Using the New Call Center Data, provide a summary report for the vice president including the following information
Using new call time and coded quality, develop a prediction equation for new call time. Evaluate the model and discuss the coefficient of determination, significance, and use the prediction equation to predict a call time if there is a defect.
Evaluate whether the new call time meets customer specification. As stated in a previous lesson, customers indicated they did not want a call time longer than 7.5 minutes. Assume a standard deviation of 2 min is acceptable. Is the call center now meeting the customer specifications? If not where is the specification not being met? Explain your answers.
Old Call Time | New Call Time | Shift | Quality | Coded Quality | |
6.5 | 5.2 | AM | Y | 0 | |
6.5 | 5.2 | AM | Y | 0 | |
6.5 | 5.2 | AM | Y | 0 | |
6.5 | 5.2 | AM | Y | 0 | |
7 | 5.6 | AM | Y | 0 | |
7 | 5.6 | AM | Y | 0 | |
7 | 5.6 | AM | Y | 0 | |
7 | 5.6 | AM | N | 1 | |
7 | 5.6 | AM | Y | 0 | |
8 | 6.4 | AM | Y | 0 | |
8 | 6.4 | AM | Y | 0 | |
8.5 | 6.8 | AM | Y | 0 | |
8.5 | 6.8 | AM | Y | 0 | |
9 | 7.2 | AM | Y | 0 | |
9 | 7.2 | AM | Y | 0 | |
9 | 7.2 | AM | N | 1 | |
9 | 7.2 | AM | N | 1 | |
9.5 | 7.6 | AM | N | 1 | |
9.5 | 7.6 | AM | Y | 0 | |
9.5 | 7.6 | AM | Y | 0 | |
10 | 8 | AM | Y | 0 | |
10 | 8 | AM | Y | 0 | |
10 | 8 | AM | Y | 0 | |
10 | 8 | AM | Y | 0 | |
10 | 8 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
10.5 | 8.4 | AM | Y | 0 | |
11.5 | 9.2 | AM | Y | 0 | |
11.5 | 9.2 | AM | Y | 0 | |
11.5 | 9.2 | AM | Y | 0 | |
12 | 9.6 | AM | Y | 0 | |
12 | 9.6 | AM | Y | 0 | |
12 | 9.6 | AM | N | 1 | |
12 | 9.6 | AM | Y | 0 | |
12.5 | 10 | AM | Y | 0 | |
12.5 | 10 | AM | N | 1 | |
13 | 10.4 | AM | Y | 0 | |
13 | 10.4 | AM | Y | 0 | |
13.5 | 10.8 | AM | Y | 0 | |
15.5 | 12.4 | AM | Y | 0 | |
16 | 12.8 | AM | Y | 0 | |
16.5 | 13.2 | AM | Y | 0 | |
17 | 13.6 | AM | Y | 0 | |
18 | 14.4 | AM | Y | 0 | |
6 | 4.8 | PM | Y | 0 | |
9 | 7.2 | PM | N | 1 | |
9.5 | 7.6 | PM | N | 1 | |
10 | 8 | PM | Y | 0 | |
10.5 | 8.4 | PM | Y | 0 | |
10.5 | 8.4 | PM | Y | 0 | |
11 | 8.8 | PM | Y | 0 | |
11 | 8.8 | PM | Y | 0 | |
11 | 8.8 | PM | Y | 0 | |
11 | 8.8 | PM | Y | 0 | |
11.5 | 9.2 | PM | Y | 0 | |
11.5 | 9.2 | PM | Y | 0 | |
11.5 | 9.2 | PM | Y | 0 | |
12 | 9.6 | PM | Y | 0 | |
12 | 9.6 | PM | Y | 0 | |
12 | 9.6 | PM | Y | 0 | |
12 | 9.6 | PM | Y | 0 | |
12 | 9.6 | PM | Y | 0 | |
12 | 9.6 | PM | Y | 0 | |
12 | 9.6 | PM | Y | 0 | |
12.5 | 10 | PM | Y | 0 | |
12.5 | 10 | PM | Y | 0 | |
12.5 | 10 | PM | Y | 0 | |
12.5 | 10 | PM | Y | 0 | |
13 | 10.4 | PM | N | 1 | |
13 | 10.4 | PM | N | 1 | |
13.5 | 10.8 | PM | Y | 0 | |
13.5 | 10.8 | PM | Y | 0 | |
14 | 11.2 | PM | Y | 0 | |
14 | 11.2 | PM | Y | 0 | |
14 | 11.2 | PM | Y | 0 | |
14 | 11.2 | PM | N | 1 | |
14 | 11.2 | PM | Y | 0 | |
14.5 | 11.6 | PM | Y | 0 | |
14.5 | 11.6 | PM | Y | 0 | |
14.5 | 11.6 | PM | N | 1 | |
15 | 12 | PM | N | 1 | |
15 | 12 | PM | Y | 0 | |
15.5 | 12.4 | PM | N | 1 | |
16 | 12.8 | PM | Y | 0 | |
16.5 | 13.2 | PM | Y | 0 | |
17 | 13.6 | PM | Y | 0 | |
17.5 | 14 | PM | Y | 0 | |
18 | 14.4 | PM | Y | 0 | |
18 | 14.4 | PM | Y | 0 | |
18 | 14.4 | PM | Y | 0 | |
18.5 | 14.8 | PM | Y | 0 | |
19 | 15.2 | PM | Y | 0 | |
19.5 | 15.6 | PM | Y | 0 | |
19.5 | 15.6 | PM | Y | 0 | |
5.25 | 4.2 | AM | Y | 0 | |
5.25 | 4.2 | PM | Y | 0 | |
5.25 | 4.2 | AM | Y | 0 | |
5.25 | 4.2 | PM | Y | 0 | |
5.75 | 4.6 | AM | Y | 0 | |
5.75 | 4.6 | PM | Y | 0 | |
5.75 | 4.6 | AM | Y | 0 | |
5.75 | 4.6 | PM | Y | 0 | |
5.75 | 4.6 | AM | Y | 0 | |
6.75 | 5.4 | PM | Y | 0 | |
6.75 | 5.4 | AM | Y | 0 | |
7.25 | 5.8 | PM | Y | 0 | |
7.25 | 5.8 | AM | Y | 0 | |
7.75 | 6.2 | PM | Y | 0 | |
7.75 | 6.2 | AM | Y | 0 | |
7.75 | 6.2 | PM | N | 1 | |
7.75 | 6.2 | AM | Y | 0 | |
8.25 | 6.6 | PM | Y | 0 | |
8.25 | 6.6 | AM | N | 1 | |
8.25 | 6.6 | PM | Y | 0 | |
8.75 | 7 | AM | Y | 0 | |
8.75 | 7 | PM | Y | 0 | |
8.75 | 7 | AM | Y | 0 | |
8.75 | 7 | PM | Y | 0 | |
8.75 | 7 | AM | Y | 0 | |
9.25 | 7.4 | PM | Y | 0 | |
9.25 | 7.4 | AM | Y | 0 | |
9.25 | 7.4 | PM | Y | 0 | |
9.25 | 7.4 | AM | Y | 0 | |
9.25 | 7.4 | PM | Y | 0 | |
9.25 | 7.4 | AM | Y | 0 | |
9.25 | 7.4 | PM | Y | 0 | |
9.25 | 7.4 | AM | Y | 0 | |
10.25 | 8.2 | PM | Y | 0 | |
10.25 | 8.2 | AM | Y | 0 | |
10.25 | 8.2 | PM | Y | 0 | |
10.75 | 8.6 | AM | Y | 0 | |
10.75 | 8.6 | PM | Y | 0 | |
10.75 | 8.6 | AM | Y | 0 | |
10.75 | 8.6 | PM | Y | 0 | |
11.25 | 9 | AM | Y | 0 | |
11.25 | 9 | PM | Y | 0 | |
11.75 | 9.4 | AM | Y | 0 | |
11.75 | 9.4 | PM | Y | 0 | |
12.25 | 9.8 | AM | Y | 0 | |
14.25 | 11.4 | PM | Y | 0 | |
14.75 | 11.8 | AM | Y | 0 | |
15.25 | 12.2 | PM | Y | 0 | |
15.75 | 12.6 | AM | Y | 0 | |
16.75 | 13.4 | PM | Y | 0 |
First copy given data in Excel as it is. Then copy only that part in which the lables are not included.
Then run following R- code.
a=read.table("clipboard",header=F)
attach(a)
l=lm(V2~V5)
summary(l)
And the output is:
> summary(l)
Call:
lm(formula = V2 ~ V5)
Residuals:
Min 1Q Median 3Q Max
-4.6448 -1.8448 -0.3448 1.4750 6.7552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.84478 0.23462 37.698 <2e-16 ***
V5 0.08022 0.71839 0.112 0.911
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’
1
Residual standard error: 2.716 on 148 degrees of
freedom
Multiple R-squared: 8.425e-05, Adjusted R-squared: -0.006672
F-statistic: 0.01247 on 1 and 148 DF, p-value:
0.9112
Here the equation is:
New Call Time = (0.08022)*Coded Quality +
8.84478
Multiple R-squared: 8.425e-05
p-value: 0.9112
Thus the model is not significant.
Get Answers For Free
Most questions answered within 1 hours.