MPA 509: Statistics for Public Administration
MPA 509: Statistics for Public Administration
Course Code: MPA 509
Credits: 3 Credit Hours
Total Lecture Hours: 48 Hours
Program: Masters in Public Administration (MPA)
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Start Practicing βCourse Description
This course introduces statistical concepts essential for public administration research and decision-making. Students will learn to analyze data, understand relationships between variables, and make evidence-based conclusions for policy development.
Course Objectives
Upon completion of this course, students will be able to:
- Understand fundamental statistical concepts and terminology
- Calculate and interpret measures of central tendency and dispersion
- Analyze relationships using correlation and regression
- Apply probability theory to administrative decision-making
- Perform statistical estimation with confidence intervals
- Conduct hypothesis tests for various scenarios
Units and Topics
Unit 1: Introduction to Statistics (4 LH)
Foundation concepts including types of statistics, data collection, and basic descriptive measures.
| # | Topic | Status |
|---|---|---|
| 1.1 | Introduction to Statistics | β |
| 1.2 | Descriptive and Inferential Statistics | β |
| 1.3 | Measures of Central Tendency | β |
| 1.4 | Measures of Dispersion | β |
Unit 2: Correlation and Simple Linear Regression (4 LH)
Analyzing relationships between variables and making predictions.
| # | Topic | Status |
|---|---|---|
| 2.1 | Karl Pearsonβs Correlation | β |
| 2.2 | Spearmanβs Rank Correlation | β |
| 2.3 | Simple Linear Regression | β |
Unit 3: Probability Theory (10 LH)
Fundamental probability concepts and important probability distributions.
| # | Topic | Status |
|---|---|---|
| 3.1 | Basic Terminologies in Probability | β |
| 3.2 | Approaches to Probability | β |
| 3.3 | Addition Rule of Probability | β |
| 3.4 | Multiplication Rule and Conditional Probability | β |
| 3.5 | Binomial Distribution | β |
| 3.6 | Normal Distribution | β |
Unit 4: Estimation (5 LH)
Sampling distributions, estimation techniques, and confidence intervals.
| # | Topic | Status |
|---|---|---|
| 4.1 | Estimation and Sampling Distribution | β |
| 4.2 | Criteria of Good Estimators | β |
| 4.3 | Point and Interval Estimates | β |
| 4.4 | Determining Sample Size | β |
Unit 5: Hypothesis Testing (25 LH)
Comprehensive coverage of statistical hypothesis testing for various scenarios.
| # | Topic | Status |
|---|---|---|
| 5.1 | Introduction to Hypothesis Testing | β |
| 5.2 | Steps in Hypothesis Testing and Critical Values | β |
| 5.3 | Large Sample Test for Single Mean (Z-Test) | β |
| 5.4 | Large Sample Test for Two Means | β |
| 5.5 | Large Sample Test for Single Proportion | β |
| 5.6 | Large Sample Test for Two Proportions | β |
| 5.7 | Small Sample Test - Independent Means (t-Test) | β |
| 5.8 | Paired t-Test (Dependent Samples) | β |
| 5.9 | Chi-Square Test for Independence | β |
| 5.10 | Chi-Square Goodness of Fit Test | β |
| 5.11 | Kruskal-Wallis Test | β |
Key Features of These Notes
π Beginner-Friendly Approach
- Clear, step-by-step explanations
- Simple language with minimal jargon
- Progressive complexity within each topic
π Mathematical Rigor with Clarity
- All formulas rendered with KaTeX
- Visual diagrams using Mermaid.js
- Derivations explained when relevant
βοΈ Extensive Worked Examples
- 4-5 detailed examples per topic
- Step-by-step solutions
- Real-world public administration context
π Exam-Focused Content
- Practice problems with each chapter
- Summary tables for quick revision
- Key formulas highlighted
Quick Reference: Statistical Tests
| Scenario | Test to Use |
|---|---|
| One mean (Ο known or nβ₯30) | Z-test |
| One mean (Ο unknown, n<30) | t-test |
| Two means (independent, large n) | Two-sample Z-test |
| Two means (independent, small n) | Two-sample t-test |
| Two means (paired data) | Paired t-test |
| One proportion | Z-test for proportion |
| Two proportions | Two-proportion Z-test |
| Categorical association | Chi-square independence |
| Distribution fit | Chi-square goodness of fit |
| 3+ groups (non-parametric) | Kruskal-Wallis test |
Quick Reference: Key Formulas
Measures of Central Tendency
- Mean: $\bar{x} = \frac{\sum x}{n}$
- Median: Middle value when ordered
- Mode: Most frequent value
Correlation
- Pearsonβs r: $r = \frac{\sum(x-\bar{x})(y-\bar{y})}{\sqrt{\sum(x-\bar{x})^2 \sum(y-\bar{y})^2}}$
Regression
- Slope: $b = \frac{n\sum xy - \sum x \sum y}{n\sum x^2 - (\sum x)^2}$
- Intercept: $a = \bar{y} - b\bar{x}$
Probability
- Addition: $P(A \cup B) = P(A) + P(B) - P(A \cap B)$
-
Multiplication: $P(A \cap B) = P(A) \times P(B A)$
Z-Score
- Sample mean: $z = \frac{\bar{x} - \mu}{\sigma/\sqrt{n}}$
- Proportion: $z = \frac{\hat{p} - p}{\sqrt{p(1-p)/n}}$
t-Statistic
- Single mean: $t = \frac{\bar{x} - \mu}{s/\sqrt{n}}$
- Paired: $t = \frac{\bar{d}}{s_d/\sqrt{n}}$
Chi-Square
- Test statistic: $\chi^2 = \sum \frac{(O-E)^2}{E}$
Study Tips
- Master the basics first - Units 1-2 are foundational
- Practice calculations - Statistics requires hands-on work
- Use decision trees - Know which test to use when
- Review assumptions - Each test has specific requirements
- Work through examples - Understanding comes from doing
Resources
- Calculator: Scientific calculator essential for exams
- Tables: Z-table, t-table, chi-square table (often provided)
- Practice: Work through all end-of-chapter problems
These notes are designed to help MPA students prepare for examinations. Content follows the approved syllabus for MPA 509.
Last Updated: August 2026
