Effective Data Analysis for Better Decision Making

Objectives

At the end of this training program, participants will be able to, among others:

1. Able to construct and interpret common graphs & summary statistics for single variables, two or more variables
2. Able to apply appropriate numerical and graphical analysis, interpret results, make sound conclusion and prepare the report.

Course Content

1. Introduction to Statistics

• Deterministic Vs. Probabilistic
• How to solve Probabilistic Problem?
• What does statistics mean to you?
• Statistics is ….
• The Role of Statistics
• Statistical Methods
• Descriptive Statistics
• Inferential Statistics

2. Data Types and Sampling

• About Data
• Why We Need Data?
• Data Sources
• Data Types
• Population vs Sample
• The importance of Sampling
• Representative Samples

3. Distributions and Summary Statistics

• Distribution Shapes
• Normal Distribution
• Binomial Distribution
• Summary Statistics
• Continuous Variables
• Discrete Variable

4. Numerical and Graphical Analysis for Single Variable

• Numerical Analysis such as Mean, Median, Mode, Standard Deviation, Variance, Range, Quartiles, Inter Quartile Range (IQR), Coefficient of variation, Normality Test, P-Values and its interpretation
• Graphical analysis
• Continuous data: Histogram, Box-Plot, Normal Quantile Plot
• Discrete data: Bar Graph, Pareto Chart

5. Numerical and Graphical Analysis for Two or More Variable

 

6. Relationship between variables

• Numerical analysis such as Coefficient of Correlation (r)
• Graphical analysis
• Continuous vs continuous: Scatter Plot, Run (Trend) Chart
• Continuous vs discrete: Side-by-side Box-Plot, Variability Chart -  Dot-Plot, Bar Graph, Pareto Chart
• Discrete vs discrete: Mosaic Plot
• Non Parametric Test

7. Principles of Graphing Practices

8. Principles of Graphical Excellence

9. Good Graphing Practices

10. Errors in Presenting Data (Data Cleaning)