An ice cream company collected data on their ice cream cones sales over a month in July in a Chicago suburb, along with daily temperature and the weather. The company is interested to develop a correlation between ice cream sales to the hot weather. Market research showed that more people come out in certain neighborhoods, to either enjoy the nice weather, or venture out if they do not have air conditioning in their apartments. The Chicago Police also tracked crime statistics during the same period. Crime statistics included murder, assault, robbery, battery, burglary, theft and motor vehicle theft. The data are shown below:
July |
Day Temp (F) |
Weather |
Ice cream sales (units) |
Crime stats reported |
1 |
83 |
Thunderstorm |
590 |
201 |
2 |
81 |
Thunderstorm |
610 |
220 |
3 |
84 |
Thunderstorm |
640 |
199 |
4 |
79 |
Partly sunny |
490 |
195 |
5 |
80 |
Mostly sunny |
550 |
187 |
6 |
84 |
Sunshine |
710 |
280 |
7 |
84 |
Sunshine |
690 |
261 |
8 |
86 |
Thunderstorm |
750 |
310 |
9 |
83 |
Shower |
720 |
254 |
10 |
86 |
Partly sunny |
850 |
300 |
11 |
83 |
Partly sunny |
690 |
219 |
12 |
84 |
Cloudy |
750 |
275 |
13 |
81 |
Thunderstorm |
450 |
156 |
14 |
82 |
Thunderstorm |
550 |
210 |
15 |
80 |
Heavy rain |
25 |
98 |
16 |
81 |
Heavy rain |
78 |
110 |
17 |
86 |
Sunshine |
790 |
256 |
18 |
81 |
Sunshine |
530 |
145 |
19 |
81 |
Sunshine |
490 |
199 |
20 |
80 |
Sunshine |
620 |
245 |
21 |
80 |
Sunshine |
690 |
260 |
22 |
79 |
Sunshine |
540 |
159 |
23 |
81 |
Partly sunny |
610 |
299 |
24 |
80 |
Partly sunny |
590 |
239 |
25 |
81 |
Partly sunny |
590 |
250 |
26 |
80 |
Sunshine |
580 |
200 |
27 |
87 |
Sunshine |
880 |
300 |
28 |
91 |
Sunshine |
1,059 |
361 |
29 |
90 |
Sunshine |
1,000 |
401 |
30 |
91 |
Partly sunny |
960 |
375 |
31 |
88 |
Partly sunny |
890 |
360 |
1.)Develop a linear regression model for ice cream sales over daily temperature. Show the linear equation in the form of y = ax + b, and the coefficient of determination.
What would be the projected forecast of ice cream sales in units, for daily temperature of 94 F?
2.) On July 15 & 16 there were heavy down pour of rain, which might have prevented some to venture out to purchase ice cream during the day. If you were to override those 2 data points, what would be the linear regression model be (by deleting July 15 & 16 data).
which would be considered a better forecast for ice cream sales
3.) Develop a linear regression on ice cream sales to crime statistics. Show the linear equation in the form of y = ax + b, and the r-square value.
Does this correlation demonstrate causation, that high ice cream sales cause crime statistics to go up?
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dt |
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