MAT132
MAT132 Introduction to Statistics has a uniform departmental syllabus and a uniform final. The course outcomes and the major outcomes incorporated into this course are assessed on the uniform final exam.
The overwhelming majority of students in MAT132 are neither math majors nor intend to be math majors. However, this course fulfills the Quantitative Skills requirement at Lehman and has between 10 and 15 sections every semester, so it will be included in assessment activities on a regular basis.
MAT132 Course Outcomes [assessed using the problems in the brackets]:
Know the difference between population and samples in an inferential study, compare and contrast different sampling methods. [1]
Categorize variables as either qualitative or quantitative, and discrete or continuous. [2]
Get and interpret descriptive measures of univariate data sets for both samples and populations. Also, differentiate between a parameter and a statistic. [1,2]
Recognize correlations between data sets using scatter diagrams; express linear correlations using least squares regression; determine the strength of the correlation via the correlation coefficient; awareness of lurking variables. [3]
Be familiar with and use the basic definitions and rules of probability theory. [4]
Recognize the features of a binomial experiment and apply the binomial probability distribution. [5]
Infer population parameters using sampling distributions and the Central Limit Theorem. [6]
Limit the error of estimation by calculating confidence intervals. [7]
Accept or reject a hypothesis by establishing a level of significance. [8]
2018-2019 Report
The data referred to in this report may be found here and here. The data show that the target of 70% success was essentially achieved in both semesters on questions #1-5 and in the spring semester it was achieved on questions #6-7 as well. In both semesters below 70% success occurred in question #8, with a low of 53% success in the fall. Hypothesis testing (the topic of question #8) is one of the most difficult in the course and is covered only at the very end of the semester, so lower percentages on #8 are somewhat inevitable. Note that only 11 sections reported their final exam results in the spring. Steps will be taken to ensure a better response rate in the future.
The average pass rate in fall 2018 was 89% and in spring 2019 was 75%, the latter being below the target pass rate of 80%. Again this may be inaccurate because of the low response rate in the spring.