![]() 0079, so heteroskedasticity is definitely a problem. 05, then heteroskedasticity is a problem. Here are the results for the model above: In Stata, we can test for heteroskedasticity by typing hettest after a regression. Given the statistical significance, you might be included to stop here, but you still need to test for heteroskedasticity. For every added $100 in pocket money, a child spends another $3.73 on candy. When we run the ordinary least squares (OLS) regression, it happens to be significant, F(1, 38) = 19.26, p <. How about a dataset that tracks children’s spending on candy as a function of their monthly pocket money? We can graph the relationship between these two variables (treating candy spending as a dependent variable and pocket money as an independent variable), and, as expected, as pocket money goes up, so does spending on candy. ![]() WallStreetMojo has an excellent definition of heteroskedasticity that you should check out.įor non-specialists, the best way to understand statistical concepts is often by diving into an example, so let’s examine one. In this blog entry, we’ll offer you a real-world example of heteroskedasticity and explain how to address this problem. This is part of more advanced ordinary least squares (OLS) regression procedures commonly conducted and reported on in quantitatively oriented academic papers, research essays, and theses. ![]() After conducting linear regression in a program such as Stata, you will want to conduct some diagnostics and check some assumptions. ![]()
0 Comments
Leave a Reply. |