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 be able to:

    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.

    Core Learning Goals:
    Category III:
      Critical Thinking and Reading: The student will be able to:
      1. use methods, concepts and theories in new situations. (application skills)
      2. identify the explicit and implied features of a communication, especially in arguments that put forth a conclusion. (analysis skills)
      3. assess the credibility of a communication and the strength of claims and arguments. (evaluation skills)
      4. reason from what they know to form new knowledge, draw conclusions, solve problems, explain, decide, and/or predict. (inductive and/or deductive reasoning skills)
      5. communicate and justify clearly the results of their reasoning. (presenting argument skills)

    Core Learning Objectives:
    Category III:
      Critical Thinking and Reading: The student will be able to:
      1. use methods, concepts and theories in new situations. (1)
      2. identify the main conclusion in an argument. (2)
      3. determine if the conclusion is supported with reasons and identify those that are stated or implied. (2)
      4. identify the background information provided to explain reasons which support a conclusion. (2)
      5. evaluate the credibility, accuracy, and reliability of sources of information. (3)
      6. determine if an argument rests on false, biased, or doubtful assumptions. (3)
      7. collect and question evidence. (4)
      8. list alternatives and consider their pros and cons, including their plausibility and practicality, when making decisions or solving problems. (4)
      9. project alternative hypotheses regarding an event, and develop a variety of different plans to achieve some goal.(4)
      10. locate and cite various independent sources of evidence, rather than a single source of evidence, to provide support for a conclusion. (4)
      11. present an argument succinctly in such a way as to convey the crucial point of an issue. (5)
      12. present supporting reasons and evidence for conclusion(s) which address the concerns of the audience. (5)

  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



Review/Approval Date - Unavailable