Curve Fitting with Linear and Nonlinear Regression
We often think of a relationship between two variables as a straight line. That is, if you increase the predictor by 1 unit, the response always increases by X units. However, not all data have a linear relationship, and your model must fit the curves present in the data.
Regression Analysis: How to Interpret the Constant (Y Intercept)
Regression Analysis: How to Interpret the Constant (Y Intercept)
How to Interpret Regression Analysis Results: P-values and Coefficients
How to Interpret Regression Analysis Results: P-values and Coefficients
Multiple Regression Analysis: Use Adjusted R-Squared and Predicted R-Squared to Include the Correct Number of Variables
Multiple Regression Analysis: Use Adjusted R-Squared and Predicted R-Squared to Include the Correct Number of Variables
Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit?
Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit?
What Are the Effects of Multicollinearity and When Can I Ignore Them?
What Are the Effects of Multicollinearity and When Can I Ignore Them?
Enough Is Enough! Handling Multicollinearity in Regression Analysis
Enough Is Enough! Handling Multicollinearity in Regression Analysis
Violations of the Assumptions for Linear Regression: Closing Arguments and Verdict
Violations of the Assumptions for Linear Regression: Closing Arguments and Verdict
Violations of the Assumptions for Linear Regression: Residuals versus the Fits (Day 3)
Violations of the Assumptions for Linear Regression: Residuals versus the Fits (Day 3)
Orthogonal Regression: Testing the Equivalence of Instruments
Orthogonal Regression: Testing the Equivalence of Instruments