Question

In: Statistics and Probability

Dataset #2 – Star War Film Data Description: Weekly domestic box office revenues for the 8...

Dataset #2 – Star War Film Data

Description: Weekly domestic box office revenues for the 8 Star War films

Research ‘Question’: Find a ‘best’ linear model to predict Star War revenue/day using the number of theaters, number of weeks since release, film number, and release year.

theaters weeknum film year revperday
3672 1 IV 1977 18498679.7
3672 2 IV 1977 9505314.86
3672 3 IV 1977 4127697.71
3672 4 IV 1977 2632591
3422 5 IV 1977 1950438.14
3311 6 IV 1977 2521766.29
3186 7 IV 1977 2831227.86
2681 8 IV 1977 1023363.71
2170 9 IV 1977 652710.714
1851 10 IV 1977 566439
1202 11 IV 1977 250623.714
907 12 IV 1977 179533.714
505 13 IV 1977 102494.857
311 14 IV 1977 74403.1429
206 15 IV 1977 44651.5714
215 16 IV 1977 46953.5714
228 17 IV 1977 54924.2857
172 18 IV 1977 29591.1429
291 19 IV 1977 76476.1429
270 20 IV 1977 59581
160 21 IV 1977 41030.1429
111 22 IV 1977 28579.4286
57 23 IV 1977 22707.5714
43 24 IV 1977 17242.4286
40 25 IV 1977 11668.7143
30 26 IV 1977 9229
3682 1 V 1980 15161652.6
3682 2 V 1980 8844278.29
3682 3 V 1980 5120454.57
3387 4 V 1980 1772898.57
3025 5 V 1980 1165040.57
2505 6 V 1980 1340427.71
2505 7 V 1980 1944470
2015 8 V 1980 799467
1550 9 V 1980 421755.857
1077 10 V 1980 303789.143
783 11 V 1980 142854.857
502 12 V 1980 85785.1429
352 13 V 1980 52545.1429
441 14 V 1980 70452.4286
388 15 V 1980 45788.2857
388 16 V 1980 41332.7143
360 17 V 1980 39414.5714
205 18 V 1980 24388.8571
151 19 V 1980 17734.5714
95 20 V 1980 14462.7143
80 21 V 1980 12256.4286
72 22 V 1980 4412
15 23 V 1980 786.285714
7 24 V 1980 455.285714
3855 1 VI 1983 17580664.1
3855 2 VI 1983 7119019.71
3805 3 VI 1983 3913192.71
3004 4 VI 1983 2412629
2725 5 VI 1983 1652119.43
2002 6 VI 1983 977608.429
1460 7 VI 1983 643752.429
1008 8 VI 1983 404027.429
605 9 VI 1983 240410.429
409 10 VI 1983 169831.286
310 11 VI 1983 107789.429
248 12 VI 1983 80801.4286
391 13 VI 1983 95609.8571
391 14 VI 1983 90454.4286
321 15 VI 1983 38485
228 16 VI 1983 29893
246 17 VI 1983 25054
164 18 VI 1983 11661.4286
119 19 VI 1983 9036
74 20 VI 1983 8862.57143
55 21 VI 1983 7250
55 22 VI 1983 5731.71429
3858 1 I 1999 20897581.3
3858 2 I 1999 9015073
3858 3 I 1999 3487897.43
3325 4 I 1999 1834563.57
2750 5 I 1999 1438515.14
2424 6 I 1999 1818900.29
2316 7 I 1999 1315771.29
1555 8 I 1999 510037.571
1003 9 I 1999 345916.714
560 10 I 1999 159016.429
340 11 I 1999 96117.5714
245 12 I 1999 69097
160 13 I 1999 49419.4286
441 14 I 1999 136217
422 15 I 1999 93123.1429
331 16 I 1999 57197.7143
231 17 I 1999 39329.1429
191 18 I 1999 29226.5714
140 19 I 1999 22458.7143
89 20 I 1999 14974.7143
4285 1 II 2002 19483946.1
4285 2 II 2002 7050087.71
4005 3 II 2002 3828435.43
3125 4 II 2002 2158583
2585 5 II 2002 1212925.71
1955 6 II 2002 817540.571
1322 7 II 2002 488799.571
1017 8 II 2002 417103.143
775 9 II 2002 193287.571
589 10 II 2002 143490.429
320 11 II 2002 59758.8571
241 12 II 2002 41315.4286
408 13 II 2002 74103.8571
377 14 II 2002 54086.4286
283 15 II 2002 38864.1429
225 16 II 2002 27574.1429
159 17 II 2002 18940
105 18 II 2002 14270.4286
90 19 II 2002 9984.85714
56 20 II 2002 8214.28571
52 21 II 2002 4788.28571
38 22 II 2002 2020.85714
4325 1 III 2005 21314847.9
4455 2 III 2005 6561318.43
4393 3 III 2005 3879632
3455 4 III 2005 1973952.71
2771 5 III 2005 1146060.29
1936 6 III 2005 718753.857
1508 7 III 2005 474352.286
1091 8 III 2005 403442.857
744 9 III 2005 173298.571
415 10 III 2005 78098.7143
301 11 III 2005 51525.8571
190 12 III 2005 33442.8571
505 13 III 2005 84180.1429
356 14 III 2005 51179.8571
245 15 III 2005 33814.8571
201 16 III 2005 21102
135 17 III 2005 17775.7143
95 18 III 2005 11938.8571
44 19 III 2005 7837.85714
44 20 III 2005 6345.28571
36 21 III 2005 3118.28571
23 22 III 2005 1052.42857
4125 1 VII 2015 24281289.7
4125 2 VII 2015 8218801.86
4125 3 VII 2015 3098252
3577 4 VII 2015 1644693.14
1840 5 VII 2015 1302432.86
1732 6 VII 2015 1294747
1732 7 VII 2015 918122.286
1507 8 VII 2015 442270.857
941 9 VII 2015 291175.571
725 10 VII 2015 168580.857
465 11 VII 2015 109324.714
365 12 VII 2015 71774.2857
409 13 VII 2015 93213.2857
321 14 VII 2015 77634.8571
303 15 VII 2015 45363.7143
208 16 VII 2015 30144.8571
122 17 VII 2015 20494.5714
94 18 VII 2015 14027.7143
85 19 VII 2015 12463.4286
66 20 VII 2015 8202.42857
4375 1 VIII 2017 32302438.4
4375 2 VIII 2017 10059634.3
4145 3 VIII 2017 4872357.86
3175 4 VIII 2017 2777846.71
2414 5 VIII 2017 1630078.29
1738 6 VIII 2017 963457.571
1328 7 VIII 2017 558613
1092 8 VIII 2017 564588.286
810 9 VIII 2017 196717.429
601 10 VIII 2017 136677.857
320 11 VIII 2017 76497
252 12 VIII 2017 53219.8571
407 13 VIII 2017 86566.5714
330 14 VIII 2017 57112.1429
240 15 VIII 2017 35131
163 16 VIII 2017 22387.2857
225 17 VIII 2017 21222.2857
85 18 VIII 2017 10420.1429
78 19 VIII 2017 5208.14286

Solutions

Expert Solution

Hello

I'm going to use excel to find "best" linear model, by drawing a regression line in each variable's scatterplot diagram.

(a) Dependent Variable : Revenue per day, Independent Variable : No. of theatres

y = 2227.2x - 1000000

R² = 0.4543

(b) Dependent Variable : Revenue per day, Independent Variable : No. of weeks since release

y = -408649x + 7000000

R² = 0.3118

(c) Dependent Variable : Revenue per day, Independent Variable : Film number

y = 84910x + 2000000

R² = 0.0016

(d) Dependent Variable : Revenue per day, Independent Variable : Release Year

y = 18222x - 30000000

R² = 0.0032

R² is a statistical measure of how close your data is, to the plotted regression equation.

and because R² of the variable No. of theatres is maximum, i.e. 0.4543, it will be the best fit.

I hope this solves your query.

Do give a thumbs up if you find this helpful.

orchestra answered 1 year ago
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