Statistical Inference3.1 MethodsThe components of our multiple linear regression model are the following:Numerical outcome variable y = Review RatingNumerical explanatory variable x1 = Movie BudgetCategorical explanatory variable x2 = Movie Rating3.2 Model Results Table 2. Regression table of linear model of horror movies: (SEE IMAGE ATTACHED)Interpret the regression table A lippie.terminterceptbudgetmovie_rating: NOT RATEDmovie_rating: PGmovie_rating: PG-13movie_rating: Rmovie_rating: TV-14movie_rating: TV-MAmovie_rating: UNRATED9 rows | 1-4 of 7 columnsestimate8.1000.000-3.544-2.342• -3.124-3.091-3.735-3.709-3.320std_error1.3110.0001.3141.5321.3261.3151.3751.3331.334statistic6.1784.394-2.697-1.528-2.356-2.350-2.716-2.783-2.488F

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 3.2 Model Results Table 2. Regression table of linear model of horror movies: (SEE IMAGE ATTACHED) Interpret the regression table

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 3.2 Model Results Table 2. Regression table of linear model of horror movies: (SEE IMAGE ATTACHED) Interpret the regression table

Holt Mcdougal Larson Pre-algebra: Student Edition 2012

1st Edition

ISBN:9780547587776

Author:HOLT MCDOUGAL

Publisher:HOLT MCDOUGAL

Chapter12: Angle Relationships And Transformations

Section12.1: Angle Relationships

Problem 44E

See similar textbooks

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

3.2 Model Results

Table 2. Regression table of linear model of horror movies: (SEE IMAGE ATTACHED)

Interpret the regression table

A lippie.
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
F

Expert Solution

Step 1

Given information:

Numerical outcome variable y = Review Rating

Numerical explanatory variable x₁ = Movie Budget

Categorical explanatory variable x₂ = Movie Rating

term	estimate	std_error	Statistic
intercept	8.1	1.311	6.178
budget	0	0	4.394
movie_rating: NOT RATED	-3.544	1.314	-2.697
movie_rating: PG	-2.342	1.532	-1.528
movie_rating: PG-13	-3.124	1.326	-2.356
movie_rating: R	-3.091	1.315	-2.35
movie_rating: TV-14	-3.735	1.375	-2.716
movie_rating: TV-MA	-3.709	1.333	-2.783
movie_rating: UNRATED	-3.32	1.334	-2.488

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