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How to test the statistical significance of the difference of two univariate Linear Regression betas? Hi all, There are two samples of data: D1 and D2. … On data D1 we do a univariate Linear Regression and get the coefficient beta1. On data D2 we do a univariate Linear Regression and get the coefficient beta2. …

In general, I feel that I am quite weak in drawing observations and obtaining intuitions from regression studies... are there books focusing on these interpretations and intuitions? Thanks a lot! …

By adjusting the intercept for that group, I at least still can get the regression line passing the cloud, am I right? … Here by "passing the cloud" I mean not all points will reside on the same side of the regression line. In a random intercept model, for each group, which point does the regression line pass thru? …

If I do regression in two stages: Stage 1: $y\sim x_1 + 1$ Stage 2: resid_1st_stage $\sim x_2 + 1$ Will the resid_2nd_stage be orthogonal to $x_1$? …

What can go wrong if I include two categorical variables and intercept in linear regression? With: y~x1+x2 Both x1 and x2 are categorical variables, lets say x1 has 3 levels, x2 has 2 levels. …

\end{cases}$$ And I got regression results. How shall I interpret the regression results? … esp. how shall I interpret the two coefficients I get from this regression, $\beta_1 \text{ and } \beta_2$? …

Each group has a different within group regression line (same slope but different intercepts). The regression line within each group crosses the "cloud" consisting of the group members. … All data points in a group reside on one side of the regression line of that group? In a random intercept model, for each group, which point does the regression line pass thru? …

Beta stability in linear regression with high multi-collinearity? Let's say in a linear regression, the variables $x_1$ and $x_2$ has high multi-collinearity (correlation is around 0.9). …

Lets say I have a linear regression: $$y \sim 1 + x_1+x_2$$ where the range of $x_2$ is $[0,10]$. I fit this model using lm or rlm with regression weights in R. … After the same linear regression as above, if I take a smaller portion of the data, say all the data with $x_2<6$. …

Also, how can I add regression weights into the expression? …

We have a special type of regression which is called Geometric Mean Regression. … But for the above customized regression, how do we obtain the prediction intervals? Are there functions in R that can help us do this? We are thinking of using bootstrapping, etc. …

I am trying to connect the dots between statistics and linear algebra/optimization. As you know, Least Square problems are linear algebra and optimization problems. But they also can be connected to …

I am looking for a R command to test the difference of two linear regressoon betas. Lets say I have data $x_1, x_2...x_{n＋1}$. $\beta_1$ is obtained from regressing $x_1$ to $x_n$ onto $1$ to $n$. $ …

lm(y~x1 + x2 -1) where x1 is a continuous numerical variable and x2 is a categorical factor variable with 4 levels. Is there a way to measure the "correlation" between the x1 variable and each leve …

Does the non-normality matter in using regression for prediction? Hi all, In the Q-Q plot of the residuals after linear regression, the residuals turned out to be highly non-Gaussian. …