The manager of an amusement park would like to be able to predict
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- Beam777
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The manager of an amusement park would like to be able to predict daily attendance in order to develop more accurate plans about how much food to order and how many ride operators to hire. After some consideration, he decided that the following three factors are critical:
Yesterday’s attendance
Weekday or weekend (1 if weekend, 0 if weekday)
Predicted weather
Rain forecast ( 1 if forecast for rain, 0 if not)
Sun ( 1 if mostly sunny, 0 if not)
He then took a random sample of 40 days. For each day, he recorded the attendance, the previous day’s attendance, day of the week, and weather forecast. An example of the first few lines of Data and the regression output are below:
Attendance Yest Att I1 I2 I3
7882 8876 0 1 0
6115 7203 0 0 0
5351 4370 0 0 0
8546 7192 1 1 0
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.836766353
R Square 0.700177929
Adjusted R Square 0.665912549
Standard Error 810.7745532
Observations 40
ANOVA
df SS MS F Significance F
Regression 4 53729535 13432384 20.43398 9.28E-09
Residual 35 23007438 657355.4
Total 39 76736973
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 3490.466604 469.1554 7.439894 1.04E-08 2538.031 4442.903
Yest Att 0.368547078 0.077895 4.731349 3.6E-05 0.210412 0.526682
I1 1623.095785 492.5497 3.295294 0.002258 623.1668 2623.025
I2 733.4646317 394.3718 1.85983 0.071331 -67.1527 1534.082
I3 765.5429068 484.6621 -1.57954 0.123209 -1749.46 218.3734
Test to see if the model is valid. Use alpha = .05
Can we conclude that weather is a factor in determining attendance?
If the manager is looking for a way to help predict attendance, Is this a good model to use? How would you suggest making this model better?
[Solved] The manager of an amusement park would like to be able to predict
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- Submitted On 22 Jun, 2020 07:21:59
- Beam777
- Rating : 2
- Grade : A+
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