- Cover
- Title Page
- Contents
- About the Authors
- Preface
- I Introduction to Marketing Research
- 1 Marketing Research: An Introduction
- 1.1 Introduction
- 1.2 Difference Between Basic and Applied Research 5
- 1.3 Defining Marketing Research 6
- 1.4 Roadmap to Learn Marketing Research 7
- 1.5 Marketing Research: A Decision Making Tool in the Hands of Management
- 1.6 Use of Software in Data Preparation and Analysis
- 1.7 Ethical Issues in Marketing Research
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 2 Marketing Research Process Design
- 1 Marketing Research: An Introduction
- II Research Design Formulation
- 3 Measurement and Scaling
- 4 Questionnaire Design
- 5 Sampling and Sampling Distributions
- 5.1 Introduction
- 5.2 Sampling
- 5.3 Why Is Sampling Essential?
- 5.4 The Sampling Design Process
- 5.5 Random Versus Non-Random Sampling
- 5.6 Random Sampling Methods
- 5.7 Non-Random Sampling
- 5.8 Sampling and Non-Sampling Errors
- 5.9 Sampling Distribution
- 5.10 Central Limit Theorem
- 5.11 Sample Distribution of Sample Proportion
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- III Sources and Collection of Data
- 6 Secondary Data Sources
- 7 Data Collection: Survey and Observation
- 7.1 Introduction
- 7.2 Survey Method of Data Collection
- 7.3 A Classification of Survey Methods
- 7.4 Evaluation Criteria for Survey Methods
- 7.5 Observation Techniques
- 7.6 Classification of Observation Methods
- 7.7 Advantages of Observation Techniques
- 7.8 Limitations of Observation Techniques
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 8 Experimentation
- 8.1 Introduction
- 8.2 Defining Experiments
- 8.3 Some Basic Symbols and Notations in Conducting Experiments
- 8.4 Internal and External Validity in Experimentation
- 8.5 Threats to the Internal Validity of the Experiment
- 8.6 Threats to the External Validity of the Experiment
- 8.7 Ways to Control Extraneous Variables
- 8.8 Laboratory Versus Field Experiment
- 8.9 Experimental Designs and their Classification
- 8.10 Limitations of Experimentation
- 8.11 Test Marketing
- Summary
- Key Terms
- Discussion Questions
- Case Study
- 9 Fieldwork and Data Preparation
- IV Descriptive Statistics and Data Analysis
- 10 Descriptive Statistics: Measures of Central Tendency
- 10.1 Introduction
- 10.2 Central Tendency
- 10.3 Measures of Central Tendency
- 10.4 Prerequisites for an Ideal Measure of Central Tendency
- 10.5 Mathematical Averages
- 10.6 Positional Averages
- 10.7 Partition Values: Quartiles, Deciles, and Percentiles
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 11 Descriptive Statistics: Measures of Dispersion
- 11.1 Introduction
- 11.2 Measures of Dispersion
- 11.3 Properties of a Good Measure of Dispersion
- 11.4 Methods of Measuring Dispersion
- 11.5 Empirical Rule
- 11.6 Empirical Relationship Between Measures of Dispersion
- 11.7 Chebyshev’s Theorem
- 11.8 Measures of Shape
- 11.9 The Five-Number Summary
- 11.10 Box-and-Whisker Plots
- 11.11 Measures of Association
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 12 Statistical Inference: Hypothesis Testing for Single Populations
- 12.1 Introduction
- 12.2 Introduction to Hypothesis Testing
- 12.3 Hypothesis Testing Procedure
- 12.4 Two-Tailed and One-Tailed Tests of Hypothesis
- 12.5 Type I and Type II Errors
- 12.6 Hypothesis Testing for a Single Population Mean Using the z Statistic
- 12.7 Hypothesis Testing for a Single Population Mean Using the t Statistic (Case of a Small Random Sample When n < 30)
- 12.8 Hypothesis Testing for a Population Proportion
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 13 Statistical Inference: Hypothesis Testing for Two Populations
- 13.1 Introduction
- 13.2 Hypothesis Testing for the Difference Between Two Population Means Using the z Statistic
- 13.3 Hypothesis Testing for the Difference Between Two Population Means Using the t Statistic (Case of a Small Random Sample, n1, n2 < 30, When Population Standard Deviation is Unknown)
- 13.4 Statistical Inference About the Difference Between the Means of Two Related Populations (Matched Samples)
- 13.5 Hypothesis Testing for the Difference in Two Population Proportions
- 13.6 Hypothesis Testing About Two Population Variances (F Distribution)
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 14 Analysis of Variance and Experimental Designs
- 15 Hypothesis Testing for Categorical Data (Chi-Square Test)
- 16 Correlation and Simple Linear Regression Analysis
- 16.1 Measures of Association
- 16.2 Introduction to Simple Linear Regression
- 16.3 Determining the Equation of a Regression Line
- 16.4 Using MS Excel for Simple Linear Regression
- 16.5 Using Minitab for Simple Linear Regression
- 16.6 Using SPSS for Simple Linear Regression
- 16.7 Measures of Variation
- 16.8 Statistical Inference About Slope, Correlation Coefficient of the Regression Model, and Testing the Overall Model
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 17 Multivariate Analysis I: Multiple Regression Analysis
- 17.1 Introduction
- 17.2 The Multiple Regression Model
- 17.3 Multiple Regression Model with Two Independent Variables
- 17.4 Determination of Coefficient of Multiple Determination (R2), Adjusted R2, and Standard Error of the Estimate
- 17.5 Statistical Significance Test for the Regression Model and the Coefficient of Regression
- 17.6 Indicator (Dummy Variable Model)
- 17.7 Collinearity
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 18 Multivariate Analysis lI: Discriminant Analysis and Conjoint Analysis
- 19 Multivariate Analysis III: Factor Analysis, Cluster Analysis, Multidimensional Scaling and Correspondence Analysis
- 20 Sales Forecasting
- 20.1 Introduction
- 20.2 Types of Forecasting Methods
- 20.3 Qualitative Methods of Forecasting
- 20.4 Time Series Analysis
- 20.5 Components of Time Series
- 20.6 Time Series Decomposition Models
- 20.7 The Measurement of Errors in Forecasting
- 20.8 Quantitative Methods of Forecasting
- 20.9 Freehand Method
- 20.10 Smoothing Techniques
- 20.11 Exponential Smoothing Method
- 20.12 Double Exponential Smoothing
- 20.13 Regression Trend Analysis
- 20.14 Seasonal Variation
- 20.15 Solving Problems Involving all Four Components of Time Series
- 20.16 Autocorrelation and Autoregression
- Summary
- Key Terms
- Discussion Questions
- Numerical Problems
- Case Study
- 10 Descriptive Statistics: Measures of Central Tendency
- V Result Presentation
- VI Applications of Marketing Research
- Appendix
Part IV
Descriptive Statistics and Data Analysis
Chapter 10 Descriptive Statistics: Measures of Central Tendency
Chapter 11 Descriptive Statistics: Measures of Dispersion
Chapter 12 Statistical Inference: Hypothesis Testing for Single Populations
Chapter 13 Statistical Inference: Hypothesis Testing for Two Populations
Chapter 14 Analysis of Variance and Experimental Designs
Chapter 15 Hypothesis Testing for Categorical Data
Chapter 16 Correlation and Simple Regression Analysis
Chapter 17 Multivariate Analysis I: Multiple Regression Analysis
Chapter 18 Multivariate Analysis II: Discriminant Analysis and Conjoint Analysis
Chapter 19 Multivariate Analysis III: Factor Analysis, Cluster Analysis, Multidimensional Scaling and Correspondence Analysis
Chapter 20 Sales Forecasting
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