:
In order to check the linear functional form assumption for simple linear regression, we can plot a scatter plot of the outcome variable and predictor variable, then check whether the relationship is linear (can be represented by a straight line). For example, the provided plot of weight vs. height shows a linear relationship.
In order to check the normality assumption for simple linear regression, we can plot a histogram of the residuals and check whether they appear approximately normal (no skew or multimodality). For example, the provided histogram of residuals would meet the normality assumption.
In order to check the homoscedasticity assumption for linear regression, we can plot the residuals against the fitted values. If the assumption is met, the residuals should be symmetrically scattered around 0, with no funneling or other patterns. The provided plot demonstrates what this should look like if the assumption is met.
The fitted values for a linear regression model are the predicted values of the outcome variable for the data that is used to fit the model. For a statsmodels model object named
model that was fit using a dataframe named
data, the provided code shows how we could calculate the fitted values.
fitted_values = model.predict(data)
In linear regression, the residuals are the differences between each of the fitted values and true values of the outcome variable. They can be calculated by subtracting the fitted values from the true values.
We can fit a simple linear model with a categorical predictor. When we fit a regression with a binary categorical predictor, one category will be coded as
0 and the other as
1. The intercept will be the average value of the outcome variable for the category coded as
0 and the slope will be the difference in average value of the outcome variable for the two groups. The provided image shows this graphically.