Jan. High Jan. Low July High July Low Precip. Days Precip. Snow Sun Jan. High X
ID: 3170478 • Letter: J
Question
Jan. High
Jan. Low
July High
July Low
Precip.
Days Precip.
Snow
Sun
Jan. High
X
X
X
X
X
X
X
X
Jan. Low
0.965
X
X
X
X
X
X
X
July High
0.152
-0.022
X
X
X
X
X
X
July Low
0.554
0.473
0.712
X
X
X
X
X
Precip.
-0.073
0.002
0.114
0.243
X
X
X
X
Days Precip.
-0.572
-0.460
-0.130
-0.345
0.695
X
X
X
Snow
-0.807
-0.825
-0.080
-0.613
0.157
0.444
X
X
Sun
-0.643
0.512
0.377
0.521
-0.506
-0.826
-0.363
X
a) Suppose you want to predict the annual snowfall for an American city and you are allowed to look at that city’s averages for these other variables. Which variable would be most useful to you? Which variable would be least useful? Support your answer with a short explanation.
b) Suppose you want to predict the average July high temperature for a U.S. city and you are allowed to look at that city’s averages for these other variables. Which variable would be most useful to you? Which variable would be least useful? Support your answer with a short explanation.
c)Your answers to the above questions were largely based on correlation coefficients. What other exploratory data analysis technique should you always use in addition to calculating the correlation coefficient?
Jan. High
Jan. Low
July High
July Low
Precip.
Days Precip.
Snow
Sun
Jan. High
X
X
X
X
X
X
X
X
Jan. Low
0.965
X
X
X
X
X
X
X
July High
0.152
-0.022
X
X
X
X
X
X
July Low
0.554
0.473
0.712
X
X
X
X
X
Precip.
-0.073
0.002
0.114
0.243
X
X
X
X
Days Precip.
-0.572
-0.460
-0.130
-0.345
0.695
X
X
X
Snow
-0.807
-0.825
-0.080
-0.613
0.157
0.444
X
X
Sun
-0.643
0.512
0.377
0.521
-0.506
-0.826
-0.363
X
Climate xlsx StatCrunch Applets Edit Data Stat Graph Help Row City Jan Hi Jan Lo July H July Lo Precip Prec days Snow sun var10 var 11 1 Atlanta 50.4 31.5 88 69.5 50.77 115 2 61 2 Baltimore 40.2 23.4 87.2 66.8 40.76 113 21.3 57 3 Boston 35.7 21.6 81.8 65.1 41.51 126 40.7 58 4 Chicago 29 12.9 83.7 62.6 35.82 126 38.7 55 5 Cleveland 31.9 17.6 82.4 61.4 36.63 156 54.3 49 6 Dallas 54.1 32.7 96.5 74.1 33.7 78 2.9 64 7 Denver 43.2 16.1 88.2 58.6 15.4 89 59.8 70 8 Detroit 30.3 15.6 83.3 61.3 32.62 135 41.5 53 9 Houston 61 39.7 92.7 72.4 46.07 104 0.4 56 16.7 88.7 68.2 37.62 104 20 62 10 Kansas City 34.7 11 Los Angeles 12.01 12 Miami 13 Minneapolis 20.7 2.8 84 63.1 28.32 114 49.2 58 14 Nashville 45.9 26.5 89.5 68.9 47.3 119 10.6 56 60.8 41.8 90.6 73.1 61.88 114 0.2 60 15 New Orleans 16 New York 37.6 25.3 85.2 68.4 47.25 121 28.4 58 37.9 22.8 82.6 67.2 41.41 117 21.3 56 17 Philadelphia 18 Phoenix 65.9 41.2 105.9 81 7.66 36 0 86 19 Pittsburgh 33.7 18.5 82.6 61.6 36.85 154 42.8 46 20 St. Louis 37.7 20.8 89.3 70.4 37.51 111 19.9 57 21 Salt Lake Cit 36.4 19.3 92.2 63.7 16.18 90 57.8 66 San Diego 65.7 23 San Francisc 24 Seattle 45 35.2 75.2 55.2 37.19 156 12.3 46 25 Washington 42.3 26.8 88.5 71.4 38.63 112 17.1 56Explanation / Answer
a. Snowfall is largely correlated with the Jan. Low values and hence would be most useful to predict snowfall.
Similarly, the variable least useful would be July high as snowfall is least correlated with it.
b. Most important for July high is July low and least important is Jan Low
c. In addition to correlation coefficient we must use a linear regression technique to fit the model and check for the significance of the model and the slope parameter. If they turn out to be significant then the model can be used as a predictor of the variables.
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