Linear regression excel how to#

We now turn our attention to the intercepts.

75275, which supports the null hypothesis that the two slopes are not significantly different. From cell P22, we see that the p-value for this coefficient is. Thus, the null hypothesis that the two slopes are equal is equivalent to b 3 = 0.

For females, g = 1 and so the regression model becomes y = ( b 0 + b 2) + ( b 1 + b 3) x with slope b 1 + b 3. The reformatted data is shown in range G4:J21.įor males, g = 0 and so the regression model becomes y = b 0 + b 1 x with slope b 1. We also use the interaction xg of x and g as an independent variable. We combine the two samples in Figure 1 into one sample using the dummy variable g (for gender) where g = 0 for male samples and g = 1 for female samples. In Figure 2, we show another approach to achieving this result. Using the formula =SlopesTest(A5:A12,B5:B12,D5:D13,E5:E13,TRUE) in range A15:B18, we see that there is no significant difference between the two slopes (p-value =. If there is no significant difference in the slopes, determine whether there is a significant difference in the intercepts.įigure 1 – Slopes test for independent samples Determine whether there is any significant difference in the slopes for these two populations. The first sample is for males and the second for females. ExampleĮxample 1: In Figure 1, we show two samples, each comparing life expectancy vs. We start by showing another approach to solving Example 1 of Comparing the Slopes for Two Independent Samples.

Linear regression excel how to#

We now show how to extend this test to intercepts as well as slopes, and to more than two independent samples. In Comparing the Slopes for Two Independent Samples, we show how to test whether the slopes of two independent samples are significantly different.