Student Learning Outcomes BS in Statistics

A student should demonstrate knowledge of the underlying principles, computational methods, and applications of:

1. Calculus-based probability

a. Demonstrate knowledge of probability laws and conditional probability.

b. Set up and solve distributional problems including problems that involve calculus.

c. Demonstrate knowledge of properties of well-known distributions such as normal and binomial.

d. Demonstrate knowledge of the basic theorems for linear combination of independent random variables.

2. Statistical inference

a. Role of Central Limit Theorem in constructing large sample confidence intervals and tests of hypotheses for means and proportions.

b. Use of the t-distribution including assumptions under which its use is appropriate. c. Frequency interpretation of confidence intervals, tests of hypothesis, and p-values.

d. Knowledge of methods of estimation including maximum likelihood and method of moments, mean square error.

3. Analysis of variance

a. Set up models for single factor and multiple factor treatment structures and interpret interaction and main effects.

b. Apply multiple comparison procedures such as LSD and Tukey HSD.

c. Distinguish between completely random and randomized complete block design and know how to use blocking effectively.

d. Carry out analysis using SAS and interpret output in a way non-statisticians would understand.

4. Regression analysis

a. Set up models for simple linear, multiple regression, and variants of the multiple regression model such as polynomial regression in which the parameters are linear.

b. Carry out inferences for the model parameters.

c. Construct prediction intervals and confidence intervals for E(Y|X), assess model fit or lack of fit.

d. Carry out analysis using SAS and interpret output in a way non-statisticians would understand

5. Study design (experimental design or sampling)

a. Select the appropriate experimental design or sampling plan for a scientific study.

b. Be able to identify standard study designs

c. Select appropriate models and analyses for standard study designs.

d. Report results using tabular and graphical methods as appropriate and explain results in a way non-statisticians would understand.

The major assessment activity was the revision of the student learning outcomes. This was done as a result of the negative feedback by the assessment office on last year’s assessment activities. The outcomes are now clearly defined and can be directly assessed using conventional means such as test questions and projects instead of composite course grades which were a major component of the initially-approved assessment plan. Data were taken for the spring and fall semesters of 2008 under the new assessment plan. Because the undergraduate program is small, at most 4 students were available to be assessed on any of the revised outcomes. Outcomes appear to be satisfactory but not outstanding. Two items emerged for further consideration: (1) how to deal with a possible disconnect between theory and application for statistics students in applied courses, and (2) the possibility of taking a more individualized approach in dealing with the few statistics majors in the required courses which are mostly populated with non-majors.