Please refer to the data given below and use program R. Thanks.

\fRefer to the data on \"Abrasion loss\". Hand et al. describe the data set as follows: \"The data come -om an experiment to investigate how the resistance of rubber to abrasion is affected by the hardness of the rubber and its tensile strength. Each of 30 samples of rubber was tested for hardness (in degrees Shore; the larger the number, the harder the rubber) and for tensile strength (measured in kg per square centimetre), and was then subjected to steady abrasion for a xed time. The weight loss due to abrasion was meaSured in grams per hour.\" To summarize, the data set has one response variable, Abrasion Loss (units: g/h), and two predictor variables, Hardness (units: degrees S) and Tensile Strength (units: kg/cml). Most parts of this question will focus on simple linear regression and correlation analysis. A bonus part will provide a glimpse into multiple regression analysis. (a) Obtain the correlation coefcients between Abrasion loss and each of Hardness and Tensile Strength. Based on these values, briey explain why Hardness is likely a better predictor of Abrasion Loss than Tensile Strength is. (b) Obtain scatterplots of Abrasion Loss on Hardness and Abrasion Loss on Tensile Strength. Remember to label your axes properly, and include titles on the graphs. (0) Use R to obtain the simple linear regression equations that represent the least squares \"lines of best t\" for the data in each scatterplot. Report these equations. (d) Fit the lines given by your equations in (c) to the appropriate scatterplots. You will hand these graphs in