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Owner: horsewriter01@hotmail.com
Created: May 17, 2011
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Views: 631
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Results in this report
1. Comparision of shopping from 2009 to 2010
2. Final One sample Proportion with data
3. Final One sample Proportion with data
4. Pie Chart
5. Final One sample Proportion with data
6. Comparision Male and Female One sample Proportion with data
7. Age of Online Shopping Histogram
8. Percent of Online Shopping Histogram
9. Age and Percent of Online Shoppers Column Statistics
10. Final Two sample Proportion with data
11. Final Simple Linear Regression
12. One Way ANOVA
13. Contingency Table with data
 
Data sets in this report
1. Responses to Shopping Online
 
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Final
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Statistical Question:

For my final project, I chose to use stat crunch survey results to answer my questions regarding online shopping.  My prime interest was to see if online shopping was becoming more popular to see if I would actually consider using it. Furthermore, I would compare who was doing more online shopping between genders and ages and what percent of shopping was being done online.

Survey Questions:

  • Did online shopping experience a increase from year 2010 from 2009?
  • Does gender affect whether online shopping occurs more or less?
  • Does age come in to effect with online shopping?
  • Did people do more shopping online or offline?

Collection/Sample Method:

I collect my results from the Online Shopping Survey taken in 2010. http://www.statcrunch.com/5.0/index.php?dataid=473741 The survey was directed more toward holiday shopping that was done in those years. It was also tested to seen if shoppers increased their shopping since 2009. The overseer of the survey allowed for the results of the survey to be reused. Since the analyst neglected to mention how the survey was done and assuming this was a stat crunch survey, I conclude this to be a voluntary sample.

Observations and Conclusion:

      First off, I tested to see if more online shopping occurred in the year of 2010 compared to the year 2009. Observing a pie chart of the comparison of the two years made it appear more online shopping occurred in 2010. I followed this chart with a confidence interval to see if this data would appropriately fit a population proportion which turnout to be true on a 95% interval. Then I preceded with a hypothesis test to see if the "More" variable was presently higher than the "Less" and "Same" variables. I found the "More" variable proportion to be equivalent to 50% meaning most of the time people did more shopping online in 2010. Though this was surprising it was somewhat more expected considering businesses last year were promoting online shopping with special offers and savings to get people to buy more. I suspect the subsequent change may have been due to the additional benefits of purchasing online. However, an Anova test displayed that the difference between the two wasn't that significant.
     When it came to comparing the different qualities of the shoppers I was surprised to see more males doing shopping online than female. I checked the confidence interval to make sure this was an appropriate poulation proportion than I proceeded with my hypothesis. Interestingly the male proportion didn't average out to 50% and furthermore comparing males and female P-values I saw it didn't made much a difference between males and females. With age and percent, the average amount of people shopping online came out to be approximately 35 and a average percentage of 42 people doing there shopping online.

Result 1: Comparision of shopping from 2009 to 2010   [Info]
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Result 2: Final One sample Proportion with data   [Info]
95% confidence interval results:
Outcomes in : compare
Success : More
p : proportion of successes
Method: Standard-Wald

Variable Count Total Sample Prop. Std. Err. L. Limit U. Limit
compare 22 41 0.5365854 0.077877566 0.38394815 0.6892226

Result 3: Final One sample Proportion with data   [Info]
Hypothesis test results:
Outcomes in : compare
Success : More
p : proportion of successes
H0 : p = 0.5
HA : p ≠ 0.5

Variable Count Total Sample Prop. Std. Err. Z-Stat P-value
compare 22 41 0.5365854 0.07808688 0.4685213 0.6394

Result 4: Pie Chart   [Info]
Right click to copy

Result 5: Final One sample Proportion with data   [Info]
95% confidence interval results:
Outcomes in : gender
Success : Male
p : proportion of successes
Method: Standard-Wald

Variable Count Total Sample Prop. Std. Err. L. Limit U. Limit
gender 23 41 0.5609756 0.077504046 0.40907046 0.71288073

Result 6: Comparision Male and Female One sample Proportion with data   [Info]
Hypothesis test results:
Outcomes in : gender
Success : Male
Group by: gender
p : proportion of successes
H0 : p = 0.5
HA : p ≠ 0.5
gender Count Total Sample Prop. Std. Err. Z-Stat P-value
Female 0 18 0 0.11785113 -4.2426405 <0.0001
Male 23 23 1 0.1042572 4.7958317 <0.0001

Result 7: Age of Online Shopping Histogram   [Info]
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Result 8: Percent of Online Shopping Histogram   [Info]
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Result 9: Age and Percent of Online Shoppers Column Statistics   [Info]
Summary statistics:
Column n Mean Variance Std. Dev. Std. Err. Median Range Min Max Q1 Q3
percent 41 42.048782 1142.8976 33.806767 5.2797303 40 95 0 95 10 75
age 41 35.439026 135.65244 11.646993 1.8189547 33 51 11 62 29 42

Result 10: Final Two sample Proportion with data   [Info]
Hypothesis test results:
p1 : proportion of successes (Success = Male) for gender
p2 : proportion of successes (Success = Female) for gender
p1 - p2 : difference in proportions
H0 : p1 - p2 = 0
HA : p1 - p2 ≠ 0
Difference Count1 Total1 Count2 Total2 Sample Diff. Std. Err. Z-Stat P-value
p1 - p2 23 41 18 41 0.12195122 0.11043153 1.1043153 0.2695

Result 11: Final Simple Linear Regression   [Info]
Simple linear regression results:
Dependent Variable: percent
Independent Variable: age
percent = 24.263477 + 0.50185645 age
Sample size: 41
R (correlation coefficient) = 0.1729
R-sq = 0.029893674
Estimate of error standard deviation: 33.72182

Parameter estimates:
Parameter Estimate Std. Err. Alternative DF T-Stat P-Value
Intercept 24.263477 17.05705 ≠ 0 39 1.4224898 0.1628
Slope 0.50185645 0.45779097 ≠ 0 39 1.0962567 0.2797


Analysis of variance table for regression model:
Source DF SS MS F-stat P-value
Model 1 1366.6162 1366.6162 1.2017789 0.2797
Error 39 44349.285 1137.1611
Total 40 45715.902

Result 12: One Way ANOVA   [Info]
Analysis of Variance results:
Responses stored in age.
Factors stored in compare.
Factor means
compare n Mean Std. Error
Less 9 33.11111 4.978968
More 22 36.227272 2.2743933
Same 10 35.8 3.6325688


ANOVA table
Source df SS MS F-Stat P-value
Treatments 2 63.745037 31.872519 0.22586274 0.7989
Error 38 5362.3525 141.11455
Total 40 5426.0977


Tukey 95% Simultaneous Confidence Intervals

Less subtracted from
Lower Upper
More -8.347288 14.579612
Same -10.622478 16.000256


More subtracted from
Lower Upper
Same -11.47647 10.621924

Result 13: Contingency Table with data   [Info]
Contingency table results:
Rows: compare
Columns: gender
Female Male Total
Less 5 4 9
More 10 12 22
Same 3 7 10
Total 18 23 41


Chi-Square test:
Statistic DF Value P-value
Chi-square 2 1.3025447 0.5214

Data set 1. Responses to Shopping Online   [Info]
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<A href="http://www.statcrunch.com/5.0/viewreport.php?reportid=19822">Final</A>

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