University General Course Catalog 2019-2020 
    
    Oct 20, 2020  
University General Course Catalog 2019-2020 ARCHIVED CATALOG: LINKS AND CONTENT ARE OUT OF DATE. CHECK WITH YOUR ADVISOR.

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APST 270 - Introduction to Statistical Methods

(4 units) CO2
Principles of statistics and application to the fields of social, life, and environmental sciences; and economics. Emphasis is given to computer applications. (Depending on the major, students must also achieve a satisfactory score on a placement examination to receive Core credit. This course will fulfill the Core Mathematics requirement for major programs that accept MATH 120.)

Prerequisite(s): ACT Math score of 27 or SAT Math score of 610 or revised SAT score of 630 or Accuplacer QAS of 276 and AAF of 276 or MATH 126  or MATH 127  or MATH 128  or MATH 176  or MATH 181 .

Units of Lecture: 3
Units of Laboratory/Studio: 1
Offered: Every Fall and Spring

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. engage in critical thinking to justify choice of statistics, legitimate data manipulations, data visualization and expressing verbal variables in algebraic terms; compute statistics using algebraic formulas and statistical software; identify results; design and create appropriate graphics and tables for descriptive statistics.
2. work with algebraic formulas to make basic probability calculations; identify and generate classic probability distributions; compare sample data to the normal distribution; evaluate the degree of similarity and defend that evaluation.
3. provide a foundation (including interpreting a verbal description of a research problem into statistical terms); formulate hypotheses both algebraically and verbally; evaluate alternative candidate statistics (including the assumptions) and defend their choice. Statistics include difference-of-means test (“t-test”), difference-of-proportions test, regression, analysis of variance, chi-squared, and, where appropriate, non-parametric analogs.
4. compute the statistical analyses chosen; identify relevant estimates in output from statistical software; design and produce appropriately labeled and clearly presented graphs and tables of statistical results; write correct summaries of and meaningful conclusions about statistical results, drawing both on hypothesis tests and on confidence intervals.


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