Question

Short Answer: Please select 4 out of 5 questions to answer (49 pts) 1. The U.S. Bureau of Mines produces data on the price of minerals. Shown here are the average prices per year for several minerals over a decade. Use these data and multiple regression to produce a model to predict the average price of gold from the other variables. (leave 3 decimal places) (12.25 pts) Copper 64.2 93.3 101.3 84.2 72.8 76.5 66.8 67 66.1 82.5 120.5 Silver 4.4 11.1 20.6 10.5 Gold 161.1 308 613 460 376 424 361 318 368 448 438 382.6130.9 39.8 61 71.6 76 76 77.8 81 81 81 72.3 110.1 87.8 114 6.1 5.5 6.5 (1). After import the data to R, and build the multiple linear regression model, we receive the following output. Based on the output, write down the multiple linear regression model. (2.25 pts) Call: 1m(formula - MineSGold - MineSCopper + MinesSilver MineSAluminun Coefficients: CIntercept) MineSCopper MineSSilver MineSALuminum 18.78349 0.86963 3.53782 -51.57489 (2). Based on the following output, given alpha - 0.05, manually test if the built regression model is better than the average (v) model lazy model). (10 pts) Call: ovCformula - Mine. model) Terms: MinesCopper MineSSi lver MineSAluminum Residuals 26208.34 22845.47 Sum of Squares Deg. of Freedom 22572.95 55663.78 Residual standard error: 53.43859 Estinated effects may be unbal anced

Answer 1

Solution1:

From output:

Regression equation is

Gold=-51.575+0.07*copper+18.783*silver+3.538*aluminium