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The components of our multiple linear regression model are the following: Numerical outcome variable y = Review Rating Numerical explanatory variable x1 = Movie Budget Categorical explanatory variable x2 = Movie Rating (SEE ATTACHED REGRESSION TABLE TO ANSWER 3.4

The components of our multiple linear regression model are the following: Numerical outcome variable y = Review Rating Numerical explanatory variable x1 = Movie Budget Categorical explanatory variable x2 = Movie Rating (SEE ATTACHED REGRESSION TABLE TO ANSWER 3.4

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
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Statistical Inference

3.1 Methods

The components of our multiple linear regression model are the following:

  • Numerical outcome variable y = Review Rating
  • Numerical explanatory variable x1 = Movie Budget
  • Categorical explanatory variable x2 = Movie Rating

(SEE ATTACHED REGRESSION TABLE TO ANSWER 3.4)

3.4 Inference for multiple regression

Using the output of our regression table we’ll test the null hypothesis for our numerical explanatory variable. 

A trople. 3x7 term <chr> intercept budget movie_rating: NOT RATED movie_rating: PG movie_rating: PG-13 movie_rating: R movie_rating: TV-14 movie_rating: TV-MA movie_rating: UNRATED 9 rows | 1-4 of 7 columns estimate <dbl> 8.100 0.000 -3.544 -2.342 -3.124 -3.091 -3.735 -3.709 -3.320 std_error <dbl> 1.311 0.000 1.314 1.532 1.326 1.315 1.375 1.333 1.334 statistic <dbl> 6.178 4.394 -2.697 -1.528 -2.356 -2.350 -2.716 -2.783 -2.488 1 F
Transcribed Image Text:A trople. 3x7 term <chr> intercept budget movie_rating: NOT RATED movie_rating: PG movie_rating: PG-13 movie_rating: R movie_rating: TV-14 movie_rating: TV-MA movie_rating: UNRATED 9 rows | 1-4 of 7 columns estimate <dbl> 8.100 0.000 -3.544 -2.342 -3.124 -3.091 -3.735 -3.709 -3.320 std_error <dbl> 1.311 0.000 1.314 1.532 1.326 1.315 1.375 1.333 1.334 statistic <dbl> 6.178 4.394 -2.697 -1.528 -2.356 -2.350 -2.716 -2.783 -2.488 1 F
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