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

<The Statistical Sommelier: An Introduction to Linear Regression
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 2: One-Variable Linear Regression

Quick Question

The plots below show the relationship between two of the independent variables considered by Ashenfelter and the price of wine.

Plot of price vs. average growing season temperature.

What is the correct relationship between harvest rain, average growing season temperature, and wine prices?

More harvest rain is associated with a higher price, and higher temperatures is associated with a higher price

More harvest rain is associated with a higher price, and higher temperatures is associated with a lower price

More harvest rain is associated with a lower price, and higher temperatures is associated with a higher price

More harvest rain is associated with a lower price, and higher temperatures is associated with a lower price

Explanation

The plots show a positive trend between average growing season temperature and the wine price. While the trend is less clear between harvest rain and price, there is a slight negative association.