Question

Fit a polynomial model with degree 10 and use backward elimination to reduce the degree of the model. Plot your fitted model on top of the data. Use this model to predict the temperature in 2020.

a) Correct model and prediction
b) Incorrect model but correct prediction
c) Correct model but incorrect prediction
d) Incorrect model and prediction

Answer 1

The provided information relates to a linear regression model, and predictions can be made for sales growth by substituting the day into the linear equation given, not a polynomial model with degree 10.

Step-by-step explanation:

To fit a polynomial model with degree 10 and reduce the degree using backward elimination, one would typically start with a dataset and a high-degree polynomial model. However, backward elimination isn't mentioned in the data provided. Instead, provided details indicate a simple linear regression model for predicting sales growth, where the regression equation is given as îy = 101.32 + 2.48x. To predict sales on a given day, simply plug the value of the day into the x variable and calculate îy.

Prediction for Day 60

îy = 101.32 + 2.48(60) = 101.32 + 148.8 = 250.12 (thousands of dollars).

Prediction for Day 90

îy = 101.32 + 2.48(90) = 101.32 + 223.2 = 324.52 (thousands of dollars).