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V Result Presentation
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V Result Presentation
by Naval Bajpai
Marketing Research
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
2.1 Introduction
2.2 Marketing Research Process Design
Summary
Key Terms
Discussion Questions
Case Study
II Research Design Formulation
3 Measurement and Scaling
3.1 Introduction
3.2 What Should be Measured?
3.3 Scales of Measurement
3.4 Four Levels of Data Measurement
3.5 The Criteria for Good Measurement
3.6 Measurement Scales
3.7 Factors in Selecting an Appropriate Measurement Scale
Summary
Key Terms
Discussion Questions
Case Study
4 Questionnaire Design
4.1 Introduction
4.2 What is a Questionnaire?
4.3 Questionnaire Design Process
Summary
Key Terms
Discussion Questions
Case Study
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
6.1 Introduction
6.2 Meaning of Primary and Secondary Data
6.3 Benefits and Limitations of Using Secondary Data
6.4 Classification of Secondary Data Sources
6.5 Roadmap to Use Secondary Data
Summary
Key Terms
Discussion Questions
Case Study
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
9.1 Introduction
9.2 Fieldwork Process
9.3 Data Preparation
9.4 Data Preparation Process
9.5 Data Analysis
Summary
Key Terms
Discussion Questions
Case Study
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
14.1 Introduction
14.2 Introduction to Experimental Designs
14.3 Analysis of Variance
14.4 Completely Randomized Design (One-Way ANOVA)
14.5 Randomized Block Design
14.6 Factorial Design (Two-Way ANOVA)
Summary
Key Terms
Discussion Questions
Numerical Problems
Case Study
15 Hypothesis Testing for Categorical Data (Chi-Square Test)
15.1 Introduction
15.2 Defining χ2-Test Statistic
15.3 χ2 Goodness-of-Fit Test
15.4 χ2 Test of Independence: Two-Way Contingency Analysis
15.5 χ2 Test for Population Variance
15.6 χ2 Test of Homogeneity
Summary
Key Terms
Discussion Questions
Numerical Problems
Case Study
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
18.1 Discriminant Analysis
18.2 Conjoint Analysis
Summary
Key Terms
Discussion Questions
Case Study
19 Multivariate Analysis III: Factor Analysis, Cluster Analysis, Multidimensional Scaling and Correspondence Analysis
19.1 Factor Analysis
19.2 Cluster Analysis
19.3 Multidimensional Scaling
19.4 Correspondence Analysis
Summary
Key Terms
Discussion Questions
Case Study
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
V Result Presentation
21 Presentation of Result: Report Writing
21.1 Introduction
21.2 Organization of the Written Report
21.3 Tabular Presentation of Data
21.4 Graphical Presentation of Data
21.5 Oral Presentation
Summary
Key Terms
Discussion Questions
Case Study
VI Applications of Marketing Research
22 Marketing Mix Research: Product, Price, Place and Promotion Research
22.1 Introduction
22.2 Marketing Mix: Meaning
22.3 New Product Research
22.4 Pricing Research
22.5 Distribution (Place) Research
22.6 Promotional Research
Summary
Key Terms
Discussion Questions
Case Study
Appendix
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20 Sales Forecasting
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21 Presentation of Result: Report Writing
Part V
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