Please complete the exercise 7.9 "Excel MLR Practice: Disney Movie Revenue" found https://app.myeducator.com/reader/web/1382fc/chp07/kz4jl/ for Chapter 7 assignment, but in R instead of Excel. To do this, download the file disney_movies_total_gross.csv from

the book under Resource Files and load it into RStudio. As you work on each question from the book, post your answer in the book. Here are the steps in more detail: 1. Download the file disney_movies_total_gross.csv from the book under Resource Files. Open RStudio and load the data file. 2. Once the data file is loaded, you can start working on the questions in the exercise. 3. To answer each question, write R codes to perform the necessary tasks and then post your answer in the book. As you work on the assignment, please take a look at the lecture (5 minutes long, link provided below) on how to interpret the coefficients for categorical variables with more than two levels in regression models. Please log into MyJSU before clicking on the link. https://www.linkedin.com/learning/introduction-to-stata-15/categorical-explanatory-variables-in- ols?u=36441276 The lectures I posted on Canvas explain how to interpret a model result mostly using continuous variables, except for the Gender variable, which has two levels (0/1). The lecture directed by the link above explains the output of a model with a categorical variable with more than four levels. This lecture will help you to interpret the coefficients of your model in the assignment./nDeliverables: 1. Please create a separate Word file that will include an interpretation of your model results for Question #7. Specifically, please interpret the results for the coefficients, Std. Error, t-value, p- value for each of the variables (days_since_release, mpaa_rating) produced in R. My lectures posted on Canvas can help you with the answers to this question. 2. For question 17, upload your R file to Canvas, with your code separated by question number and with the question number included before the code for that question. 1/n7) Fill in all missing values of mpaa_rating with the value "Empty" Create dummy code features to convert mpaa_rating to sets of numeric 0/1 features for all values except "Empty" Run another MLR a few rows below the previous one using both days_since_release and all of the dummy codes you just created for mpaa_rating What did the inclusion of these dummy coded mpaa_rating features do to the model fit?/n17)Upload the Excel file containing the data and all of your regression models 7,17questions and read the instructions for both questions in assignment document. 7,17 give above are questions for above mentioned exercise 7.9 Excel MLR Practice: Disney Movie Revenue Objective Create an MLR model to explain and predict the gross revenue of Disney movies from 1937 to 2016. Data Source Use the .csv file provided below, which includes 573 records with the following features: Labels total_gross: the actual gross revenue of the movie inflation_adjusted_gross: the gross revenue converted to account for inflation Features movie_title: the title of the film release_date: the first date it appeared in theaters genre: the type of file mpaa_rating: G, PG, PG-13, R, Not Rated, or null/empty Tasks Perform the steps included in each of the questions below and answer the associated questions. Deliverables