GSPA

Course Description: Welcome to Tools for Analysis! This course will help you understand and apply statistical and mathematical analyses to problems in public administration, economics and other social sciences. To begin, we will identify methods of collecting data and discuss some common sources of data and statistics. We will then practice methods of organizing and presenting data, and we will explore descriptive statistics, sampling, statistical inference, probabilities, regression analysis, and methodologies for research. As usual with math, the text material is rather dry...but absolutely necessary!

The text material will be supplemented with a MicroCase computer disk (PC version) and handout problems. This statistical package is licensed to GSPA and can be used on your home computer or in the UCCS computer laboratory (Dwire Hall). Hopefully, yo u will perceive that a statistics software package is a useful tool for solving real world public administration, economics, and other social science problems.

However, don't forget that the best computer is still the one "between your ears". A thorough understanding of statistical methods and the assumptions and limitations of computer programs are essential to avoid problems caused by poor input data and m isinterpretation and/or misuse of computer statistical outputs.

Course Objectives: The primary objectives of this course are to:

1. Demonstrate how the tools of quantitative analysis can be used to make sound decisions in public administration, economics and the other social sciences.

2. Gain an increased awareness of the complexity of good statistics and mathematical methods for policy planning and management in a world of uncertainty.

3. Stimulate intellectual growth through class preparation, discussions, and open-ended exams.

This course includes the mathematical aspects of current topics in public administration, therefore the schedule of lessons and the assigned material may be revised slightly as the course progresses and new material becomes available.

You should come to class prepared to discuss the assigned material. You are expected to demonstrate the ability to maturely and critically discuss the assigned topics and problems. Homework problems will not be graded per se, but you may be asked to demonstrate that you have worked the problems during class discussions. Thus, you are expected to work on the assigned problems prior to the lesson due, and you should be able to explain your solutions to the class if called upon. Also, your participati on in class discussions will be the primary basis for your "instructor option" grade. Finally, there is a direct correlation between the homework problems and the problems included on the exams!

Course Materials: Text: Applied Statistics; Fourth Ed., J. Neter, W. Wasserman, and G. Whitmore, Allen and Bacon Publishers, Inc., 1993.

Handouts: Written handouts and a MicroCases Computer Disk will be provided as supplements to the text material.

Extra Instruction: You are encouraged to schedule extra instruction with me at mutually acceptable times. Don't wait until you believe you are failing! Whenever you experience difficulty, arrange for help. However, do not feel that your confe rence must be limited to discussing course objectives. I am interested in you as an individual, and I will try to assist you in defining and reaching your goals, whatever they may be.

Absence from Class: If you can, notify me in advance. Arrangements which are mutually agreeable to you and me will be made to make up your assignments. Remember, it is your responsibility to contact me and make arrangements to complete all wo rk!

Exams: Exams are mandatory. You are responsible for taking each exam as scheduled.

A Personal Note: As your instructor, I am looking forward to a meaningful learning experience with you this semester. As we go through the course material and apply statistics to problems in public administration, I am sure you will perceive t he relevance of quantitative methods as practical tools for public sector decision making.

Course Schedule

1 Introduction, Data Acquisition, Data Classification

2 Bivariate Data and Models

3 Data Summarization

4 Probability Concepts

5 Random Variables

6 Probability Distributions Review for Exam 1

7 Introduction to Estimation Exam 1

8 Retrospective Analysis of Exam 1 Sampling Distribution of the Mean

9 Estimation of Population Mean

10 Tests for Population Mean

11 Statistical Inference Review for Exam 2

12 Introduction to Linear Regression Exam 2

13 Retrospective Analysis of Exam 2 Simple Linear Regression

14 Review Matrix Algebra

15 Multiple Regression

16 Final Exam

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