3. 2 1 ACTION!!! Introduction We explored the top 100 movies in the last 10 years in terms of gross...

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Transcript of 3. 2 1 ACTION!!! Introduction We explored the top 100 movies in the last 10 years in terms of gross...

3

2

1

ACTION!!!

Introduction

• We explored the top 100 movies in the last 10 years in terms of gross revenues

• We investigated why certain movies have high opening weekend revenues

• We also examined how certain aspects influenced sales

DO YOU KNOW WHICH MOVIE DID THE BEST?????

TITANIC!!!!

Genre Share for top 100 movies (in percent %)

Action, 20.00%

Adventure, 9.00%

Comedy, 20.00%Drama, 14.00%

Family, 18.00%

Horror, 2.00%

Indie, 1.00%

Romance, 4.00%

Sci-Fi, 12.00%

Genre Share for top 10 movies (in percent %)

Adventure, 10.00%

Comedy, 10.00%

Sci-Fi, 20.00% Action, 20.00%

Family, 20.00%

Drama, 20.00%

Exploratory Data Analysis

Rating Share top 100 movies (in percent %)

R, 17.00% G, 7.00%

PG, 22.00%

PG-13, 54.00%

G

PG

PG-13

R

Rating Share top 10 movies (in percent %)

PG-13, 50.00%

PG, 30.00%

R, 10.00%G, 10.00%

G

PG

PG-13

R

Distribution of Opening Weekend Revenues

0

5

10

15

20

25

30

35

Opening Weekend Revenues

Num

ber

of M

ovie

s

0.00%

50.00%

100.00%

BoxPlot

$597,362 $20,597,362 $40,597,362 $60,597,362 $80,597,362 $100,597,362 $120,597,362

My Big Fat Greek Wedding

Spiderman 2

Median: American Pie 2 & Meet the Fockers

Distribution of Gross Revenue

0

5

10

15

20

25

30

35

40

45

50

Total Revenues

Nu

mb

er

of

Mo

vie

s

.00%

50.00%

100.00%

Gross

$- $100,000,000

$200,000,000

$300,000,000

$400,000,000

$500,000,000

$600,000,000

Dinosaurs

Titanic

Median: Ocean’s Eleven & What Women Want

Distribution of Budget

0

5

10

15

20

25

30

Budget

Nu

mb

er o

f M

ovie

s

.00%

50.00%

100.00%

Budget

$- $50,000,000 $100,000,000 $150,000,000 $200,000,000

The Blair Witch Project

Median: Ocean’s Eleven

Titanic &

Spiderman 2

Number of Screens

108 1108 2108 3108 4108

Number of Screens Distribution

0

10

20

30

40

50

60

Number of Screens

Nu

mb

er

of

Mo

vie

s

.00%

50.00%

100.00%

My Big Fat Greek Wedding Shrek 2

My Big Fat Greek Wedding

Good Will Hunting

Passion of Christ

Beautiful Mind

The Blair Witch Project

Good Will Hunting

Rate of Return for all data

0 500 1000 1500 2000 2500 3000 3500 4000

Rate of return after elmination of four largest outliers

0.859144 2.859144 4.859144 6.859144 8.859144

Distribution of Rate of Returns

0

5

10

15

20

25

30

35

Rate of Returns

Nu

mb

er

of

Mo

vie

s

.00%

50.00%

100.00%

Terminator 3: Rise of Machines

The Blair Witch Project

My Big Fat Greek Wedding

Good Will Hunting

Passion of Christ

There’s Something About Mary

Scary Movie

The Water Boy

AP: The Spy Who.

Shrek 2

Gross Yearly Revenues for the last 10 years

y = 2E+08x - 4E+11

R 2 = 0.502

0

500000000

1000000000

1500000000

2000000000

2500000000

3000000000

3500000000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

YEARS

RE

VE

NU

ES

Revenues per Movie for the Last 10 Years

0

50000000

100000000

150000000

200000000

250000000

300000000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

YEARS

RE

VE

NU

E P

ER

MO

VIE

Statistical Analysis1. Data Overview

Opening Weekend v Screens

0

500

1000

1500

2000

2500

3000

3500

4000

4500

$- $20,000,000 $40,000,000 $60,000,000 $80,000,000 $100,000,000 $120,000,000 $140,000,000

Opening Weekend

Continue…

Gross v Opening Weekend

$-

$100,000,000

$200,000,000

$300,000,000

$400,000,000

$500,000,000

$600,000,000

$700,000,000

$- $20,000,000 $40,000,000 $60,000,000 $80,000,000 $100,000,000 $120,000,000 $140,000,000

Opening Weekend

Continue…

Opening Weekend vs. Budget

0

50000000

100000000

150000000

200000000

250000000

0 20000000 40000000 60000000 80000000 100000000 120000000 140000000

Opening Weekend

Regressions

Opening Weekend Rev vs. Screens

$-

$20

,000

,000

$40

,000

,000

$60

,000

,000

$80

,000

,000

$10

0,000

,000

$12

0,000

,000

$14

0,000

,000

050

010

0015

0020

0025

0030

0035

0040

0045

00

Screens

Op

en

We

eke

nd

Open Weekend

Predicted Open Weekend

Summary for Opening Weekend v. Screens

Regression StatisticsMultiple R 0.93569579R Square 0.87552661Adj R Square 0.8654256Standard Error 19690500.9Observations 100

ANOVAdf SS MS F

Regression 1 2.7E+17 2.7E+17 696.3507Residual 99 3.84E+16 3.88E+14Total 100 3.08E+17

Coefficients Std Error t Stat P-valueIntercept 0 #N/A #N/A #N/AScreens 16325.9471 618.6776 26.38846 1.37E-46

Further regressions:

Opening Weekend (sqrt) vs Screens

0

2000

4000

6000

8000

10000

12000

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Screens

sqrt

(ope

n w

kend

)

sqrt(open wkend)

Predicted sqrt(open wkend)

Summary of Opening Weekend (Sqrt) vs. Screens

Regression StatisticsMultiple R 0.98283686R Square 0.96596829Adj R Square 0.95586728Standard Error 1309.20978Observations 100

ANOVAdf SS MS F

Regression 1 4.82E+09 4.82E+09 2810.052Residual 99 1.7E+08 1714030Total 100 4.99E+09

Coefficients Std Error t Stat P-valueIntercept 0 #N/A #N/A #N/AScreens 2.18059006 0.041136 53.00992 1.73E-74

Continued…

Opening Weekend vs Budget

$0

$20,000,000

$40,000,000

$60,000,000

$80,000,000

$100,000,000

$120,000,000

$140,000,000

$0 $50,000,000 $100,000,000 $150,000,000 $200,000,000 $250,000,000

Budget (est)

Op

en

We

eke

nd

Open Weekend

Predicted Open Weekend

Summary for Opening Weekend vs. Budget

Regression StatisticsMultiple R 0.44671158R Square 0.19955123Adj R Square 0.19129918Std Error 22115568.2Observations 99

ANOVAdf SS MS F

Regression 1 1.18E+16 1.18E+16 24.18202Residual 97 4.74E+16 4.89E+14Total 98 5.93E+16

Coefficients Std Error t Stat P-valueIntercept 26594357.5 5268121 5.048168 2.09E-06Budget (est) 0.27039426 0.054986 4.917522 3.57E-06

•Gross Revenues are highly dependent upon the amount of time movies are in theaters.

Time in Theaters vs Gross Revenues (sqrt)

0

5000

10000

15000

20000

25000

30000

0 50 100 150 200 250 300 350 400

Time in Theaters (in days)

Sqr

t (G

ross

)

sqrt(gross)

Predicted sqrt(gross)

More…

Conclusion• As you can see, the revenues relate to:

– Number of screens – Advertisement– Demographics– Word of mouth

• Distribution looks pretty normal• The top 100 movies were released in

summer and winter months

That’s all folks!!!