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.

Voorspellingen maken

Huisprijzen voorspellen

Voorspellingen visualiseren

De grenzen van voorspellen

Werken met modelobjecten

Modelonderdelen uitlezen

Handmatig huizenprijzen voorspellen

broom gebruiken

Regressie naar het gemiddelde

Homerun!

Opeenvolgende portefeuillerendementen plotten

Opeenvolgende rendementen modelleren

Variabelen transformeren

De verklarende variabele transformeren

Ook de responsvariabele transformeren

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

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. 

Regressie naar het gemiddelde

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