Please complete this for me in R studio using the code described in https://nulib.github.io/kuyper-stat202/

MLB11 Regression Analysis
Name of the student
University Name
Question1. Choose another traditional variable from mlb11 that you think might be a good predictor of runs. Produce a scatterplot of the two variables and fit a linear model. At a glance, does there seem to be a linear relationship?
Answer: Load the MLB11 dataset.
##Load the Data
download.file("http:
www.openintro.org/stat/data/mlb11.RData", destfile = "mlb11.RData")
load("mlb11.RData")
Dataset has 30 observations with 12 variables. Variables are as follows: "team" ,"runs" ,"at_bats","hits" , "homeruns", "bat_avg", "strikeouts" , "stolen_bases", "wins", "new_onbase", "new_slug" ,"new_obs" .
The linear model for the relationship between runs and hits is given by:
Runs=−375.5600+0.7589×Hits
An interpretation of the summary results are:
Intercept: The estimated intercept is -375.5600. It represents the estimated runs when the hits are zero. However, in the context of baseball, zero hits wouldn't make sense, so the intercept might not have a practical interpretation.
Coefficient for Hits: The estimated coefficient for hits is 0.7589. This means, on average, for each additional hit, the runs increase by 0.7589. This coefficient is statistically significant (p-value < 0.05), suggesting that hits is a significant predictor of runs.
R-squared: The R2 value is 0.6419, indicating that approximately 64.19% of the variability in runs can be explained by the linear relationship with hits.
F-statistic: The F-statistic tests the overall significance of the model. The p-value (1.043e-07) is less than 0.05, suggesting that the model is significant.
Residuals: The residuals (differences between observed and predicted values) have a mean close to zero, indicating that, on average, the model predicts well.
Scatterplot: The scatterplot with the fitted line suggests a positive linear relationship between hits and runs. As the number of hits increases, the runs also tend to increase.
In conclusion, based on the linear model and the scatterplot, there seems to be a positive linear relationship between hits and runs in the mlb11 dataset. The model is statistically significant, and hits can be considered a good predictor of runs.
Question2. How does this relationship compare to the relationship between runs and at_bats? Use the R22 values from the two model summaries to compare. Does your variable seem to predict runs better than at_bats? How can you tell?
Answer:
The R-squared values for the two models are as follows:
· R-square value for the model with hits: 0.6419
· R-square value for the model with at_bats: 0.3729
The R-square value is a measure of how well the independent variable(s) explain the variability in the dependent variable. In this context, it represents the proportion of variability in runs that can be explained by hits (or at_bats).
Comparing the R-square values:
The model with hits (R2=0.6419) has a higher R2 value compared to the model with at_bats (R2=0.3729).
A higher R2 value indicates that a larger proportion of the variability in runs is explained by the model.
Therefore, based on the...