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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
-
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
Law of Total Probabilities
The law of total probability is a rule relating marginal probabilities to conditional probabilities. It expresses the total probability of an outcome which can be realized via several distinct events.
Consider n mutually exclusive and collectively exhaustive events, A1, A2 … An, the probability for any event B to occur:
$$P(B)=\sum_{i=1}^n P(B∩A_i )=P(B∩A_1 )+P(B∩A_2 )… P(B∩A_n)$$ $$P(B)=P(B│A_1 )P(A_1 )+P(B│A_2 )P(A_2 )… P(B|A_n)P(A_n)$$Taking cards for example (Exhibit 34.5), the probability of picking a queen:
$$P(Q)=P(Q│H)P(H)+P(Q│D)P(D)+P(Q|C)P(C)+P(Q|S)P(S)$$Basically, this is the same as weighted average.
Example: As shown is Exhibit 34.6, the age profile of a target population (universe) is broken down into 4 groups: below 20, 20 to 30, 30 to 50 and over 50. The proportion of the population that falls in each group is 20%, 20%, 40% and 20% respectively, and the proportion of brand buyers in each is 10%, 15%, 20% and 15%. Based on this information, the proportion of brand buyers in the target population is 16%, as computed in Exhibit 34.6.
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