Online Templates for Basic Statistics: Rubric Lines 5 & 6 & (4)

1. Online Templates for Basic Statistics:Rubric Lines 5 & 6 & (4) Cindy Alonso David Buncher AP Research

2. Common statistics:Methods, Results, Discussion • Number of participants • Mean • Standard deviation • t-tests • ANOVA • Chi Square • Regression and R2

3. Results • Presents the findings, evidence, results, or products • Tables, data tables, charts, graphs etc… • Label all tables and refer to tables and graphs in the text • Note the p values, t-values, F-values

4. Discussion/Conclusion • Interprets the significance of the p values of the results or findings • Explores connections to the original research question • Include more lit review • Discuss the implications and limitations of the research • Line 5: establish argument from results • Line 6: Data analysis from results • Limitations, future research

5. Variables • Independent variable- manipulated variable How much water is added to a pea plant • Dependent variable- the outcome How tall the flower grows • Control- the pea plant without receiving any water • Constants- same temperature, light, etc • Usually mentioned in “Methods”

6. Number of participants • Number of groups • Number of participants in each group • How were the participants selected • Filtering data: males/females, AP/non AP, … • “Methods”

7. Results: Mean and standard deviation • # of participants (N), the “more” the better (discuss) • Mean (average) Add up all the numbers and divided by the # of responses • Standard deviation- how spread out is the data? • http://www.socscistatistics.com • Range • Makes nice looking graphs and charts for results

8. t-tests • Are the two means statistically (significantly) different? 2 independent means: • Dominos vs Papa Johns delivery time • http://www.socscistatistics.com/tests/studentttest/ • 2 dependent means: • Pretest vs posttest • http://www.socscistatistics.com/tests/ttestdependent/Default.aspx

9. t-tests • Null hypothesis: No difference between the two means • P level usually < .05: results and conclusions • 95% confident of your results • 1-tailed or 2-tailed outcome • Bar graphs with p values in results or conclusions sections

10. ANOVA • Are the “greater than two” means statistically (significantly) different from each other? • F statistic, p value • http://www.danielsoper.com/statcalc3/calc.aspx?id=43 • Bar graphs in results or conclusion

11. Chi Square • Let’s say you want to know if there is a difference in the proportion of men and women who are left handed and let’s say in your sample 10% of men and 5% of women were left-handed. For example, you ask 120 men and 140 women which hand they use and get this:

12. Interpretation • Greater differences between expected and actual data produce a larger Chi-square value. The larger the Chi-square value, the greater the probability that there really is a significant difference. • Tables in results • Discussion of p value in discussion section

13. Correlation (Linear regression) • Relationship between one independent variable and one dependent variable: • Y = mx +b straight line • Prediction model Y = dependent variable x = independent variable b = dependent variable when independent variable = 0 (y-intercept) m= slope !!! Discussion section

14. Scatter plot to determineCorrelation Linear line of best fit y=mx+b

15. Correlation • Caution: • cause and effect • Obvious relationships: colinear • R strength of correlation R = 1 is perfect • http://www.socscistatistics.com/tests/pearson/ • P value

16. R2 • R-squared (R2) is always between 0 and 100%: • 0% indicates that the model explains none of the variability of the response data around its mean. • 100% indicates that the model explains all the variability of the response data around its mean. • In general, the higher the R-squared, the better the model fits your data.

17. Good Luck • David Buncher • dbuncher@dadeschools.net • Cell: 305-527-5000