Computers R Us Executive Summary
Data Analysis Report Case Study – Computers R Us
Executive summary
This report aims to figure out two basic questions, current consumers’ satisfaction and strategy that would be most potent to increase overall satisfaction. At the beginning, a survey of three parts was designed and conducted across different age groups.420 samples were collected and coding and editing of a variety of data ensued. By resorting to hypothesis test, regression model, this report found out that current consumers’ satisfaction level is much lower than management expected and two gender groups show significantly different satisfaction .Also, consumer satisfaction was similar across five age groups. In respect of determinants of consumer satisfaction, satisfaction …show more content…
3. H0: There is no differences in the overall customer satisfaction across the following age groups. HA: At least the overall customer satisfaction of one age group is different from other age groups.
Since the comparisons of the overall customer satisfaction across the following age groups, ANOVA is necessary to tell if there is are differences. The result is below:
Table3 Anova: Single Factor
SUMMARY
AGE Groups
Count
Sum
Average
Variance
<=20
47
182
3.87234
6.983349
21-30
109
513
4.706422 …show more content…
5. H0: There is no difference in consumer’s satisfaction in response to the initiatives of ‘decreasing response times in the CompleteCare division’ and the ‘new loyalty rewards program’. HA: There is a difference in consumer’s satisfaction in response to the initiatives of ‘decreasing response times in the CompleteCare division’ and the ‘new loyalty rewards program’.
As these two samples are independent and come from the same population, two-sample t-test is appropriate to prove if there is a difference. The result is listed below,
Table5 t-Test: Two-Sample Assuming Equal Variances
Variable 1
Variable 2
Mean
4.371428571
5.645238095
Variance
6.348585066
7.842817366
Observations
420
420
Pooled Variance
7.095701216
Hypothesized Mean Difference
0
df
838
t Stat
-6.929732637
P(T<=t) one-tail
4.20464E-12
t Critical one-tail
1.646673991
P(T<=t)
Executive summary
This report aims to figure out two basic questions, current consumers’ satisfaction and strategy that would be most potent to increase overall satisfaction. At the beginning, a survey of three parts was designed and conducted across different age groups.420 samples were collected and coding and editing of a variety of data ensued. By resorting to hypothesis test, regression model, this report found out that current consumers’ satisfaction level is much lower than management expected and two gender groups show significantly different satisfaction .Also, consumer satisfaction was similar across five age groups. In respect of determinants of consumer satisfaction, satisfaction …show more content…
3. H0: There is no differences in the overall customer satisfaction across the following age groups. HA: At least the overall customer satisfaction of one age group is different from other age groups.
Since the comparisons of the overall customer satisfaction across the following age groups, ANOVA is necessary to tell if there is are differences. The result is below:
Table3 Anova: Single Factor
SUMMARY
AGE Groups
Count
Sum
Average
Variance
<=20
47
182
3.87234
6.983349
21-30
109
513
4.706422 …show more content…
5. H0: There is no difference in consumer’s satisfaction in response to the initiatives of ‘decreasing response times in the CompleteCare division’ and the ‘new loyalty rewards program’. HA: There is a difference in consumer’s satisfaction in response to the initiatives of ‘decreasing response times in the CompleteCare division’ and the ‘new loyalty rewards program’.
As these two samples are independent and come from the same population, two-sample t-test is appropriate to prove if there is a difference. The result is listed below,
Table5 t-Test: Two-Sample Assuming Equal Variances
Variable 1
Variable 2
Mean
4.371428571
5.645238095
Variance
6.348585066
7.842817366
Observations
420
420
Pooled Variance
7.095701216
Hypothesized Mean Difference
0
df
838
t Stat
-6.929732637
P(T<=t) one-tail
4.20464E-12
t Critical one-tail
1.646673991
P(T<=t)