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Tutorial: Statistical Tests 
for Mean Differences 
Number Analytics
Statistical Tests for Differences 
Choose the correct statistic test to compare 
means 
Area of Applica 
tion 
Level Sca 
ling 
Subgroups 
Test Example 
Hypotheses 
about 
frequency 
Nominal All Chi-square 
Do customer industry types 
differ by company size ? 
Hypotheses 
about means 
Metric 
(Interval 
or 
ratio) 
One 
One Sample 
T-test 
Is the purchase frequency 
different from 1.5? 
Two 
Independent 
Samples T-test 
Is the purchase frequency 
greater for email promotion 
responders than that for non-responders? 
Three or more 
One-way 
ANOVA 
Is the purchase frequency 
different by company size?
One Sample T-test
One Sample T-test 
You can choose your own file 
by uploading it to the cloud. 
Is the overall rating significantly 
different than 4?
One Sample T-test 
3 STEPS! Easy to apply! 
Then manually enter the 
test value and choose th 
e sided test. 
First select the test variable 
in your data file 
Now click on ‘Run’!
One Sample t-test 
Conclusion: Rating is not different 
from 4 on average.
Independent Samples T-test
Independent Samples T-test 
Now we’re interested to know whether 
the rating for female group is 
significantly different from rating for 
male group.
Independent Samples T-test 
Choose the groupin 
g variable 
Select the test variable 
Let’s run results!
Independent Samples T-test
ANOVA
ANOVA 
How about the rating among 
different ethnical groups?
ANOVA
ANOVA 
P value 0.32 (>0.05) 
Conclusion: Rating is indifferent 
across ethnicity. 
P Value: Exact Probability of getting 
a computed test statistic that is due 
to chance. The smaller the p value, 
the smaller the probability that the 
observed result occurred by chance
Cross-tab (Chi-square test)
Cross-tab (Chi-square test) 
• Cross-tab is a frequency table of two 
or three variables 
• Used to examine association between 
two or 3 variables (usually 2) 
• H0: there is a relation between variable X 
and variable Y 
• Variables take a limited number of 
values, for example: 
Consumers: gender, ethnicity 
Business: industry, company size
Cross-tab (Chi-square test)
Cross-tab (Chi-square test) 
P value greater than 0.05, reject H0. 
Conclusion: There is no relation between 
gender and ethnicity

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原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样
 

Tutorial mean difference

  • 1. Tutorial: Statistical Tests for Mean Differences Number Analytics
  • 2. Statistical Tests for Differences Choose the correct statistic test to compare means Area of Applica tion Level Sca ling Subgroups Test Example Hypotheses about frequency Nominal All Chi-square Do customer industry types differ by company size ? Hypotheses about means Metric (Interval or ratio) One One Sample T-test Is the purchase frequency different from 1.5? Two Independent Samples T-test Is the purchase frequency greater for email promotion responders than that for non-responders? Three or more One-way ANOVA Is the purchase frequency different by company size?
  • 4. One Sample T-test You can choose your own file by uploading it to the cloud. Is the overall rating significantly different than 4?
  • 5. One Sample T-test 3 STEPS! Easy to apply! Then manually enter the test value and choose th e sided test. First select the test variable in your data file Now click on ‘Run’!
  • 6. One Sample t-test Conclusion: Rating is not different from 4 on average.
  • 8. Independent Samples T-test Now we’re interested to know whether the rating for female group is significantly different from rating for male group.
  • 9. Independent Samples T-test Choose the groupin g variable Select the test variable Let’s run results!
  • 11. ANOVA
  • 12. ANOVA How about the rating among different ethnical groups?
  • 13. ANOVA
  • 14. ANOVA P value 0.32 (>0.05) Conclusion: Rating is indifferent across ethnicity. P Value: Exact Probability of getting a computed test statistic that is due to chance. The smaller the p value, the smaller the probability that the observed result occurred by chance
  • 16. Cross-tab (Chi-square test) • Cross-tab is a frequency table of two or three variables • Used to examine association between two or 3 variables (usually 2) • H0: there is a relation between variable X and variable Y • Variables take a limited number of values, for example: Consumers: gender, ethnicity Business: industry, company size
  • 18. Cross-tab (Chi-square test) P value greater than 0.05, reject H0. Conclusion: There is no relation between gender and ethnicity