# Removing constant from the regression model

I am trying to calibrate two variables $$(X,Y)$$ of different measuring techniques from two instruments, the result of the linear regression analysis appears as shown in the image.

The result shows the regression constant is not statistically significant but the model is significant. I have tried to remove the regression constant (it is a very small value close to zero) and $$R$$ of the new model is raised to 90%. Is it correct to remove the regression constant? • Have a look at this thread. It's an interesting take on the regression without intercept. Also this was referred – RonsenbergVI Aug 26 '20 at 9:57
• regression coefficient/constant is an output part. you can not remove it. Further, your interpretation of summary outputs is invalid. The regression coefficient of the independent variable is highly significant. The significance of "constant" is neither required nor logical. It is not reflecting the effect of independent variable under consideration. – Subhash C. Davar Jan 13 at 1:53

When you estimate a linear model without constant, you essentially "force" the estimated function to go through the $$(0,0)$$ coordinates.

With an intercept, you estimate a linear function like:

$$y = \beta_0 + \beta_1 x .$$

Without intercept, you estimate a linear function like:

$$y = 0 + \beta_1 x .$$

So when $$x=0$$, $$y$$ will be $$0$$ as well.

You should not only look at $$R^2$$ since $$R^2$$ often will go up when you have no intercept. Think about the structure of your model, how the data look like, and what you want to achieve.

Example in R:

library(ISLR)
auto = ISLR::Auto

ols1 = lm(mpg~horsepower,data=auto)
summary(ols1)
plot(auto$$horsepower, auto$$mpg)
lines(auto$horsepower, predict(ols1, newdata=auto), type="l", col="red") ols2 = lm(mpg~horsepower+0,data=auto) summary(ols2) plot(auto$$horsepower, auto$$mpg) lines(auto$horsepower, predict(ols2, newdata=auto), type="l", col="red")


Results:

Model with intercept:

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 39.935861   0.717499   55.66   <2e-16 ***
horsepower  -0.157845   0.006446  -24.49   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.906 on 390 degrees of freedom
Multiple R-squared:  0.6059,    Adjusted R-squared:  0.6049
F-statistic: 599.7 on 1 and 390 DF,  p-value: < 2.2e-16


Model without intercept:

Coefficients:
Estimate Std. Error t value Pr(>|t|)
horsepower 0.178840   0.006648    26.9   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.65 on 391 degrees of freedom
Multiple R-squared:  0.6492,    Adjusted R-squared:  0.6483
F-statistic: 723.7 on 1 and 391 DF,  p-value: < 2.2e-16


Summary:

In this example, excluding the intercept improved the $$R^2$$ but by forcing the (estimated) function to go through $$(0,0)$$, the model results are entirely different. In essence, the model without intercept produces bullshit in this case. So be very careful to exclude the intercept term.  • I think the model in my result should go through zero because the constant is too small near zero and it is not statistically significant. – Yazan Alatoom Aug 26 '20 at 19:22
• Significance is not a good argument here. If you have good reasons to believe that the model should go precisely through 0, OK. Otherwise include the intercept. – Peter Aug 26 '20 at 19:48
• Can you explain how does removing the intercept term improve the R^2? I can't wrap my head around it; more degrees of freedom = better fit, no? – Itamar Mushkin Aug 30 '20 at 7:54
• Actually, I can‘t but had the same puzzle. Hard to imagine, that R2 is better for the second plot, compared to the first one. – Peter Aug 30 '20 at 9:44
• The reason for R² increasing is actually explained here. In short R calculates R² differently if no intercept is present which leads to higher values for worse models. – Fnguyen Sep 8 '20 at 16:13