Descriptive Statistics

Class Objectives:  Distinguish measurement, categorical and ordinal data; create and interpret frequency tables, proportions and percentages to describe a research sample on categorical data; create and interpret frequency distributions and histograms to describe a research sample on measurement data; calculate and interpret means and standard deviations to describe a research sample on measurement data.

The Normal Distribution and Sampling Distributions

Class Objectives:  Determine when the normal model is appropriate to describe data; apply the characteristics of the normal model to describe the distribution of a measurement variable; apply the standard normal distribution, using statistical tables and Excel, to describe the distribution of a measurement variable.

Estimation, sampling distributions, Introduction to confidence intervals

Class Objectives:  Understand sampling distributions; distinguish between the standard deviation and the standard error for a measurement variable; calculate and interpret a confidence interval for a mean; determine means, standard deviations, and confidence intervals using statistical computing packages.

Introduction to hypothesis testing

Class Objectives:  Understand the logic and notation of hypothesis testing, including the null and alternative hypothesis, Type I and Type II errors, significance levels and p-values, significant and non-significant results, one and two tailed tests; carry out and interpret a one-sample test for a mean; understand the relationship between a t-test and a confidence interval; conduct and interpret a one-sample t-test using statistical packages.

Comparing groups on measurement data

Class Objectives: Classify studies as independent sample or paired sample designs; carry out and interpret results for the paired sample t-test to compare means by hand; carry out and interpret results for the independent sample t-test to compare means by hand; relationship between t-tests and confidence intervals for differences in means; conduct paired and independent sample t-tests using statistical     package R.

Comparing groups on measurement data

Class Objectives: Classify studies as independent sample or paired sample designs; carry out and interpret results for the paired sample t-test to compare means by hand; carry out and interpret results for the independent sample t-test to compare means by hand; relationship between t-tests and confidence intervals for differences in means; conduct paired and independent sample t-tests using statistical package R.

Procedures for categorical outcome data, chi-square tests

Class Objectives: Calculate and interpret a confidence interval for a proportion; conduct and interpret the chi-square goodness of fit test; conduct and interpret the chi-square test of independence; conduct the chi-square test of independence using statistical computing packages.

Association with a measurement outcome, Regression and Correlation

Class Objectives:  Identify when correlation or regression would be an appropriate statistical procedure; interpret a correlation coefficient and the p-value associated with a correlation coefficient; understand the assumptions of the regression model; use a regression equation for prediction; interpret the slope of a regression equation; interpret the p-value associated with the slope or the confidence interval for the slope from a regression equation; interpret the R2 for a regression; conduct correlation and regression analysis using statistical computing packages.

More on categorical outcome data

Class Objectives: Identify when Fisher’s exact test is more appropriate than the usual chi-square test of independence; carry out Fisher’s exact test and Yates’ corrected chi-square using statistical computing packages; chi-squares for larger tables; pairwise comparisons in the context of contingency table analysis.

Multiple regression

Class Objectives:  Identify when multiple regression would be an appropriate statistical procedure; interpret the R2 from a multiple regression; interpret the slope, p-value for the slope, and confidence interval for the slope from a multiple regression; define confounding; interpret the results of multiple regression with respect to controlling for confounding; interpret standardized slopes and partial R2 s from a multiple regression analysis; conduct a multiple regression analysis using R statistical computing package.

Analysis of Variance

Class Objectives:  Identify when ANOVA would be an appropriate statistical procedure; interpret results from the ANOVA table for a one-factor ANOVA; manipulate information from the ANOVA table; understand the role of post-hoc multiple testing in the context of one-factor ANOVA; conduct a one factor ANOVA using statistical computing packages.

More on Analysis of Variance

Class Objectives:  Identify when two-factor ANOVA would be an appropriate statistical procedure; understand the concept of statistical interaction in the context of ANOVA; interpret results and manipulate information from a two-factor ANOVA table; conduct a two-factor ANOVA using statistical computing packages.

Sample size and power considerations

Class Objectives:  Define statistical power; understand information required to carry out sample size calculations; find necessary sample size for descriptive studies estimating means or proportions; find necessary sample size or statistical power for studies comparing two groups on means; find necessary sample size or statistical power for studies comparing two groups on percentages; on-line tools for sample size and power calculations.

More on Analysis of Variance

Class Objectives:  Identify when two-factor ANOVA would be an appropriate statistical procedure; understand the concept of statistical interaction in the context of ANOVA; interpret results and manipulate information from a two-factor ANOVA table; conduct a two-factor ANOVA using statistical computing packages.

More on Nonparametric procedures

- Review / Additional topics of interest
- Final exam distributed

- Review of final exam
- Summary of the course