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.

Mengapa Anda memerlukan regresi logistik

Mengeksplorasi variabel penjelas

Memvisualisasikan model linear dan logistik

Regresi logistik dengan glm()

Prediksi dan rasio odds

Probabilitas

Hasil yang paling mungkin

Rasio odds

Log odds ratio

Mengukur kecocokan regresi logistik

Menghitung confusion matrix

Mengukur kinerja model logistik

Metrik kinerja logistik

Akurasi

Sensitivitas

Spesifisitas

Akurasi, sensitivitas, spesifisitas

Selamat

Simple logistic regression

Taiwan Real Estate

Ad conversion data

Churn data

S&P 500 Yearly Returns

Regresi linear dan regresi logistik adalah dua model statistik yang paling banyak digunakan dan berfungsi seperti kunci utama yang membuka rahasia tersembunyi dalam himpunan data. Dalam kursus ini, Anda akan mempelajari keterampilan yang diperlukan untuk memasangkan regresi linear dan logistik sederhana. Melalui latihan praktik, Anda akan menelusuri hubungan antarvariabel dalam himpunan data dunia nyata, termasuk klaim asuransi kendaraan bermotor, harga rumah di Taiwan, ukuran ikan, dan lainnya. Di akhir kursus, Anda akan memahami cara membuat prediksi dari data Anda, mengukur kinerja model, dan mendiagnosis masalah pada kecocokan model.

Introduction to Data Visualization with ggplot2

Introduction to Statistics in R

Pelajari cara memprediksi harga rumah dan rasio klik iklan dengan regresi linear dan logistik di R.

Pengantar Regresi di R

Prediksi harga properti dan tingkat klik iklan dengan menerapkan, menganalisis, dan menafsirkan analisis regresi menggunakan R. 

Associate Data Scientist di R

Ahli Statistik di R

Dasar-Dasar Statistik di R

Proporsi pelanggan yang diprediksi model dengan benar apakah mereka churn atau tidak.

Proporsi pelanggan yang melakukan churn di mana model memprediksi dengan benar bahwa mereka churn.

Proporsi pelanggan yang tidak melakukan churn di mana model memprediksi dengan benar bahwa mereka tidak churn.

Akurasi, sensitivitas, spesifisitas

Pengantar Regresi di R

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