Suppose you are an operations manager for a multiplex in a metro city. You noticed that a minimum few seats always went unsold during weekdays.

To understand the reason behind this, you collected data to predict the number of empty seats based on variables -- the duration of the movie, prices of tickets, cost of refreshments, number of songs, number of known actors, BookMyShow ratings, hit films by director, etc. The computer output of the multiple linear regression is

Number of empty seats(Y):Total number of empty seats during weekdays Movie length(X1):The duration of a movie in minutes Ticket_prices(X2):The average ticket price per seat in rupees Number_of_songs(X3):The total number of songs in the movie Cost_of_refreshments(X4):The average price of refreshments in rupees BookMyShow_ratings(X5):The rating of the movie in BookMyShow Number_of_known_actors(X6):The number of known actors in the movie Hit_films_by_director(X7):The number of hit films by the director of the movie.

Predictor	Coefficient	Std Err	Value	P-Value
Constant	26.271	12.856	2.035	0.038
Movie_length(X1)	-2.023	1.158	-1.673	0.234
Ticket_prices(X2)	2.448	1.071	2.285	0.024
Number_of_songs(X3)	0.674	0.976	0.694	0.492
Cost_of_refreshments(X4)	1.277	0.987	1.283	0.371
BookMyShow_ratings(X5)	-0.584	0.9588	-0.5812	0.037
Number_of_known_actors(X6)	-0.634	0.876	-0.736	0.042
hit_films_by_director(X7)	-1.845	0.9347	-1.193	0.038

Overall significance test results: P-value is 0.002.

Summary measures: R-squared = 42.74% Adjusted R-squared = 39.23% Standard error of estimate = 9.7384

Answer the following questions based on the information given in the comprehension above. You need not use any tool or software. The information required to answer the questions is provided in the comprehension.

Hypothesis

In the multiple linear regression, comment on the null hypothesis of the overall significance hypothesis test at a 99 percent confidence level.

Fail to reject

Reject

Accept

Can't comment at a 99 percent confidence level