Question

In: Statistics and Probability

To study how social media may influence the products consumers​ buy, researchers collected the opening weekend...

To study how social media may influence the products consumers​ buy, researchers collected the opening weekend box office revenue​ (in millions of​ dollars) for 23

recent movies and the social media message rate​ (average number of messages referring to the movie per​ hour). The data are available below. Conduct a complete simple linear regression analysis of the relationship between revenue​ (y) and message rate​ (x).

Message_Rate   Revenue_($millions)
1363.3 148
1214.8 74
575.9 64
311.3 36
458.1 35
293.2 34
248.3 25
679.5 18
151.7 17
169.6 17
109.7 16
144.3 16
410.2 15
93.4 15
104.2 15
121.8 14
70.7 13
81.3 12
127.6 6
52.2 6
149.6 5
36.3 3
4.2 2

Solutions

Expert Solution

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.893045
R Square 0.797529
Adjusted R Square 0.787887
Standard Error 14.73495
Observations 23
ANOVA
df SS MS F Significance F
Regression 1 17959.72 17959.72 82.71844 9.93E-09
Residual 21 4559.494 217.1187
Total 22 22519.22
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 2.119965 4.06647 0.521328 0.607593 -6.33672 10.57665 -6.33672 10.57665
Message_Rate   0.079935 0.008789 9.094968 9.93E-09 0.061657 0.098212 0.061657 0.098212
  • The least square regression line is:

    y' = 2.12 + 0.0799x

Calculation:

x y (x-xbar)^2 (y-ybar)^2 (x-xbar)*(y-ybar)
1363.3 148 1124033.3 14799.251 128976.1637
1214.8 74 831204.82 2270.7297 43444.69414
575.9 64 74422.212 1417.6862 10271.67675
311.3 36 67.311323 93.164461 79.18979206
458.1 35 24026.348 74.860113 1341.124575
293.2 34 97.923932 58.555766 -75.72325142
248.3 25 3002.5635 1.8166352 73.85500945
679.5 18 141680.23 69.6862 -3142.158034
151.7 17 22920.643 87.381853 1415.220227
169.6 17 17821.089 87.381853 1247.89414
109.7 16 37401.878 107.0775 2001.224575
144.3 16 25216.059 107.0775 1643.189792
410.2 15 11471.341 128.77316 -1215.401512
93.4 15 43972.267 128.77316 2379.589792
104.2 15 39559.48 128.77316 2257.03327
121.8 14 32868.113 152.46881 2238.607183
70.7 13 54007.739 178.16446 3101.976749
81.3 12 49193.311 205.86011 3182.285444
127.6 6 30798.724 414.03403 3570.955009
52.2 6 62948.628 414.03403 5105.181096
149.6 5 23560.915 455.72968 3276.798488
36.3 3 71179.92 545.12098 6229.098488
4.2 2 89338.611 592.81664 7277.459357
sum 6971.2 606 2810793.4 22519.217 224679.9348
mean 303.0956522 26.34782609 sxx syy sxy
slope=b1=sxy/sxx 0.079934703
intercept=b0=ybar-(slope*xbar) 2.119965151
SST SYY 22519.217
SSR sxy^2/sxx 17959.724
SSE syy-sxy^2/sxx 4559.4935
r^2 SSR/SST 0.7975288
error variance s^2 SSE/(n-2) 1519.8312
S^2b1 s^2/sxx 0.0005407
standard error b1=se(b1) sqrt(s^2b1) 0.0232532
test statistics b1/se(b1) 3.4375748

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