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 …

2016年12月23日 · 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 …

2017年5月2日 · How do I use the standard regression assumptions to prove that $\hat {\sigma}^2$ is an unbiased estimator of $\sigma^2$? (2 answers)

2023年12月5日 · 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 …

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 …

2016年1月15日 · 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 …

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 …

2020年9月23日 · In a log-level regression, the independent variables have an additive effect on the log-transformed response and a multiplicative effect on the original untransformed response:

2020年4月24日 · I have data containing several variables. I ran a regression model. Prior to running the model I have normalized the dependent variable Y and the independent variables …

2023年2月10日 · 25 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 …