Movies
Josh Finkelstein
John Hottinger
Jenny Yaillen
Xiang Huang
Edward Han
Rory MacDonald
Tyronne Martin
Intro
• For an economist the study of econometrics, or the statistical
analyzing of economic data, is extremely important in
understanding economic phenomena. By analyzing sets of
past economic data, economists hope to be able to discover
trends and tendencies that can be used to predict future
economic events with greater accuracy. Cinema has strongly
influenced society since its inception, not only socially but
economically. It is not uncommon for modern movies to be
produced at the cost of tens of million dollars, and to achieve
gross profits many times that. With figures as impressive as
these it is clear that movies make a discernable impact on the
economy. A clearer understanding of what factors influence
the gross profit generated by movies is vital to predicting the
impact they will have on the economy.
What we are studying?
• For this study fifty high popularity movies
created within the past sixty years were
selected to be analyzed. The aspects of the
movies that were studied were the rating,
budget, domestic gross, length, viewer score,
critic score and profit. These variables were
then regressed using statistical analysis to
determine important relationships between
variables, and their relation to the revenue
generated by the movies.
Variables
– Rating (R, PG-13, PG, G)
– Production Budget
– Gross (domestically only) www.the-numbers.com
www.rottentomatoes.com
– Length
– Viewer Rating (Rotten Tomatoes)
– Critic Rating (Rotten Tomatoes)
– Profit (Gross-Budget)
Why we are studying it?
• Study past economic data to predict future
events
• Better understand factors that influence
economic impact of movies
• Understanding of past movies allow us to
predict gross/budget of future movies
On average, what type of movie (ie
R, PG-13, PG) has the highest gross
and budget (in millions)???
Average Gross & Budget by Rating
32.343125
R
51.41
Rating
Budget
114.375
Gross
PG-13
138.62
70.125
PG
209.18
0
50
100
150
Budget & Gross (in millions)
200
250
Does there seem to be a trend in what
type of movies are being produced?
Gone with the Wind
Jaws
Grease
Halloween
Raiders of the Lost Ark
Terminator
Aliens
1939 G
1975 PG
1978 PG
1978 R
1981 PG
1984 R
1986 R
Indiana Jones and the Last Crusade
Ghost
Schindler's List
Forrest Gump
Pulp Fiction
True Lies
Braveheart
The American President
Executive Decision
Independence Day
Multiplicity
Scream
As Good As It Gets
Chasing Amy
Contact
Dante's Peak
Good Will Hunting
1989 PG-13
1990 PG-13
1993 R
1994 PG-13
1994 R
1994 R
1995 R
1995 PG-13
1996 R
1996 PG-13
1996 PG-13
1996 R
1997 PG-13
1997 R
1997 PG
1997 PG-13
1997 R
I Know What You Did Last Summer
Men in Black
Speed 2:Cruise Control
The Fifth Element
The Game
Titanic
Volcano
Armageddon
Deep Impact
Hard Rain
Saving Private ryan
The Man in the Iron Mask
1997 R
1997 PG-13
1997 PG-13
1997 PG-13
1997 R
1997 PG-13
1997 PG-13
1998 PG-13
1998 PG-13
1998 R
1998 R
1998 PG-13
Star Wars Ep. I: The Phantom Menace
How the Grinch Stole Christmas
1999 PG
2000 PG
Harry Potter and the Sorcerer's Stone
Spider-Man
The Lord of the Rings: The Return of
the King
Shrek 2
Star Wars Ep. III: Revenge of the Sith
Pirates of the Caribbean: Dead Man's
Chest
Spider-Man 3
The Dark Knight
Avatar
Paranormal Activity
Toy Story 3
Robin Hood
2001 PG
2002 PG-13
2003 PG-13
2004 PG
2005 PG-13
2006 PG-13
2007 PG-13
2008 PG-13
2009 PG-13
2009 R
2010 G
2010 PG-13
800
16
3400
14
2900
400
Movies (#)
Total Gross
Avg Gross
12
200
Number of Movies
GROSS
600
10
0
0
20
40
60
CRIT ICRAT ING
80
100
2400
1900
8
1400
6
900
4
2
400
0
-100
0
10
20
30
40
50
60
Critic Rating
70
80
90
Does Critic Rating Effect Gross?
100
Total Gross for all Movies ($Million)
Histogram of Critic Ratings for Movies
Does Critic Rating Effect
Gross?
Dependent Variable: LNGROSS
Took log
Method: Least Squares
Date: 11/27/10 Time: 15:02
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CRITICRATING
0.010266
0.004614
2.224831
0.0308
C
4.248005
0.339487
12.51302
0.0000
R-squared
0.093482
Mean dependent var
4.946064
Adjusted R-squared
0.074596
S.D. dependent var
0.952939
S.E. of regression
0.916708
Akaike info criterion
2.703121
Sum squared resid
40.33693
Schwarz criterion
2.779602
F-statistic
4.949874
Prob(F-statistic)
0.030828
Log likelihood
Durbin-Watson stat
-65.57803
1.133514
Is There a Relationship Between
Viewer Rating and Gross?
Dependent Variable: LNGROSS
Method: Least Squares
Date: 11/27/10 Time: 15:08
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
VIEWERRATING
0.017814
0.007589
2.347496
0.0231
C
3.632812
0.574099
6.327852
0.0000
R-squared
0.102984
Mean dependent var
4.946064
Adjusted R-squared
0.084296
S.D. dependent var
0.952939
S.E. of regression
0.911891
Akaike info criterion
2.692585
Sum squared resid
39.91414
Schwarz criterion
2.769066
F-statistic
5.510737
Prob(F-statistic)
0.023071
Log likelihood
Durbin-Watson stat
-65.31462
1.067283
Is there a relationship between the
length of a movie and it’s gross??
Dependent Variable: LNGROSS
Method: Least Squares
Date: 11/27/10 Time: 15:06
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LENGTH
0.011416
0.004302
2.653518
0.0108
C
3.432083
0.584553
5.871292
0.0000
R-squared
0.127925
Mean dependent var
4.946064
Adjusted R-squared
0.109757
S.D. dependent var
0.952939
S.E. of regression
0.899124
Akaike info criterion
2.664386
Sum squared resid
38.80433
Schwarz criterion
2.740867
F-statistic
7.041160
Prob(F-statistic)
0.010770
Log likelihood
Durbin-Watson stat
-64.60965
1.113324
Is there a relationship
between critic and viewer
ratings?
Dependent Variable: CRITICRATING
Method: Least Squares
Date: 11/24/10 Time: 00:05
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
VIEWERRATING
1.209273
0.162735
7.430955
0.0000
C
-21.14761
12.31142
-1.717723
0.0923
R-squared
0.534970
Mean dependent var
68.00000
Adjusted R-squared
0.525282
S.D. dependent var
28.38223
S.E. of regression
19.55530
Akaike info criterion
8.823548
Sum squared resid
18355.67
Schwarz criterion
8.900029
F-statistic
55.21909
Prob(F-statistic)
0.000000
Log likelihood
Durbin-Watson stat
-218.5887
2.155193
Is there a relationship between profit
and viewer rating?
Dependent Variable: PROFIT
Method: Least Squares
PROFIT = 3.00441646*VIEWERRATING - 96.84122145
Date: 11/24/10 Time: 00:01
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
VIEWERRATING
3.004416
1.051507
2.857247
0.0063
C
-96.84122
79.55012
-1.217361
0.2294
R-squared
0.145358
Mean dependent var
124.6444
Adjusted R-squared
0.127553
S.D. dependent var
135.2781
S.E. of regression
126.3564
Akaike info criterion
12.55527
Sum squared resid
766364.5
Schwarz criterion
12.63175
F-statistic
8.163863
Prob(F-statistic)
0.006300
Log likelihood
Durbin-Watson stat
-311.8817
1.200674
Is there a
relationship
between the
profit of a movie
(gross-budget)
and the rating
received from
critics?
Dependent Variable: PROFIT
Method: Least Squares
Date: 11/23/10 Time: 23:59
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CRITICRATING
2.232723
0.607806
3.673416
0.0006
C
-27.18079
44.71995
-0.607800
0.5462
R-squared
0.219436
Mean dependent var
124.6444
Adjusted R-squared
0.203174
S.D. dependent var
135.2781
S.E. of regression
120.7561
Akaike info criterion
12.46460
Sum squared resid
699938.2
Schwarz criterion
12.54108
F-statistic
13.49399
Prob(F-statistic)
0.000602
Log likelihood
Durbin-Watson stat
-309.6150
1.219835
Dependent Variable: PROFIT
Method: Least Squares
Date: 11/27/10 Time: 15:18
Sample: 1 50
DROP CONSTANT
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CRITICRATING
1.891296
0.230608
8.201334
0.0000
R-squared
0.213428
Mean dependent var
124.6444
Adjusted R-squared
0.213428
S.D. dependent var
135.2781
S.E. of regression
119.9766
Akaike info criterion
12.43227
Sum squared resid
705325.2
Schwarz criterion
12.47051
Durbin-Watson stat
1.188816
Log likelihood
-309.8067
Is there relationship between how
much a movie makes (profit=grossbudget) and it’s length?
Dependent Variable: PROFIT
Method: Least Squares
PROFIT = 1.299759485*LENGTH - 47.7297429
Date: 11/24/10 Time: 00:13
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LENGTH
1.299759
0.626510
2.074603
0.0434
C
-47.72974
85.12612
-0.560694
0.5776
R-squared
0.082288
Mean dependent var
124.6444
Adjusted R-squared
0.063169
S.D. dependent var
135.2781
S.E. of regression
130.9357
Akaike info criterion
12.62647
Sum squared resid
822920.0
Schwarz criterion
12.70295
F-statistic
4.303979
Prob(F-statistic)
0.043408
Log likelihood
Durbin-Watson stat
-313.6617
1.239945
Dependent Variable: PROFIT
Method: Least Squares
Date: 11/27/10 Time: 15:21
DROPPED THE CONSTANT
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LENGTH
0.956890
0.135325
7.071047
0.0000
R-squared
0.076277
Mean dependent var
124.6444
Adjusted R-squared
0.076277
S.D. dependent var
135.2781
S.E. of regression
130.0165
Akaike info criterion
12.59300
Sum squared resid
828309.8
Schwarz criterion
12.63124
Durbin-Watson stat
1.205902
Log likelihood
-313.8249
Making lngross regression more
significant…
Dependent Variable: LNGROSS
Dependent Variable: LNGROSS
Method: Least Squares
Method: Least Squares
Date: 11/27/10 Time: 15:09
Date: 11/30/10 Time: 11:46
Sample: 1 50
Sample: 1 50
Included observations: 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CRITICRATING
0.006765
0.006616
1.022541
0.3119
VIEWERRATING
0.002967
0.011707
0.253405
0.8011
LENGTH
0.009267
0.004711
1.967141
0.0552
C
3.038402
0.678472
4.478300
0.0000
R-squared
0.182595
Mean dependent var
Variable
Coefficient
Std. Error
t-Statistic
Prob.
CRITICRATING
0.007972
0.004547
1.753158
0.0861
LENGTH
0.009715
0.004322
2.247498
0.0293
C
3.115627
0.600113
5.191738
0.0000
R-squared
0.181454
Mean dependent var
4.946064
Adjusted R-squared
0.146622
S.D. dependent var
0.952939
S.E. of regression
0.880310
Akaike info criterion
2.641040
36.42248
Schwarz criterion
2.755762
F-statistic
5.209447
Prob(F-statistic)
0.009047
4.946064
Adjusted R-squared
0.129286
S.D. dependent var
0.952939
S.E. of regression
0.889207
Akaike info criterion
2.679645
Sum squared resid
Sum squared resid
36.37171
Schwarz criterion
2.832607
Log likelihood
F-statistic
3.425221
Prob(F-statistic)
0.024724
Durbin-Watson stat
Log likelihood
Durbin-Watson stat
-62.99113
1.110069
-63.02601
1.123581
Final lngross regression…..
Dependent Variable: LNGROSS
Method: Least Squares
Date: 11/30/10 Time: 11:39
Sample: 1 50
Included observations: 50
Variable
Coefficient
Std. Error
t-Statistic
Prob.
R
-1.192232
0.218347
-5.460266
0.0000
LENGTH
0.006888
0.003442
2.000871
0.0513
CRITICRATING
0.013170
0.003704
3.555065
0.0009
C
3.518552
0.478231
7.357425
0.0000
R-squared
0.503352
Mean dependent var
4.946064
Adjusted R-squared
0.470962
S.D. dependent var
0.952939
S.E. of regression
0.693120
Akaike info criterion
2.181392
Sum squared resid
22.09913
Schwarz criterion
2.334354
F-statistic
15.54032
Prob(F-statistic)
0.000000
Log likelihood
Durbin-Watson stat
-50.53480
1.552562
Average Gross & Budget by Rating
32.343125
R
51.41
Rating
Budget
114.375
Gross
PG-13
138.62
70.125
PG
209.18
0
50
100
150
Budget & Gross (in millions)
200
250
Conclusion
•
•
•
•
•
The lower the rating (R, PG-13)=higher gross
Higher critic rating=higher gross/profit
Higher viewer rating=higher gross/profit
Critic rating and viewer rating=correlated
By taking some of the most significant
relationships we found we were able to create
our final significant and correlated lngross
regression