2.2 The Statistical Sommelier: An Introduction to Linear Regression | 2.2 The Statistical Sommelier: An Introduction to Linear Regression | 2 Linear Regression | The Analytics Edge | Sloan School of Management

<Video 4: Linear Regression in R
2.2.1Video 1: Predicting the Quality of Wine
2.2.2Quick Question
2.2.3Video 2: One-Variable Linear Regression
2.2.4Quick Question
2.2.5Video 3: Multiple Linear Regression
2.2.6Quick Question
2.2.7Video 4: Linear Regression in R
2.2.8Quick Question
2.2.9Video 5: Understanding the Model
2.2.10Quick Question
2.2.11Video 6: Correlation and Multicollinearity
2.2.12Quick Question
2.2.13Video 7: Making Predictions
2.2.14Quick Question
2.2.15Video 8: Comparing the Model to the Experts
>Video 5: Understanding the Model

Quick Question

In R, use the dataset wine (CSV) to create a linear regression model to predict Price using HarvestRain and WinterRain as independent variables. Using the summary output of this model, answer the following questions:

What is the "Multiple R-squared" value of your model?

What is the coefficient for HarvestRain?

What is the intercept coefficient?

Explanation

In R, create the model by typing the following line into your R console:

modelQQ4 = lm(Price ~ HarvestRain + WinterRain, data=wine)

Then, look at the output of summary(modelQQ4). The Multiple R-squared is listed at the bottom of the output, and the coefficients can be found in the coefficients table.