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Movies Josh Finkelstein John Hottinger Jenny Yaillen Xiang Huang Edward Han Rory MacDonald Tyronne Martin.

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Presentation on theme: "Movies Josh Finkelstein John Hottinger Jenny Yaillen Xiang Huang Edward Han Rory MacDonald Tyronne Martin."— Presentation transcript:

1 Movies Josh Finkelstein John Hottinger Jenny Yaillen Xiang Huang Edward Han Rory MacDonald Tyronne Martin

2 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.

3 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.

4 Variables – Rating (R, PG-13, PG, G) – Production Budget – Gross (domestically only) – Length – Viewer Rating (Rotten Tomatoes) – Critic Rating (Rotten Tomatoes) – Profit (Gross-Budget) www.the-numbers.com www.rottentomatoes.com

5 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

6 On average, what type of movie (ie R, PG-13, PG) has the highest gross and budget (in millions)???

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8 Does there seem to be a trend in what type of movies are being produced?

9 Gone with the Wind1939G Jaws1975PG Grease1978PG Halloween1978R Raiders of the Lost Ark1981PG Terminator1984R Aliens1986R Indiana Jones and the Last Crusade1989PG-13 Ghost1990PG-13 Schindler's List1993R Forrest Gump1994PG-13 Pulp Fiction1994R True Lies1994R Braveheart1995R The American President1995PG-13 Executive Decision1996R Independence Day1996PG-13 Multiplicity1996PG-13 Scream1996R As Good As It Gets1997PG-13 Chasing Amy1997R Contact1997PG Dante's Peak1997PG-13 Good Will Hunting1997R I Know What You Did Last Summer1997R Men in Black1997PG-13 Speed 2:Cruise Control1997PG-13 The Fifth Element1997PG-13 The Game1997R Titanic1997PG-13 Volcano1997PG-13 Armageddon1998PG-13 Deep Impact1998PG-13 Hard Rain1998R Saving Private ryan1998R The Man in the Iron Mask1998PG-13 Star Wars Ep. I: The Phantom Menace1999PG How the Grinch Stole Christmas2000PG Harry Potter and the Sorcerer's Stone2001PG Spider-Man2002PG-13 The Lord of the Rings: The Return of the King2003PG-13 Shrek 22004PG Star Wars Ep. III: Revenge of the Sith2005PG-13 Pirates of the Caribbean: Dead Man's Chest2006PG-13 Spider-Man 32007PG-13 The Dark Knight2008PG-13 Avatar2009PG-13 Paranormal Activity2009R Toy Story 32010G Robin Hood2010PG-13

10 Does Critic Rating Effect Gross?

11 Dependent Variable: LNGROSS Method: Least Squares Date: 11/27/10 Time: 15:02 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. CRITICRATING0.0102660.0046142.2248310.0308 C4.2480050.33948712.513020.0000 R-squared0.093482 Mean dependent var4.946064 Adjusted R-squared0.074596 S.D. dependent var0.952939 S.E. of regression0.916708 Akaike info criterion2.703121 Sum squared resid40.33693 Schwarz criterion2.779602 Log likelihood-65.57803 F-statistic4.949874 Durbin-Watson stat1.133514 Prob(F-statistic)0.030828 Took log

12 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 VariableCoefficientStd. Errort-StatisticProb. VIEWERRATING0.0178140.0075892.3474960.0231 C3.6328120.5740996.3278520.0000 R-squared0.102984 Mean dependent var4.946064 Adjusted R-squared0.084296 S.D. dependent var0.952939 S.E. of regression0.911891 Akaike info criterion2.692585 Sum squared resid39.91414 Schwarz criterion2.769066 Log likelihood-65.31462 F-statistic5.510737 Durbin-Watson stat1.067283 Prob(F-statistic)0.023071

13 Is there a relationship between the length of a movie and it’s gross??

14 Dependent Variable: LNGROSS Method: Least Squares Date: 11/27/10 Time: 15:06 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. LENGTH0.0114160.0043022.6535180.0108 C3.4320830.5845535.8712920.0000 R-squared0.127925 Mean dependent var4.946064 Adjusted R-squared0.109757 S.D. dependent var0.952939 S.E. of regression0.899124 Akaike info criterion2.664386 Sum squared resid38.80433 Schwarz criterion2.740867 Log likelihood-64.60965 F-statistic7.041160 Durbin-Watson stat1.113324 Prob(F-statistic)0.010770

15 Is there a relationship between critic and viewer ratings?

16 Dependent Variable: CRITICRATING Method: Least Squares Date: 11/24/10 Time: 00:05 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. VIEWERRATING1.2092730.1627357.4309550.0000 C-21.1476112.31142-1.7177230.0923 R-squared0.534970 Mean dependent var68.00000 Adjusted R-squared0.525282 S.D. dependent var28.38223 S.E. of regression19.55530 Akaike info criterion8.823548 Sum squared resid18355.67 Schwarz criterion8.900029 Log likelihood-218.5887 F-statistic55.21909 Durbin-Watson stat2.155193 Prob(F-statistic)0.000000

17 Is there a relationship between profit and viewer rating?

18 Dependent Variable: PROFIT Method: Least Squares Date: 11/24/10 Time: 00:01 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. VIEWERRATING3.0044161.0515072.8572470.0063 C-96.8412279.55012-1.2173610.2294 R-squared0.145358 Mean dependent var124.6444 Adjusted R-squared0.127553 S.D. dependent var135.2781 S.E. of regression126.3564 Akaike info criterion12.55527 Sum squared resid766364.5 Schwarz criterion12.63175 Log likelihood-311.8817 F-statistic8.163863 Durbin-Watson stat1.200674 Prob(F-statistic)0.006300 PROFIT = 3.00441646*VIEWERRATING - 96.84122145

19 Is there a relationship between the profit of a movie (gross-budget) and the rating received from critics?

20 Dependent Variable: PROFIT Method: Least Squares Date: 11/23/10 Time: 23:59 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. CRITICRATING2.2327230.6078063.6734160.0006 C-27.1807944.71995-0.6078000.5462 R-squared0.219436 Mean dependent var124.6444 Adjusted R-squared0.203174 S.D. dependent var135.2781 S.E. of regression120.7561 Akaike info criterion12.46460 Sum squared resid699938.2 Schwarz criterion12.54108 Log likelihood-309.6150 F-statistic13.49399 Durbin-Watson stat1.219835 Prob(F-statistic)0.000602

21 Dependent Variable: PROFIT Method: Least Squares Date: 11/27/10 Time: 15:18 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. CRITICRATING1.8912960.2306088.2013340.0000 R-squared0.213428 Mean dependent var124.6444 Adjusted R-squared0.213428 S.D. dependent var135.2781 S.E. of regression119.9766 Akaike info criterion12.43227 Sum squared resid705325.2 Schwarz criterion12.47051 Log likelihood-309.8067 Durbin-Watson stat1.188816 DROP CONSTANT

22 Is there relationship between how much a movie makes (profit=gross- budget) and it’s length?

23 Dependent Variable: PROFIT Method: Least Squares Date: 11/24/10 Time: 00:13 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. LENGTH1.2997590.6265102.0746030.0434 C-47.7297485.12612-0.5606940.5776 R-squared0.082288 Mean dependent var124.6444 Adjusted R-squared0.063169 S.D. dependent var135.2781 S.E. of regression130.9357 Akaike info criterion12.62647 Sum squared resid822920.0 Schwarz criterion12.70295 Log likelihood-313.6617 F-statistic4.303979 Durbin-Watson stat1.239945 Prob(F-statistic)0.043408 PROFIT = 1.299759485*LENGTH - 47.7297429

24 Dependent Variable: PROFIT Method: Least Squares Date: 11/27/10 Time: 15:21 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. LENGTH0.9568900.1353257.0710470.0000 R-squared0.076277 Mean dependent var124.6444 Adjusted R-squared0.076277 S.D. dependent var135.2781 S.E. of regression130.0165 Akaike info criterion12.59300 Sum squared resid828309.8 Schwarz criterion12.63124 Log likelihood-313.8249 Durbin-Watson stat1.205902 DROPPED THE CONSTANT

25 Making lngross regression more significant… Dependent Variable: LNGROSS Method: Least Squares Date: 11/27/10 Time: 15:09 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. CRITICRATING0.0067650.0066161.0225410.3119 VIEWERRATING0.0029670.0117070.2534050.8011 LENGTH0.0092670.0047111.9671410.0552 C3.0384020.6784724.4783000.0000 R-squared0.182595 Mean dependent var4.946064 Adjusted R-squared0.129286 S.D. dependent var0.952939 S.E. of regression0.889207 Akaike info criterion2.679645 Sum squared resid36.37171 Schwarz criterion2.832607 Log likelihood-62.99113 F-statistic3.425221 Durbin-Watson stat1.110069 Prob(F-statistic)0.024724 Dependent Variable: LNGROSS Method: Least Squares Date: 11/30/10 Time: 11:46 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. CRITICRATING0.0079720.0045471.7531580.0861 LENGTH0.0097150.0043222.2474980.0293 C3.1156270.6001135.1917380.0000 R-squared0.181454 Mean dependent var4.946064 Adjusted R-squared0.146622 S.D. dependent var0.952939 S.E. of regression0.880310 Akaike info criterion2.641040 Sum squared resid36.42248 Schwarz criterion2.755762 Log likelihood-63.02601 F-statistic5.209447 Durbin-Watson stat1.123581 Prob(F-statistic)0.009047

26 Dependent Variable: LNGROSS Method: Least Squares Date: 11/30/10 Time: 11:39 Sample: 1 50 Included observations: 50 VariableCoefficientStd. Errort-StatisticProb. R-1.1922320.218347-5.4602660.0000 LENGTH0.0068880.0034422.0008710.0513 CRITICRATING0.0131700.0037043.5550650.0009 C3.5185520.4782317.3574250.0000 R-squared0.503352 Mean dependent var4.946064 Adjusted R-squared0.470962 S.D. dependent var0.952939 S.E. of regression0.693120 Akaike info criterion2.181392 Sum squared resid22.09913 Schwarz criterion2.334354 Log likelihood-50.53480 F-statistic15.54032 Durbin-Watson stat1.552562 Prob(F-statistic)0.000000 Final lngross regression…..

27

28 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

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