more answers

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@@ -16,4 +16,5 @@ h4:locate-the-test-critical-value-for-your-test Locate the test critical value f
 h4:is-the-t-test-statistically-significant-why-or-why-not Is the -test statistically significant? Why or why not?
 h4:calculate-the-confidence-interval-around-the-difference-in-sample-means Calculate the confidence interval around the difference in sample means
 h4:calculate-the-effect-size-i.e.-cohens-d-associated-with-your-t-test Calculate the effect size (i.e., Cohen’s d associated with your -test
+h4:assemble-the-results-into-a-statistical-string Assemble the results into a statistical string
 

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 C:/Users/lhbik/OneDrive/SPU backup/ReCentering Volumes/ReCenterPsychStats/docs/index.html
 C:/Users/lhbik/OneDrive/SPU backup/ReCentering Volumes/ReCenterPsychStats/docs/index.html
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-C:/Users/lhbik/OneDrive/SPU backup/ReCentering Volumes/ReCenterPsychStats/docs/index.html
+C:/Users/lhbik/OneDrive/SPU backup/ReCentering Volumes/ReCenterPsychStats/docs/tPaired.html
+C:/Users/lhbik/OneDrive/SPU backup/ReCentering Volumes/ReCenterPsychStats/Worked_Examples/docs/index.html
+C:/Users/lhbik/OneDrive/SPU backup/ReCentering Volumes/ReCenterPsychStats/Worked_Examples/docs/index.html
 
 
 

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@@ -38,7 +38,7 @@ I will create a mean score of completed items from the traditional pedagogy scal
 TradPed_vars <- c('ClearResponsibilities', 'EffectiveAnswers','Feedback', 'ClearOrganization','ClearPresentation')
 
 #Calculates a mean if at least 75% of the items are non-missing; adjusts the calculating when there is missingness
-JustANOVA$TradPed <- sjstats::mean_n(JustANOVA[, TradPed_vars], .75)
+JustANOVA$TradPed <- sjstats::mean_n(JustANOVA[, ..TradPed_vars], .75)
 
 ```
 
@@ -268,3 +268,7 @@ $$d = t\sqrt{\frac{N_{1}+N_{2}}{N_{1}N_{2}}}$$
 1.209929 * (sqrt((81 + 31)/(81 * 31)))
 ```
 
+#### Assemble the results into a statistical string
+
+$t(110) = 1.210, p > 0.05, CI95 (-0.000, 0.517), d = 0.256$
+

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@@ -577,14 +577,15 @@ Regardless which option(s) you chose, use the elements in the grading rubric to
 |:-----------------------------------------|:-------------:|:--------------|
 |1. Using traditional NHST (null hypothesis testing language), state your null and alternative hypotheses|   2     |               
 |2. Using an R package or functions in base R, calculate the means and standard deviations for both levels of the dependent variable | 4  |  |
-|3. Calulate the SE used in the denominator of the *t*-test |4 |  |
+|3. Calculate the SE used in the denominator of the *t*-test |4 |  |
 |4. Calculate the independent samples *t*-test| 4 ||
 |5. Identify the degrees of freedom associated with your *t*-test |  2 |               
 |6. Locate the test critical value for your test  |2 |  |
 |7. Is the *t*-test statistically significant? Why or why not? | 2 |  |
 |8. What is the confidence interval around the difference in sample means? |4 |  |
-|9. Calculate the effect size (i.e., Cohen's *d* associated with your *t*-test |4 |  |
-|**Totals* **                                  |     28        |             |
+|9.Calculate the effect size (i.e., Cohen's *d* associated with your *t*-test |4 |  |
+|10. Assemble the results into a statistical string |4 |  |
+|**Totals* **                                  |     32        |             |
 
 ```{r, child= 'Worked_Examples/15-4-woRked_tInd.Rmd', echo=FALSE}
 ```

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