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

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

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

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

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”

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”

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

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

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

ANOVA Are the “greater than two” means statistically (significantly) different from each other? F statistic, p value http://www.danielsoper.com/stat calc3/calc.aspx?id=43 Bar graphs in results or conclusion

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: Left-handed Right-handed Men 12 108 Women 7 133

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

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

Scatter plot to determine Correlation Linear line of best fit y=mx+b

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

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.

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