Are two linear regressions significantly different?
The slopes of the two regression lines (one for single-mated females and one for triple-mated females) are not significantly different (F1, 36=1.1, P=0.30). The Y intercepts are significantly different (F1, 36=8.8, P=0.005); females that have mated three times have significantly more offspring than females mated once.
How does Lincom work Stata?
lincom computes point estimates, standard errors, t or z statistics, p-values, and confidence intervals for linear combinations of coefficients after any estimation command, including survey estimation. Results can optionally be displayed as odds ratios, hazard ratios, incidence-rate ratios, or relative-risk ratios.
When do you say the correlation coefficient is significant?
If the test concludes that the correlation coefficient is significantly different from zero, we say that the correlation coefficient is “significant.” Conclusion: There is sufficient evidence to conclude that there is a significant linear relationship between x and y because the correlation coefficient is significantly different from zero.
How to test for difference in coefficient between two groups?
The most direct way to test for a difference in the coefficient between two groups is to include an interaction term into your regression, which is almost what you describe in your question. The model you would run is the following: y i = α + β x i + γ g i + δ (x i × g i) + ε i
When is a coefficient not significant in regression?
There are several considerations here. First, when the p-value is not significant, the coefficient is indistinguishable from zero statistically. In other words, your sample provides insufficient evidence to conclude that the sample effect exists in the population. In that light, you don’t consider the sign.
How is the correlation coefficient determined in the hypothesis test?
The hypothesis test lets us decide whether the value of the population correlation coefficient (rho) is “close to zero” or “significantly different from zero”. We decide this based on the sample correlation coefficient (r) and the sample size (n).