Sam had 3 caffeinated drinks on april 24 1. use Linear regression to predict the number non caffeinated he had on april 24 and calculate the error of estimate for you prediction.

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Sam had 3 caffeinated drinks on april 24 1. use Linear regression to predict the number non caffeinated he had on april 24 and calculate the error of estimate for you prediction.
For the five days from April 19-23, Sam counted the number of cups of caffeinated drinks he
consumed (X) and the number of non-caffeinated drinks he consumed (Y). The following were the
raw data for X and Y:
Date
X = # caffeinated drinks Y = # non-caffeinated drinks
April 19
April 20
April 21
April 22
April 23
4
5
3
10
2
The mean and standard deviation for X (i.e., # of caffeinated drinks Sam drank) and the mean and
standard deviation for Y (i.e., # of non-caffeinated drinks Sam drank) are:
meany = 6
meany = 3.6
stdDevx = 2.45
stdDevy = 1.9
Transcribed Image Text:For the five days from April 19-23, Sam counted the number of cups of caffeinated drinks he consumed (X) and the number of non-caffeinated drinks he consumed (Y). The following were the raw data for X and Y: Date X = # caffeinated drinks Y = # non-caffeinated drinks April 19 April 20 April 21 April 22 April 23 4 5 3 10 2 The mean and standard deviation for X (i.e., # of caffeinated drinks Sam drank) and the mean and standard deviation for Y (i.e., # of non-caffeinated drinks Sam drank) are: meany = 6 meany = 3.6 stdDevx = 2.45 stdDevy = 1.9
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