Statistics for Decision Making ( Summer 2025)

Course Content Overview

1. Introduction to Statistics

• Definition and Characteristics of Statistics

• Types: Descriptive vs. Inferential Statistics

• Importance, Scope, and Applications of Statistics in Business

• Limitations and Misuses of Statistics

2. Statistical Foundations

• Population vs. Sample

• Parameter vs. Statistic

• Data: Meaning, Types (Qualitative & Quantitative), Sources

• Data Collection Tools

• Variables: Definition and Types

• Levels of Measurement: Nominal, Ordinal, Interval, Ratio

3. Data Organization and Presentation

• Frequency and Relative Frequency Distributions

• Cumulative Frequency Distributions

• Graphical Representations: Histograms, Ogives, Bar Graphs, Pie Charts

• Pros and Cons of Graphical Methods

4. Measures of Central Tendency and Dispersion

• Ungrouped vs. Grouped Data

• Measures: Arithmetic Mean, Geometric Mean, Median, Mode

• Dispersion: Range, Average Deviation, Variance, Standard Deviation, Coefficient of Variation

• Uses, Merits, and Limitations

5. Exploratory Data Analysis

• Stem-and-Leaf Plot

• Quartiles, Deciles, Percentiles

• Box-and-Whisker Plot

• Mathematical Problems on Descriptive Measures

6. Correlation and Regression

• Bi-variate Data Analysis

• Scatter Diagrams

• Simple Correlation and Coefficient Calculation

• Simple and Multiple Linear Regression

• Adjusted R² and Coefficient of Determination

• Forecasting using Regression Equations

7. Probability Theory

• Concepts and Laws of Probability: Addition, Multiplication, Conditional, Independent, Complementary

• Bayes' Theorem

8. Probability Distributions

• Random Variables and Their Distributions

• Discrete Distributions: Binomial, Poisson

• Continuous Distributions: Normal Distribution and its Properties

9. Sampling and Sampling Distributions

• Population vs. Sample

• Census vs. Sampling

• Types of Sampling Techniques

• Sampling Error and Central Limit Theorem

10. Estimation and Confidence Intervals

• Concept of Estimation: Point and Interval

• Properties of a Good Estimator

• Confidence Intervals for Population Parameters

11. Hypothesis Testing

• Basic Terminology and Concepts

• Types of Hypotheses

• Level of Significance and Errors (Type I & II)

• Hypothesis Testing for:

  • A Single Mean

  • Difference Between Two Means