# 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.

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

[ Select ]                       [“-3892.2 + 40.1(x); r^2 = .78”, “-3462.4 + 49.4(x); r^2 = .78”, “-2362.5 + 39.2(x); r^2 = 0”, “-3432.6 + 41.3(x); r^2 = .61”]         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 correlation of determination (r^2).

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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.
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[ Select ]                       [“1201”, “1101”, “1001”, “1181”]         What would be the projected forecast of ice cream sales in units, for daily temperature of 94 F?

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[ Select ]                       [“-2808.1 + 41.9(x); r^2 = .86”, “-1362.5 + 33.2(x); r^2 = .67”, “-3932.6 + 51.3(x); r^2 = .93”, “-2892.2 + 60.1(x); r^2 = .55”]         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).

[ Select ]                       [“second correlation is a better forecast”, “need more data”, “no different”, “first correlation is a better forecast”]         Compare the two correlation coefficients, which would be considered a better forecast for ice cream sales

[ Select ]                       [“92.2 + 10.1(x); r^2 = .61”, “50.1 + 11.8(x); r^2 = .96”, “32.6 + .39(x); r^2 = .71”, “46.8 + .30(x); r^2 = .82”]         Develop a linear regression on ice cream sales to crime statistics. Show the linear equation in the form of y = ax + b, and the correlation of determination (r^2).

[ Select ]                       [“no, correlation does not imply causality”, “yes, strong correlation does imply causality”]         Does this correlation demonstrate causation, that high ice cream sales cause crime statistics to go up?