- Spline: a smooth function built out of smaller, component functions 
- B-splines divide the range of a predictor variable into parts
- Assign a parameter to each part and the parameters are turned on and off so they sum to a fancy curve 
- Create new predictor variables that make up a basis function

University of Michigan - Ann Arbor

Ohio State University, Columbus

Multiple regression is a statistical technique that extends simple regression by measuring several predictor variables ($$X_1, X_2, X_3, \dots, X_i$$) and using them to predict a single outcome variable ($$Y$$). It can also be utilized to describe the complex relationship between the outcome variable and a set of predictors. The result of this analysis is an equation that expresses the outcome variable as an additive combination of all the measured predictor variables.

Multiple Regression

McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd ed.). Chapman and Hall/CRC.

Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd edition)

- Uses powers of a variable (squares and cubes) as extra predictors 
- Need to first standardize the predictor variable 
Considerations: 
- A better fit to the sample does not necessarily mean a better model
- Fitting a causal relationship gives us no new information about the model 

Polynomial regression

Basis-Splines (B-Splines)

Multiple regression model looks like the polynomial model which was introduced before but we will dive into it, and use this method to figure out "correlation". 
Multiple regression have more independent varialbles, or explanatory variables, to determine the output (the dependent variable)

Multiple Regression Model 

The multiple regression equation expresses the outcome variable as an additive combination of several predictor variables. Its general form is: $$Y = b_1X_1 + b_2X_2 + b_3X_3 + \dots + b_iX_i$$, where $$Y$$ is the predicted outcome, the $$X$$ terms are the individual predictor variables, and the corresponding $$b$$ terms are their regression weights.

Multiple Regression Formula

The primary advantage of multiple regression is its ability to show whether a specific predictor variable makes a unique contribution to an outcome variable over and above the contributions made by other predictors. This allows researchers to statistically control for other variables while examining a specific relationship.

Advantage of Multiple Regression

If a researcher investigates how income and health relate to happiness, they face a confound because income and health are correlated. By using a multiple regression analysis that includes both as predictor variables, the researcher can statistically control for one while examining the other. This determines whether the part of income unrelated to health predicts happiness, and whether the part of health unrelated to income predicts happiness.

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