The local ice cream shop keeps track of how much ice cream they sell versus the
ID: 3198851 • Letter: T
Question
The local ice cream shop keeps track of how much ice cream they sell versus the temperature on that day, here are their figures for the last 4 days:Ice Cream Sales vs Temperature Temperature (C) | Ice cream Sales 2 | 27 5 | 17 7 | 10 11 | 8
Find the slope of the regression line: The local ice cream shop keeps track of how much ice cream they sell versus the temperature on that day, here are their figures for the last 4 days:
Ice Cream Sales vs Temperature Temperature (C) | Ice cream Sales 2 | 27 5 | 17 7 | 10 11 | 8
Find the slope of the regression line:
Ice Cream Sales vs Temperature Temperature (C) | Ice cream Sales 2 | 27 5 | 17 7 | 10 11 | 8
Find the slope of the regression line:
Explanation / Answer
Result:
The local ice cream shop keeps track of how much ice cream they sell versus the temperature that day. Here are their figures for the last 4 days:
Regression Analysis
r²
0.917
n
4
r
0.958
k
1
Std. Error
38.127
Dep. Var.
sales
ANOVA table
Source
SS
df
MS
F
p-value
Regression
160,218.6163
1
160,218.6163
110.22
1.02E-06
Residual
14,536.3004
10
1,453.6300
Total
174,754.9167
11
Regression output
confidence interval
variables
coefficients
std. error
t (df=10)
p-value
95% lower
95% upper
Intercept
-159.4742
54.6407
-2.919
.0153
-281.2213
-37.7270
Temp
30.0879
2.8659
10.499
1.02E-06
23.7022
36.4735
Predicted values for: sales
95% Confidence Interval
95% Prediction Interval
Temp
Predicted
lower
upper
lower
upper
Leverage
20
442.2831
416.341
468.225
353.459
531.107
0.093
Predicts sales when Temperature is 20 is 442.2831
Not applicable. We are predicting the sales from temperature not otherway.
The slope of the line tells you that when the temperature increases by 1 , then the sales will increases by 30.0879.
Calculate residual for the point (14.2, 215)
Predicted sales for Temperature 14.2 = -159.4742+30.0879*14.2 =267.77398
Residual =(215-267.77398) =-52.77398
=-52.7740 ( 4 decimals)
Regression Analysis
r²
0.917
n
4
r
0.958
k
1
Std. Error
38.127
Dep. Var.
sales
ANOVA table
Source
SS
df
MS
F
p-value
Regression
160,218.6163
1
160,218.6163
110.22
1.02E-06
Residual
14,536.3004
10
1,453.6300
Total
174,754.9167
11
Regression output
confidence interval
variables
coefficients
std. error
t (df=10)
p-value
95% lower
95% upper
Intercept
-159.4742
54.6407
-2.919
.0153
-281.2213
-37.7270
Temp
30.0879
2.8659
10.499
1.02E-06
23.7022
36.4735
Predicted values for: sales
95% Confidence Interval
95% Prediction Interval
Temp
Predicted
lower
upper
lower
upper
Leverage
20
442.2831
416.341
468.225
353.459
531.107
0.093
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