Assingment

7) 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?

Options for 7 th question and instructions given for uploading a file for 7 th question in the assignment document sent by student There is no way to tell from these results It made the model fit better Did not change it at all It made the model fit worse

17)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

Submit your Excel file with all data cleaning and regression models where specified below.

IN 7.9 7,17Questions Take reference of assignment and CSV documents submitted

7)Disney is grossing more over time because the p-value is positive.

Disney is grossing less over time because the p-value is positive.

Disney is grossing more over time because the p-value is negative.

Disney is grossing less over time because the p-value is negative.

Disney is grossing more over time because the coefficient is positive.

Disney is grossing less over time because the coefficient is positive.

Disney is grossing more over time because the coefficient is negative.

Disney is grossing less over time because the coefficient is negative.

Bookmark question for later

Can we rely on these results and expect to see something similar as new movies are added?

Bookmark question for later

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?

Options:

1There is no way to tell from these results

2It made the model fit better

3Did not change it at all

4It made the model fit worse

17)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.

INSTRUCTIONS:

Follow all the tasks shown in the video clips in Ch 10 and do them yourself while watching the clips.

At the End, Turn in the File that You Produce While Watching the

Author's Demonstration.

DELIVERABLES:

We must watch the machine learning videos and follow the steps.