Simple Regression Analysis The purpose of this assignment is to apply simple regression concepts, interpret simple regression analysis models, and justify business predictions based upon the analysis.For this assignment, you will use the "Trucks" dataset. You will use SPSS to analyze the dataset and address the questions presented. Findings should be presented in a Word document along with the SPSS outputs.The business characteristics of n = 250 U.S. trucking and delivery companies for calendar year 2011 were recorded. Among the characteristics studied were the number of drivers and the number of trucks (power units) each company employed.Part 1:Given that the data consists of counts and range of counts is large, a natural log transformation is usually performed to improve the linear model results. Apply a natural log transform to both variables and then plot the Y = log(Trucks) vs. X = log(Drivers).Is there a linear relationship? Justify your answer by providing the SPSS output as an illustration.Part 2:Build a simple linear model by regressing Y on X and testing whether or not a relationship exists between the number of drivers and the number of trucks. Address the following questions in your written response:After fitting the model, plot the standardized residuals (on vertical axis) vs. the standardize predictions (on horizontal axis). Is there a pattern? How would you interpret the pattern or lack of pattern?After fitting the model, derive the normal probability plot and interpret what the plot means.What is the coefficient of determination, R2, of the model? How would you interpret the R2?What is the estimate of β1? How would you interpret the estimate of β1?Is the estimate of β1 significantly different than 0? Assume an α = 0.01.What is a 95% confidence interval for β1? How would you interpret the 95% confidence interval for β1?If a new trucking and delivery company with 4,900 drivers were to be formed, how many trucks would you expect the company would employ based on the model?APA format is not required, but solid academic writing is expected.This assignment uses a grading rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. Please include ... Discussion of the linear relationship and supporting SPSS output chart are complete and correct,Answers to simple regression analysis questions and supporting SPSS output charts are complete and accurate, and Writer is clearly in command of standard, written, academic English. Also please answer these 2 questions... QUESTION 1 - Suppose you wanted to understand the relationship between a customer's yearly income (X) and the number of movies (Y) the customer watched in a year. You then gather data on incomes and the number of movies watched in a year. The range of incomes in your data set is $5K to $150K. After fitting a simple linear model and performing all the appropriate diagnostics, the model showed that, on average, for every $10K in income, the customer watched 1.5 movies in the year. So, for example, if a customer earned 60K in a year, he or she would be expected to watch nine movies during the year. Now you want to apply this model to your very wealthy friend who will earn $1 million in the next year. Is this an appropriate application of your model? Why or why not? Provide specific examples to justify your opinion. QUESTION 2- If you regress daily high temperature (Y) on the amount of ice cream sales (X), you will notice that there is a strong positive correlation between the two. In other words, as daily ice cream sales increase, the daily high temperature increases. This implies that if we knew the amount of ice cream sales in a particular day, we could estimate, with a high level of accuracy, the high temperature in that day. Does this mean that if we wanted to increase the daily temperature, we need to sell more ice cream? Explain why or why not?

I am on Covid duty in hospital and have no time to work on this. I will need answers to all the questions and report as mentioned in this document. You will need good knowledge about R programming/R Studio. I have attached the document which has all the details. Everything should be done in R studio and I need rmd file. Questions welcome. Thank youYou are given a single combined file of “approved” loans data from six years, which are supposedly the pre and post periods of the controversy. Step 1: The first step is to create two new columns as follows:a)Comb_Risk_One: Create a binary column by combining categories A and B (Low Risk) into one category and all the remaining categories in another (High Risk).b)Comb_Risk_Two: Create a binary column by combining categories A, B and C (Low Risk) into one category and all the remaining categories in another (High Risk).Now, break the file into two files filtering out data for 2012, 13, and 14 in one file and 2015, 16 and 17 in another file.Step 2: Each loan is graded (A to G) based on the risk, with A being least risky and G being the highest risk category. You are asked to predict Low and High-risk categories (for the two new response variables) using various modeling techniques like Naïve Bayes’, KNN, Logistic Regression, and CART model. Make sure to look for the following:a.Outliers based on the independent columns (predictors)b.Multicollinearityc.Scaling and standardization of the predictorsd.Train-Test split for both files and compare the confusion matrices on the Test.Step 3: Produce a “well documented and explained” R Markdown knit file analyzing the data with findings on the model with the highest classification ability. Also describe the features of the categories that are not classified correctly. Create a confusion matrix to answer the last question and run descriptive statistics on the misclassified categories. Provide any necessary EDA and visuals to enhance understanding of your analysis.Refer to the document attached. Refer to the datafile here: https://www.sendspace.com/file/csi8r1