This example shows how to perform a regression
with categorical covariates using categorical arrays and
MPG contains measurements on
the miles per gallon of 100 sample cars. The model year of each car
is in the variable
the weight of each car.
Draw a scatter plot of
grouped by model year.
figure() gscatter(Weight,MPG,Model_Year,'bgr','x.o') title('MPG vs. Weight, Grouped by Model Year')
The grouping variable,
has three unique values,
82, corresponding to model years 1970, 1976,
Create a table that contains the variables
Model_Year. Convert the variable
a nominal array.
cars = table(MPG,Weight,Model_Year); cars.Model_Year = nominal(cars.Model_Year);
Fit a regression model using
the dependent variable, and
the independent variables. Because
a categorical covariate with three levels, it should enter the model
as two indicator variables.
The scatter plot suggests that the slope of
differ for each model year. To assess this, include weight-year interaction
The proposed model is
fit = fitlm(cars,'MPG~Weight*Model_Year')
fit = Linear regression model: MPG ~ 1 + Weight*Model_Year Estimated Coefficients: Estimate SE ___________ __________ (Intercept) 37.399 2.1466 Weight -0.0058437 0.00061765 Model_Year_76 4.6903 2.8538 Model_Year_82 21.051 4.157 Weight:Model_Year_76 -0.00082009 0.00085468 Weight:Model_Year_82 -0.0050551 0.0015636 tStat pValue ________ __________ (Intercept) 17.423 2.8607e-30 Weight -9.4612 4.6077e-15 Model_Year_76 1.6435 0.10384 Model_Year_82 5.0641 2.2364e-06 Weight:Model_Year_76 -0.95953 0.33992 Weight:Model_Year_82 -3.2329 0.0017256 Number of observations: 94, Error degrees of freedom: 88 Root Mean Squared Error: 2.79 R-squared: 0.886, Adjusted R-Squared 0.88 F-statistic vs. constant model: 137, p-value = 5.79e-40
The regression output shows:
a nominal variable, and constructs the required indicator (dummy)
variables. By default, the first level,
the reference group (use
reorderlevels to change
the reference group).
The model specification,
specifies the first-order terms for
and all interactions.
The model R2 = 0.886, meaning the variation in miles per gallon is reduced by 88.6% when you consider weight, model year, and their interactions.
The fitted model is
|Model Year||Predicted MPG Against Weight|
The relationship between
an increasingly negative slope as the model year increases.
Plot the data and fitted regression lines.
w = linspace(min(Weight),max(Weight)); figure() gscatter(Weight,MPG,Model_Year,'bgr','x.o') line(w,feval(fit,w,'70'),'Color','b','LineWidth',2) line(w,feval(fit,w,'76'),'Color','g','LineWidth',2) line(w,feval(fit,w,'82'),'Color','r','LineWidth',2) title('Fitted Regression Lines by Model Year')
Test for significant differences between the slopes. This is equivalent to testing the hypothesis
ans = SumSq DF MeanSq F pValue Weight 2050.2 1 2050.2 263.87 3.2055e-28 Model_Year 807.69 2 403.84 51.976 1.2494e-15 Weight:Model_Year 81.219 2 40.609 5.2266 0.0071637 Error 683.74 88 7.7698
0.0072(from the interaction row,
Weight:Model_Year), so the null hypothesis is rejected at the 0.05 significance level. The value of the test statistic is
5.2266. The numerator degrees of freedom for the test is
2, which is the number of coefficients in the null hypothesis.
There is sufficient evidence that the slopes are not equal for all three model years.