Jul 11, 2022 · Well, under the hypothetical scenario that the true regression coefficient is equal to 0, statisticians have figured out how likely a given Z-score is (using the normal distribution curve). Z …

Dec 21, 2016 · Those words connote causality, but regression can work the other way round too (use Y to predict X). The independent/dependent variable language merely specifies how one thing depends …

Apr 15, 2016 · The word "regressed" is used instead of "dependent" because we want to emphasise that we are using a regression technique to represent this dependency between x and y. So, this …

Also, for OLS regression, R^2 is the squared correlation between the predicted and the observed values. Hence, it must be non-negative. For simple OLS regression with one predictor, this is equivalent to …

There ain’t no difference between multiple regression and multivariate regression in that, they both constitute a system with 2 or more independent variables and 1 or more dependent variables.

I have run a simple linear regression of the natural log of 2 variables to determine if they correlate. My output is this: R^2 = 0.0893 slope = 0.851 p < 0.001 I am confused. Looking at the $...

Mar 22, 2023 · When running a multiple regression analysis, why do we not need to correct the p-values for the amount of predictors in the model? summary(lm(mpg ~ disp + hp + drat + wt + gear, …

Feb 10, 2023 · What would be a "reasonable" minimal number of observations to look for a trend over time with a linear regression? what about fitting a quadratic model? I work with composite indices of …

another way of thinking about the n-2 df is that it's because we use 2 means to estimate the slope coefficient (the mean of Y and X) df from Wikipedia: "...In general, the degrees of freedom of an …

How to determine which variables are statistically significant in multiple regression? Ask Question Asked 13 years, 4 months ago Modified 3 years, 4 months ago