Feb 3, 2015 · For simple linear regression, the r-squared of best fit line is always described as the proportion of the variance explained, but I am not sure what to make of that either. Is proportion of …

None of the three plots show correlation (at least not linear correlation, which is the relevant meaning of 'correlation' in the sense in which it is being used in "the residuals and the fitted values are …

What is the main difference between maximum likelihood estimation (MLE) vs. least squares estimaton (LSE) ? Why can't we use MLE for predicting $y$ values in linear ...

Jan 15, 2016 · I have fish density data that I am trying to compare between several different collection techniques, the data has lots of zeros, and the histogram looks vaugley appropriate for a poisson …

Jan 6, 2021 · Three linear machine learning algorithms: Linear Regression, Logistic Regression and Linear Discriminant Analysis. Five nonlinear algorithms: Classification and Regression Trees, Naive …

Jul 18, 2015 · regression hypothesis-testing least-squares goodness-of-fit Share Cite Improve this question

I have made a generalised linear model with a single response variable (continuous/normally distributed) and 4 explanatory variables (3 of which are factors and the fourth is an integer). I have us...

Pretty basic question: What does a normal distribution of residuals from a linear regression mean? In terms of, how does this reflect on my original data from the regression? I'm totally stumped,

May 2, 2017 · How do I use the standard regression assumptions to prove that $\hat {\sigma}^2$ is an unbiased estimator of $\sigma^2$? (2 answers)

I have a question that has been confusing me ever since I took econometrics last year. What does the "root MSE" mean in Stata output when you regress a OLS model? I know that it translates into "r...