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:
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?
Note that one perspective on the relationship between regression & correlation can be discerned from my answer here: What is the difference between doing linear regression on y with x versus x with y?.
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...
A good residual vs fitted plot has three characteristics: The residuals "bounce randomly" around the 0 line. This suggests that the assumption that the relationship is linear is reasonable. The res...
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 the squared correlation between the predictor and the dependent variable -- again, this must be non-negative.
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.
