Next Step

2. Next Step • Nothing new to learn! • Just need to learn how to put it all together

3. Four Step When Solving a Problem • 1) Read the problem • 2) Decide what statistical test to use • 3) Perform that procedure • 4) Write an interpretation of the results

4. Four Step When Solving a Problem • 1) Read the problem • 2) Decide what statistical test to use • 3) Perform that procedure • 4) Write an interpretation of the results

5. Four Step When Solving a Problem • 1) Read the problem • 2) Decide what statistical test to use • 3) Perform that procedure • 4) Write an interpretation of the results

6. How do you know when to use what? • If you are given a word problem, would you know which statistic you should use?

7. Example • An investigator wants to predict a male adult’s height from his length at birth. He obtains records of both measures from a sample of males.

8. Possible Answers • a. Independent t-test k. Regression • b. Dependent t-test l. Standard Deviation • c. One-Sample t-test m. Z-score • d. Goodness of fit Chi-Square n. Mode • e. Independence Chi-Square n o. Mean • f. Confidence Interval p. Median • g. Correlation (Pearson r) q. Bar Graph • h. Scatter Plot r. Range • Line Graph s. ANOVA • j. Frequency Polygon t. Factorial ANOVA

9. Example • An investigator wants to predict a male adult’s height from his length at birth. He obtains records of both measures from a sample of males. • Use regression

10. Decision Tree • First Question: • Descriptive vs. Inferential • Perhaps most difficult part • Descriptive - a number or figure that summarizes a set of data • Inferential - use a sample to conclude something about a population • hint: these use confidence intervals or probabilities!

11. Decision Tree: Descriptive • One or Two Variables

12. Decision Tree: Descriptive: Two Variables • Graph, Relationship, or Prediction • Graph - visual display • Relationship – Quantify the relation between two continuous variables (CORRELATION) • Prediction – Predict a score on one variable from a score on a second variable (REGRESSION)

13. Decision Tree: Descriptive: Two Variables: Graph • Scatterplot vs. Line graph • Scatterlot • Linegraph • Both are used to show the relationship between two variables (it is usually subjective which one is used)

14. Scatter Plot

15. Line Graph

16. Decision Tree: Descriptive: One Variable • Central Tendency, Variability, Z-Score, Graph • Central Tendency – one score that represents an entire group of scores • Variability – indicates the spread of scores • Z-Score – converts a score so that is conveys the sore’s relationship to the mean and SD of the other scores. • Graph – Visual display

17. Decision Tree: Descriptive: One Variable: Central Tendency • Mean, Median, Mode

18. Decision Tree: Descriptive: One Variable: Central Tendency • Mean, Median, Mode

19. Decision Tree: Descriptive: One Variable: Variability • Variance, Standard Deviation, Range/IQR • Variance • Standard Deviation • Uses all of the scores to compute a measure of variability • Range/IQR • Only uses two scores to compute a measure of variability • In general, variance and standard deviation are better to use a measures of variability

20. Decision Tree: Descriptive: One Variable: Graph • Frequency Polygon, Histogram, Bar Graph • Frequency Polygon • Histogram • Interchangeable graphs – both show frequency of continuous variables • Bar Graph • Displays the frequencies of a qualitative (nominal) variable

21. Frequency Polygon

22. Histogram

23. Bar Graph

24. Decision Tree: Inferential: • Frequency Counts vs. Means w/ One IV vs. Means w/ Two or more IVs • Frequency Counts – data is in the form of qualitative (nominal) data • Means w/ one IV – data can be computed into means (i.e., it is interval or ratio) and there is only one IV • Means w/ two or more IVs – data can be computed into means (i.e., it is interval or ratio) and there are two or more IVs • Confidence Interval - with some degree of certainly (usually 95%) you establish a range around a mean

25. Decision Tree: Inferential: Frequency Counts • Goodness of Fit vs. Test of Independence • Goodness of Fit – Used to determine if there is a good fit between a qualitative theoretical distribution and the qualitative data. • Test of Independence – Tests to determine if two qualitative variables are independent – that there is no relationship.

26. Decision Tree: Inferential: Means with two or more IVs • Factorial ANOVA

27. Decision Tree: Inferential: Means with one IV • One Sample, Two Samples, Three or more • One Sample – Used to determine if a single sample is different, >, or < than some value (usually a known population mean; ONE-SAMPLE t-TEST) • Two Samples – Used to determine if two samples are different, >, or < than each other • Two or more – Used to determine if three or more samples are different than each other (ANOVA).

28. Decision Tree: Inferential: Means with one IV: Two Samples • Independent vs. Dependent • Independent – there is no logical reason to pair a specific score in one sample with a specific score in the other sample • Paired Samples – there is a logical reason to pair specific scores (e.g., repeated measures, matched pairs, natural pairs, etc.)

30. Cookbook • Due Wednesday! • Can be graded on the day of the final • Grading (out of 20 points) • 5 points for complete table of contents • 10 points for no major sections missing

31. Cookbook • Major sections: • 4 major topics (e.g., ANOVA, one-sample t-test, regression, etc.) will be randomly selected for each student • Must be able to find each section using the table of contents • For each major topic a student is missing 10 points will be deducted