Hi I need help with this statistics homework. We use the dataprograms R-Markdown and R-studio.

Stat 3022 Spring 2018 Regression Project {updated on 33, 10 am) Due : Monday, March 19'11 , 11:59 pm The data set 'movies' (available at http://usersstat.umn.eduf~parky/movies.csv) contains 128 movies released in the US. during 2009 that opened on more than 500 screens. The data set contains following variables Movie: the title of a movie in the data set T otal.Gross: Total domestic box ofce earning of the movie in millions of US. dollars Opening: Opening weekend box ofce earning of the movie in millions of US. dollars Screen: the number of screens on which the movie opened Budget: estimated production budget when reported in millions of US. dollars RT: the rating of the movie at the lm review website (rottentomatoescom) Action: 1 if the movie's genre is action, 0 otherwise. Comedy: 1 if the movie's genre is comedy, 0 otherwise. In this project, you are assigned to construct one simple linear regression model and multiple linear regression models. For each model, use the following variables. Sim I le Linear Re ession Multi I le Linear Re 9 ession TotalGross Opening Opening Screens, Budget, Action Response variable (Potential) Explanatory variable(s) 1. When you t and assess the model, answer the following questions: a) Simple linear regression model: Use Opening to predict Total Gross. Do either or both of the response variable and/or the explanatory variable need to be transformed? Include graphs/summary outputs to support your decision. Once you created the model, write the equation, interpret each coefcient and r-squared, and predict the total gross if a movie has the opening weekend box ofce earning of 50 million dollars. (5 pts: 1 for creating model correctly, 1 for appropriate decision on transformation and reason (with graphic/summary output statistic), l for checking assumptions, 1 for correct interpretation of-intereept and slope, and r-squared, 1 for correctly estimating the total gross value) b) Multiple Linear Regression model: i. Use the step() function (see Section 4.2 of the Lecture slide) to create the best, simplest model using the {Budget, Screens, Action} to Opening. Consider square terms but do not consider interaction terms. Do not transform variables. Once you create the model, use F-test to see if the overall model is signicant