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

## Logistic Regression

If you see an error in the article, please comment or drop me an email. Logistic regression is a generalized linear model where the outcome is a categorical variable. Logistic regression can be binomial (using binary independent variables), ordinal (if categories are ordered) or multinomial (with more than two categories). Binary Generalized Linear Models Binary

## Poisson regression

If you see an error in the article, please comment or drop me an email. Poisson regression In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its

## Generalized Linear Models (Intro)

If you see an error in the article, please comment or drop me an email. Generalized linear models include linear models, but they go beyond to handle many of the issues with linear models. Limitations of Linear Models Additive response models don’t make much sense if the response is discrete (for instance binary data) Additive