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.

Waarom je logistische regressie nodig hebt

De verklarende variabelen verkennen

Lineaire en logistische modellen visualiseren

Logistische regressie met glm()

Voorspellingen en odds ratio’s

Kansen

Meest waarschijnlijke uitkomst

Odds ratio

Log-oddsratio

De kwaliteit van een logistisch regressiemodel kwantificeren

De confusion matrix berekenen

De prestaties van een logistiek model meten

Prestatiematen voor logistische regressie

Accuracy

Sensitivity

Specificity

Accuracy, sensitivity, specificity

Gefeliciteerd

Simple logistic regression

Taiwan Real Estate

Ad conversion data

Churn data

S&P 500 Yearly Returns

Lineaire regressie en logistische regressie zijn de twee meest gebruikte statistische modellen en werken als loper voor het ontrafelen van geheimen in gegevenssets. In deze cursus leer je hoe je eenvoudige lineaire en logistische regressies kunt fitten. Met hands-on oefeningen onderzoek je relaties tussen variabelen in echte gegevenssets, zoals schadeclaims bij autoverzekeringen, huizenprijzen in Taiwan, vislengtes en meer. Aan het einde van deze cursus kun je voorspellingen doen op basis van je data, de modelprestatie kwantificeren en problemen met de modelfit diagnosticeren.

Introduction to Data Visualization with ggplot2

Introduction to Statistics in R

Leer hoe je woningprijzen en advertentie-klikfrequentie voorspelt met lineaire en logistische regressie in R.

Introductie tot regressie in R

Voorspel huizenprijzen en het aantal klikken op advertenties door regressieanalyse te doen, te bekijken en te begrijpen in R. 

Waarom je logistische regressie nodig hebt

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