# Introduction to Regression with statsmodels in Python

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in Python.

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## Course Description

Linear regression and logistic regression are two of the most widely used statistical models. They act like master keys, unlocking the secrets hidden in your data. In this course, you’ll gain the skills you need to fit simple linear and logistic regressions. Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, including motor insurance claims, Taiwan house prices, fish sizes, and more. By the end of this course, you’ll know how to make predictions from your data, quantify model performance, and diagnose problems with model fit.

- 1
### Simple Linear Regression Modeling

**Free**You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

A tale of two variables50 xpWhich one is the response variable?50 xpVisualizing two numeric variables100 xpFitting a linear regression50 xpEstimate the intercept50 xpEstimate the slope50 xpLinear regression with ols()100 xpCategorical explanatory variables50 xpVisualizing numeric vs. categorical100 xpCalculating means by category100 xpLinear regression with a categorical explanatory variable100 xp - 2
### Predictions and model objects

In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

Making predictions50 xpPredicting house prices100 xpVisualizing predictions100 xpThe limits of prediction100 xpWorking with model objects50 xpExtracting model elements100 xpManually predicting house prices100 xpRegression to the mean50 xpHome run!50 xpPlotting consecutive portfolio returns100 xpModeling consecutive returns100 xpTransforming variables50 xpTransforming the explanatory variable100 xpTransforming the response variable too100 xpBack transformation100 xp - 3
### Assessing model fit

In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand each observation's leverage and influence to create the model.

Quantifying model fit50 xpCoefficient of determination100 xpResidual standard error100 xpVisualizing model fit50 xpResiduals vs. fitted values50 xpQ-Q plot of residuals50 xpScale-location50 xpDrawing diagnostic plots100 xpOutliers, leverage, and influence50 xpLeverage50 xpInfluence50 xpExtracting leverage and influence100 xp - 4
### Simple Logistic Regression Modeling

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Why you need logistic regression50 xpExploring the explanatory variables100 xpVisualizing linear and logistic models100 xpLogistic regression with logit()100 xpPredictions and odds ratios50 xpProbabilities100 xpMost likely outcome100 xpOdds ratio100 xpLog odds ratio100 xpQuantifying logistic regression fit50 xpCalculating the confusion matrix100 xpDrawing a mosaic plot of the confusion matrix100 xpAccuracy, sensitivity, specificity100 xpMeasuring logistic model performance100 xpCongratulations50 xp

#### Maarten Van den Broeck

Content Developer at DataCamp

Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. After his career as a Ph.D. researcher at KU Leuven, he wished that he had discovered DataCamp sooner. He loves to combine education and data science to develop DataCamp courses. In his spare time, he runs a symphonic orchestra.

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Devon Edwards Joseph

Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden

Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers

Decision Science Analytics, USAA

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