Must read:Is there a real future in data analysis for self-learners without a math degree?

## Fitting a function

If you see an error in the article, please comment or drop me an email. How to fit functions using linear models $Y_i = \beta_0 + \beta_1 X_i + \sum_{k=1}^d (x_i – \xi_k)_+ \gamma_k + \epsilon_{i}$ Simulated example Source: https://github.com/DataScienceSpecialization/courses Separate the n values into k+1 spans. (k standing for knots) Create a basis: a

## Inference for Multiple Linear Regression

If you see an error in the article, please comment or drop me an email. Inference for Multiple Linear Regression #Load the data cognitive <- read.csv("http://bit.ly/dasi_cognitive") Let us start with the full model, thus including all variables: #Fit the full model and show the summary cog_full <- lm(kid_score ~ mom_hs + mom_iq + mom_work +

## Multiple Linear Regression

If you see an error in the article, please comment or drop me an email. Conditions for multiple linear regression linear relationship between each (numerical) explanatory variable and the response – checked using scatterplots of y vs. each x, and residuals plots of residuals vs. each x nearly normal residuals with mean 0 – checked using a

## Model Selection

If you see an error in the article, please comment or drop me an email. Scott Zeger: “a model is a lense through which to look at your data”. George Box: “All models are wrong, some are useful.” Collinearity and parsimony Collinearity: a high correlation between two independent variables such that the two variables contribute