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Basic Statistics
Basic Statistics
Probability Theory
Mutually Exclusive Events and Overlapping Events
Objective and Subjective Probabilities
Conditional (Posterior) Probability
Independent Events
Law of Total Probabilities
Bayes Theorem
Discrete Probability Distribution
Binomial Distribution
Poisson Distribution
Relationship between Variables
Covariance and Correlation
Joint Probability Distribution
Sum of Random Variables
Correlation and Causation
Simpson’s Paradox
Continuous Probability Distributions
Uniform Distribution
Exponential Distribution
Normal Distribution
Standard Normal Distribution
Approximating Binomial with Normal
t-test
Hypothesis Testing
Type I and Type II Errors
Statistical Significance and Practical Significance
Hypothesis Testing Process
One-Tailed — Known Standard Deviation
Two-tailed — Known Standard Deviation
One-Tailed — Unknown Standard Deviation
Paired t-test
ANOVA
Chi-Square (χ2)
Regression
Simple Linear and Multiple Linear Regression
Least Squares Error Estimation
Overview
Sample Size
Choice of Variables
Assumptions
Normality
Linearity
Dummy Variables
Interaction Effects
Variable Selection Methods
Issues with Variables
Multicollinearity
Outliers and Influential Observations
Coefficient of Determination (R2)
Adjusted R2
F-ratio: Overall Model
t-test: Coefficients
Goodness-of-fit
Validation
Process
Factor Analysis
- Basic Statistics
- Sampling
- Marketing mix Modelling
Coronavirus — What the metrics do not reveal?
Coronavirus — Determining death rate
- Marketing Education
- How to Choose the Right Marketing Simulator
- Self-Learners: Experiential Learning to Adapt to the New Age of Marketing
- Negotiation Skills Training for Retailers, Marketers, Trade Marketers and Category Managers
- Simulators becoming essential Training Platforms
- What they SHOULD TEACH at Business Schools
- Experiential Learning through Marketing Simulators
-
MarketingMind
Basic Statistics
Basic Statistics
Probability Theory
Mutually Exclusive Events and Overlapping Events
Objective and Subjective Probabilities
Conditional (Posterior) Probability
Independent Events
Law of Total Probabilities
Bayes Theorem
Discrete Probability Distribution
Binomial Distribution
Poisson Distribution
Relationship between Variables
Covariance and Correlation
Joint Probability Distribution
Sum of Random Variables
Correlation and Causation
Simpson’s Paradox
Continuous Probability Distributions
Uniform Distribution
Exponential Distribution
Normal Distribution
Standard Normal Distribution
Approximating Binomial with Normal
t-test
Hypothesis Testing
Type I and Type II Errors
Statistical Significance and Practical Significance
Hypothesis Testing Process
One-Tailed — Known Standard Deviation
Two-tailed — Known Standard Deviation
One-Tailed — Unknown Standard Deviation
Paired t-test
ANOVA
Chi-Square (χ2)
Regression
Simple Linear and Multiple Linear Regression
Least Squares Error Estimation
Overview
Sample Size
Choice of Variables
Assumptions
Normality
Linearity
Dummy Variables
Interaction Effects
Variable Selection Methods
Issues with Variables
Multicollinearity
Outliers and Influential Observations
Coefficient of Determination (R2)
Adjusted R2
F-ratio: Overall Model
t-test: Coefficients
Goodness-of-fit
Validation
Process
Factor Analysis
- Basic Statistics
- Sampling
- Marketing mix Modelling
Coronavirus — What the metrics do not reveal?
Coronavirus — Determining death rate
- Marketing Education
- How to Choose the Right Marketing Simulator
- Self-Learners: Experiential Learning to Adapt to the New Age of Marketing
- Negotiation Skills Training for Retailers, Marketers, Trade Marketers and Category Managers
- Simulators becoming essential Training Platforms
- What they SHOULD TEACH at Business Schools
- Experiential Learning through Marketing Simulators
Basic Statistics
Exhibit 33.1 Statistics provide the frameworks for quantitative studies in marketing analytics and research.
Statistics provide the frameworks for quantitative studies in marketing analytics and research. They are the guard rails that reveal the significance and accuracy of the results.
Statistical techniques enable the application of various methods (Exhibit 33.1) such as perceptual maps, cluster analysis, factor analysis, regression, and market response models. These techniques facilitate in-depth analysis and provide visual representations of the data.
This chapter reviews the statistical techniques and frameworks that are relevant to the research and analytics methods in marketing, including probability theory, Bayes theorem, discrete and continuous data distributions, hypothesis testing, ANOVA, correlation, regression, and factor analysis.
Other chapters in this text also delve into statistical techniques within specific contexts. For instance, Chapter 3, Segmentation (Volume I), discusses cluster analysis in the context of segmentation, Chapter 10, Product Design (Volume II), explores conjoint analysis, Chapter 16, Price (Volume II), covers discrete choice models, Chapter 34, Sampling, focuses on sampling methods and statistics, and Chapter 35, Marketing Mix Modelling, discusses marketing response models.
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