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 variables

Which one is the response variable?

Visualizing two variables

Fitting a linear regression

Estimate the intercept

Estimate the slope

Linear regression with lm()

Categorical explanatory variables

Visualizing numeric vs. categorical

Calculating means by category

lm() with a categorical explanatory variable

Simple Linear Regression

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 predictions

Predicting house prices

Visualizing predictions

The limits of prediction

Working with model objects

Extracting model elements

Manually predicting house prices

Using broom

Regression to the mean

Home run!

Plotting consecutive portfolio returns

Modeling consecutive returns

Transforming variables

Transforming the explanatory variable

Transforming the response variable too

Predictions and model objects

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 the leverage and influence of each observation used to create the model.

Quantifying model fit

Coefficient of determination

Residual standard error

Visualizing model fit

Residuals vs. fitted values

Q-Q plot of residuals

Scale-location

Drawing diagnostic plots

Outliers, leverage, and influence

Leverage

Influence

Extracting leverage and influence

Assessing model fit

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 regression

Exploring the explanatory variables

Visualizing linear and logistic models

Logistic regression with glm()

Predictions and odds ratios

Probabilities

Most likely outcome

Odds ratio

Log odds ratio

Quantifying logistic regression fit

Calculating the confusion matrix

Measuring logistic model performance

Accuracy, sensitivity, specificity

Congratulations

Simple logistic regression

Taiwan Real Estate

Ad conversion data

Churn data

S&P 500 Yearly Returns

Linear regression and logistic regression are the two most widely used statistical models and act like master keys, unlocking the secrets hidden in datasets. 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.

Introduction to Data Visualization with ggplot2

Introduction to Statistics in R

Learn how you can predict housing prices and ad click-through rate by implementing, analyzing, and interpreting linear and logistic regressions using R.

Introduction to Regression in R

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

Associate Data Scientist in R

Statistiker in R

Grundlagen der Statistik in R

Q-Q plot of residuals

Introduction to Regression in R

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