Tutorial mean difference

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Tutorial: Statistical Tests for Mean Differences Number Analytics

description

Statistical Analytics (Comparing means test) One-sample T-test, Indepedent samples T-test, ANOVA, Cross-tab chi-square test

Transcript of Tutorial mean difference

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Tutorial: Statistical Tests for Mean Differences

Number Analytics

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Statistical Tests for Differences

Choose the correct statistic test to compare means

Area of Application

Level Scaling 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?

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One Sample T-test

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One Sample T-test

You can choose your own file by uploading it to the cloud.

Is the overall rating significantly different than 4?

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One Sample T-test

Then manually enter the test value and choose the sided test.

First select the test variable in your data file

3 STEPS! Easy to apply!

Now click on ‘Run’!

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One Sample t-test

Conclusion: Rating is not different from 4 on average.

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Independent Samples T-test

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Independent Samples T-test

Now we’re interested to know whether

the rating for female group is

significantly different from rating for

male group.

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Independent Samples T-test

Choose the grouping variable

Select the test variable

Let’s run results!

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Independent Samples T-test

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ANOVA

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ANOVA

How about the rating among different ethnical groups?

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ANOVA

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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

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Cross-tab (Chi-square test)

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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

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Cross-tab (Chi-square test)

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Cross-tab (Chi-square test)

P value greater than 0.05, reject H0. Conclusion: There is no relation between

gender and ethnicity