Hello, I have R programming questions.

I need your help.

Thank you so much.

1. Download and save the AdmissionsPredz'ctcsv dataset and read it into R. This data set consists of data regarding international students who are applying for graduate programs in English speaking countries. The data set contains 7 variables: GRE (Graduate Record Examination score) TOEFL (Test of English as a Foreign Language score) University Rating (score out of 5) SOP (Statement of Purpose, score out of 5) LOB (Letter of Recommendation, score out of 5) UGPA (Undegraduate Grade Point Average, score out of 10) Chance of Admit (value between 0 and 1) The response variable is y = Chance of Admit. All other variables are possible explana tory variables. Create a vector called racemes which contains the names of each of the explanatory variables. Use the command par(mfrow 2 C(2, 3)) and then write a forloop which plots each of the explanatory variables against the response variable. Make sure each plot has an xaxis title and a yaxis title. Use racemes from part (a) to create the xaxis title. (c) For which explanatory variables are you able to identify the form of the relationship with 3;? What is the form that you see? (d) Create a linear regression model called fuleodel which includes all of the possible explanatory variables. Write out the model that you obtain. Example: 9 = 0.3 + 0.1(1131) 0.5(m2) + (e) Are all terms signicant? Identify any variables which should be removed from the model (and show the code that you are using to make this decision). (f) Create a new model called new_m0del which contains only the terms which were signif icant in the full model. Write out the model that you obtain. (g) What is the range of values for each variable included in the newjnodel that we can use for prediction (so that we avoid extrapolation). (h) Consider a student with a GRE score of 320, a TOEFL score of 101, applying to a University with a rating of 4, with a SOP score of 3, a LOR score of 4 and an under graduate GPA of 8.4. Use your model from part (f) to predict this students chance of being accepted to the graduate program at their University of choice. 2. Type in the following vectors which represent people of various ages (in years) who are each timed (in seconds) running the same distance. age = c(2,3,4,5,8,11,14,17,21,28,38,5@,67,83) speed = C(65,58,4@,3?,32,26,18,16,17,17,23,29,42,59) (a) Which is the response variable and which is the explanatory variable? (b) Plot the variables (you do not need any titles). What form does the relationship seem to have? ((3) Fit a model to the form that you identied in part (b). Write out the model that you obtain. ((1) Use your model to predict how long it would take for a 70 year old to run that distance. (6) What percentage of the variation in the response variable can be explained by the vari ation in the explanatory variables in the model?