1. Introduction

1.1 Definition of Statistics

1.2 Nature and Scope of Statistics

1.3 Importance of Statistics in Business and Management

1.4 Limitations of Statistics

2. Collection of Data

2.1 Primary and Secondary Data

2.2 Types of data

2.3 Methods of Collecting Primary data

2.3 Designing a Questionnaire

3. Presentation of Data in Tables and Charts

3.1 Organization of Numerical Data

3.1.1 Ordered Array

3.1.2 Frequency Distributions

3.1.3 General Rules for Forming Frequency Distributions

3.1.4 Relative Frequency Distributions and Percentage Distributions

3.1.5 Cumulative Frequency Distributions

3.2 Charts for Numerical Data

3.2.1 Histogram, Frequency Polygon

3.2.2 Cumulative Frequency Polygon (Ogive)

3.2.3 Scatter Diagram (Graph for Bivariate Numerical Data)

3.3 Tables and Charts for Categorical Data

3.3.1 Summary Table

3.3.2 Bar Chart, Pie Chart

3.4 Tables and Charts for Bivariate Categorical Data

3.4.1 Contingency Table

3.4.2 Component Bar Chart

4. Statistical Summery Measures

4.1 Measures of Central Tendency (Averages)

4.1.1 Arithmetic Mean, Median, Mode, Geometric Mean

4.1.2 Quartiles

4.2 Measures of Dispersion

4.2.1 Range, Interquartile Range,

4.2.2 Variance and Standard Deviation

4.2.3 Coefficient of Determination

4.3 Measures of Skewness

5. Probability Theory

5.1 The Concept of Probability

5.2 Basic Definitions

5.2.1 Sets, Set Operations, Venn Diagram

5.2.2 Sample Space and Events

5.2.3 Permutations and Combinations

5.3 Probability Approaches

5.3.1 Classical Approach

5.3.2 Relative Frequency Approach

5.3.3 Subjective Approach

5.4 Basic Probability Rules

5.5 Conditional Probability

5.6 Statistical independence and Multiplication Rule

5.7 Joint-Probability Table

5.7.1 Marginal (Simple) Probabilities

5.7.2 Joint Probabilities

5.8 The Law of Total Probability and Probability Tree Diagrams

5.9 Bays’ Theorem

6. Random Variables and Probability Distributions

6.1 Discrete and Continuous Random Variables

6.2 Probability Distribution for a Discrete Random Variable

6.2.1 Expected Value of a Discrete Random Variable

6.2.2 Variance and Standard Deviation of a Discrete Random Variable

6.3 Binomial Distribution

6.3.1 Conditions for a Binomial Random Variable

6.3.2 Applications of Binomial Distribution

6.4 Poisson Distribution

6.4.1 Applications of Poisson Distribution

6.4.2 Poisson Approximation to Binomial Distribution

6.5 Probability Distributions for a Continuous Random Variable

6.6 Normal Probability Distribution

6.6.1 Properties of Normal Distribution

6.6.2 The Standard Normal Distribution

6.6.3 Normal Approximation to Binomial Distribution

7. Sampling and Sampling Distributions

7.1 Census and Sample surveys

7.2 Sampling and Non-Sampling Errors

7.3 Methods of Sampling

7.3.1 Probability Sampling Methods

7.3.2 Non-probability Sampling Methods

7.4 Sampling Distributions

7.4.1 Sampling Distribution of Sample Mean

7.4.2 Central Limit Theorem

7.4.3 Sampling Distribution of Sample Proportion

7.4.4 Finite Population Correction

8. Statistical Estimation

8.1 Point Estimation

8.1.1 Point Estimators for Population Mean Variance and Proportion

8.1.2 Properties of a Good Point Estimator

8.2 Interval Estimation

8.2.1 Confidence Intervals for Population Mean (When σ is Known)

8.2.2 T Distribution

8.2.3 Confidence Intervals for Population Mean (When σ is Unknown)

8.2.4 Confidence Intervals for Population Proportion

9. Hypothesis Testing

9.1 Null Hypothesis and Alternative Hypothesis

9.2 Concepts of Hypothesis Testing

9.2.1 Type I and Type II Errors

9.2.2 The Significance Level

9.2.3 The Test Statistic

9.2.4 Critical Region, Critical Value and P Value

9.2.5 One-Tailed and Two-Tailed Tests

9.3 Testing Hypothesis Concerning Population Mean

9.4 Testing Hypothesis Concerning Population Proportion P

9.5 Chi-square Test of Independence

10. Correlation and Simple Linear Regression

10.1 Scatter Diagram and Correlation

10.2 Simple Linear Regression Model

10.3 The Method of Least Squares

10.4 Coefficient of Determination

10.5 Standard error of the Estimate

11. Time Series Forecasting

11.1 The importance of Time Series Forecasting

11.2 Components of a Time Series

11.3 Mathematical Models for Time Series

11.4 Estimation of Linear Trend

11.4.1 Method of Least Squares

11.4.2 Method of Moving Averages

11.5 Estimation of the Seasonal Component

11.5.1 Seasonal Indices

11.5.2 Ratio to Moving average Method

11.6 Deseasonalized Data

11.7 Forecasting a Multiplicative Series