An ice cream company collected data on their ice cream conessales over a month in July in a Chicago suburb, along with dailytemperature and the weather. The company is interested to develop acorrelation between ice cream sales to the hot weather. Marketresearch showed that more people come out in certain neighborhoods,to either enjoy the nice weather, or venture out if they do nothave air conditioning in their apartments. The Chicago Police alsotracked crime statistics during the same period. Crime statisticsincluded murder, assault, robbery, battery, burglary, theft andmotor 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 overdaily 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 inunits, 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 icecream 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 creamsales
3.) Develop a linear regression on ice cream sales to crimestatistics. Show the linear equation in the form of y = ax + b, andthe r-square value.
Does this correlation demonstrate causation, that high ice creamsales cause crime statistics to go up?
