ADVANCE STATISTICS IN PUBLIC ADMNISTRATION.pptx

POWERPOINT ON A MODULAR OUTLINE OF INSTRUCTIONS ON ADVANCE STATISTICS IN PUBLIC ADMNISTRATION

1. ADVANCE STATISTICS IN PUBLIC ADMNISTRATION Modular Outline of Instructions

2. A. Introduction to Statistics 1. Descriptive vs. inferential Statistics (MODULE 1)

3. 2. Basic elements 2.1. Population and sample 2.2. Types of variables 2.3. Level of measurements

4. 3. Significance of Statistics 3.1. As a research tool 3.2. Research process

5. 4. Research designs (MODULE 2) 4.1. Observational design 4.2. Experimental design 4.3. Sample design

6. 5. Techniques for proper organization. 5.1. Processing of qualitative data 5.2. Coding and encoding of qualitative samples

7. 6. Tables and graphs based on variable measured (MODULE 3) 6.1. Tabular and graphical data presentation 6.2. Frequency and percentage distribution tables 6.3. Frequency histogram and Polygon 6.4. Ogives

8. B. Descriptive Statistics 1.1 Ungrouped data (MODULE 4) 1.1.1 Mean 1.1.2 Mode 1.1.3 Median 1.2. Grouped Data 1.2.1. Mean (MODULE 5) 1.2.2. Mode 1.2.3. Median

9. 2. Measure of Dispersion (MODULE 6) (Manual computation and using the excel and jamovi , SPSS) 2.1. Range 2.2 Variance 2.3. Standard deviation

10. 3. Measure of Positions (Grouped data in Manual computation & Excel or JAMOVI) (MODULE 7) 3.1 Decile 3.2. Quartile 3.3. Percentile 3.4. Z-scores

11. 4. Measure of skewness and Kurtosis (MODULE 8) 3.1. Symmetrical distribution 3.2. Skewed distribution 3.3. Pearson’s coefficient of skewness 3.4. Kurtosis

12. C. Counting rules and probability (Manual computation & Excel or JAMOVI) (MODULE 9) 1. Techniques of counting 2. Rules of probability 3. Random variables 4. Application to Public Administration

13. D. Hypothesis Testing 1. Test of significance (One Sample Manual computation & Excel or JAMOVI) (MODULE 10) 1.1. T test 1.2. Z Test

14. 2. Analysis of variance (MODULE 11) 2.1 One-way ANOVA 2.2. Two-way ANOVA

15. 5. Regression (Manual computation & Excel or JAMOVI) (MODULE 13) 5.1. Linear regression 5.2. multi-linear regression