MATH215 Elementary Statistics II (Experimental)

Department of Science, Technology, Engineering & Mathematics: Mathematics

I. Course Number and Title
MATH215 Elementary Statistics II (Experimental)
II. Number of Credits
3 credits
III. Number of Instructional Minutes
IV. Prerequisites
MATH115 (C or better)
V. Other Pertinent Information
Lab assignments using current technology may be required.
VI. Catalog Course Description
This course is a continuation of MATH115 and is designed primarily for business, economics, and management students. Topics include decision-making procedures in business and related fields that include ANOVA, simple and multiple regression, correlation, time series, forecasting, index numbers, total quality management, and nonparametric methods.
VII. Required Course Content and Direction
  1. Course Learning Goals

    Students will:

    1. use inferential statistical techniques in decision-making;
    2. know which statistical procedure is most appropriate for the analysis of a wide host of real-world situations;
    3. execute statistical procedures both by hand and using current technology with equal confidence; and
    4. explain clearly and concisely the outcome of a statistical procedure.
  2. Planned Sequence of Topics and/or Learning Activities

    1. Analysis of Variance
      1. One-Way ANOVA
      2. Randomized Block Design
      3. Two-Way ANOVA (Factorial Experiments)
    2. Nonparametric Methods
      1. The Sign Test
      2. Wilcoxon Signed Rank Test for One Sample
      3. Wilcoxon Signed Rank Test for Paired Samples
    3. Regression Analysis
      1. The Simple Linear Regression Model
      2. Estimation and Prediction
      3. Coefficients of Correlation and Determination
      4. Significance Tests in Simple Linear Regression
      5. Residual Analysis in Simple Linear Regression
      6. The Multiple Regression Model
      7. Interval Estimation in Multiple Regression
      8. Multiple Correlation Analysis
      9. Significance Tests in Multiple Regression
      10. Computer Analysis and Interpretation
      11. Multicollinearity
      12. Polynomial Regression Models
    4. Time Series
      1. Smoothing Techniques
      2. Seasonal Indexes and Forecasting
      3. Estimation Equations
      4. Index Numbers
    5. Total Quality Management
      1. Statistical Process Control
      2. Control Charts for Variables
      3. Control Charts for Attributes
  3. Assessment Methods for Course Learning Goals

  4. Reference, Resource, or Learning Materials to be used by Student:

    Departmentally-selected textbook, calculators. Details provided by the instructor of each course section. See course syllabus.

Review/Approval Date - Unavailable; New Core 8/2015