SONY REGRESSION ANALYSIS
BY ADAM MANWARING, NELSON MOAK, GARRETT GRAHAM, BETHANY
PULIDO, AND HAMAIDU FADIKA OF DAT STATS THO. LLC
DESIGN THE STUDY
• Linear Regression model
Fill the 5 requirements:
-Linear Relationship
-Normal Error Term
-Constant Variance
-X’s are known constants (Assumed)
-Observations are Independent (Assumed)
COLLECT DATA
[CELLRANGE]
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$0
$500,000,000
$1,000,000,000
$1,500,000,000
$2,000,000,000
$2,500,000,000
$0 $50,000,000 $100,000,000 $150,000,000 $200,000,000 $250,000,000 $300,000,000 $350,000,000
WorldwideGross
Production Budget
Budget and Gross Correlation of Top Box Office Movies
DESCRIBE THE DATA
SCATTER PLOT OF RESIDUALS
QQ PLOT OF RESIDUALS
QQ Plot of Residuals
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 0.2 0.4 0.6 0.8 1 1.2 1.4
QQPlot
MAKE INFERENCE
• According to the data, we can be 95% confident that the first
movie that they have already spent $275,000,000 on should
gross about $916,466,146.33.
• If you want your next movie to gross $1,000,000,000, you
should plan to invest $281,769,512.54. You can be 95% certain
that if you invest that amount, you will gross $1Billion.
ACTION TO BE TAKEN
• According to the data, we can be 95% confident that the first
movie that they have already spent $275,000,000 on should
gross between $812,450,669 and $1,003,768,632.34.
• If you want your next movie to gross $1,000,000,000, you
should plan to invest $300,000,000 (at least $281,769,512.54).
This expense checked in the linear equation is estimated to
produce a gross of $1,005,192,394.
DAT STATS THO, LLC.
• Dat Stats Tho LLC is a business group with leading market
analysts Luke “Harrison Ford” Harris, Nelson “the Moak” Moak,
Garrett “The Action” Graham, Bethany “Lil Mama” Pulido,
Haimidu “Farquad” Fadika, and Adam “Big Tasty” Manwaring
• 1358 3rd West, Rexburg ID 83440
• Due to high phone call volume, email us at
getstattedup@yummystats.org
• Thank YOU!

Regression AnalysisFinal ppt

Editor's Notes

  • #8 The estimated linear regression solution is Y^ = b0 + b1X; b0 represents the y-intercept, and b1X represents the slope. When the data is plugged in it equates to Y^ = -59522578.317 + 3.549X. In this particular case, we tried to predict we predicted what the linear regression would be if the value of “X” were to be substituted by $275 million. Our conclusions were made on the following slide.