Loss,x1,x2372,45,162206,55,233175,61,232154,66,231136,71,231112,71,23755,81,22445,86,219221,53,203166,60,189164,64,210113,68,21082,79,19632,81,180228,56,200196,68,173128,75,18897,83,16164,88,119249,59,161219,71,151186,80,165155,82,151114,89,128341,51,161340,59,146283,65,148267,74,144215,81,134148,86,127
I am asking the R studio Code, pleas leave your code and commenthere, thanks a lot!
Q1.The data file abrasion contains the resultsfrom a small scale study (Davies, O.L. and Goldsmith, P.L.Statistical methods in Research and Production,1972),  of the relation between rubber's resistance toabrasion (Y) and rubber hardness (X1) and rubber tensile strength(X2).
The data set abrasion is in Course Content-> Data Sets AL -> Ch04
- Import the data set into R.
- Obtain the scatter plot matrix and the correlation matrix.Youcan do this together using the commands in the file pairs.r
Upload the results here (one file in .png or .pdf formats) -Remember to include a title.
Q2.
Run the regression model. Obtain the estimates of thecoefficients (round answer to 4 decimal places, it the answer is7.5e-08 enter 0)
| Coefficient | estimate | se | p-value |
| b0 | __ | __ | __ |
| b1 | __ | __ | __ |
| b2 | __ | __ | __ |
Which variable is significant? __ (enter exactly on of the threeoptions: x1, x2 or both)
Q3.
Enter here the coefficient of determination (adjustedR-squared). Round your answer to 4 decimal places.
Q4.
Enter here the estimate for σ, that iss or the residual standard error.Round your answer to 2 decimal places.
Q5.
Use your model to obtain the mean abrasion loss for rubber withhardness 71 an tensile strength 201. Round your answer to 2 decimalplaces.
Q6.
Use your model to obtain a 98% confidence interval for the meanabrasion loss for rubber with hardness 71 an tensile strength201.
Enter here the Lower Bound for the confidence interval. Roundyour answer to 2 decimal places.
Q7.
After the scatter plots, the correlation between the variables,the summary of the model, R-squared and s, and the F-test, brieflycomment on the adequacy of the model fit.
