I was just wondering why regression problems are called "regression" problems. What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state."
This kind of regression seems to be much more difficult. I've read several sources, but the calculus for general quantile regression is going over my head. My question is this: How can I calculate the slope of the line of best fit that minimizes L1 error? Some constraints on the answer I am looking for:
Multivariable regression is any regression model where there is more than one explanatory variable. For this reason it is often simply known as "multiple regression". In the simple case of just one explanatory variable, this is sometimes called univariable regression. Unfortunately multivariable regression is often mistakenly called multivariate regression, or vice versa. Multivariate ...
The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be the ...
What statistical tests or rules of thumb can be used as a basis for excluding outliers in linear regression analysis? Are there any special considerations for multilinear regression?
I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mea...
Linear regression can use the same kernels used in SVR, and SVR can also use the linear kernel. Given only the coefficients from such models, it would be impossible to distinguish between them in the general case (with SVR, you might get sparse coefficients depending on the penalization, due to $\epsilon$-insensitive loss)
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-scores greater than 2 (in absolute value) only occur about 5% of the time when the true regression coefficient is equal to 0.
A quick question: Is "residual standard error" the same as "residual standard deviation"? Gelman and Hill (p.41, 2007) seem to use them interchangeably.
Just to complement what Chris replied above: The F-statistic is the division of the model mean square and the residual mean square. Software like Stata, after fitting a regression model, also provide the p-value associated with the F-statistic. This allows you to test the null hypothesis that your model's coefficients are zero. You could think of it as the "statistical significance of the ...
