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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
- Is Marketing Education Fluffy and Weak?
- 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
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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
- Is Marketing Education Fluffy and Weak?
- 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
Regression Analysis — Interaction Effects
Intended to achieve common imperatives, the elements of the marketing mix often interact synergistically to produce an effect greater than the sum of their individual effects.
Take consumer promotions for instance. Discounts, displays and co-op ads, often have a synergistic impact lifting sales more than the sum of their individual impact on sales.
Similarly, theme advertising strengthen a brand’s equity, resulting in the lowering of consumers’ sensitivity to changes in price.
These interaction effects where one element of the marketing mix affects the sensitivity of one or more other elements, can be captured in response functions by the inclusion of an additional term, x1 × x2, formed by the product of the two variables that interact.
$$ y = b_0 + b_1 x_1 + b_2 x_2 + b_3 x_1 x_2 $$In this example, the overall effect of x1 for any value of x2 is equal to: b1-total = b1 + b3 x2.
To determine the presence of the moderator effect (b3x1x2), compare the unmoderated R2 with the moderated R2. A statistically significant difference would imply the existence of the interaction effect between the variables.
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