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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
F-ratio: Statistical Significance of the Overall Regression Model
The F-ratio, which follows the F-distribution, is the test statistic to assess the statistical significance of the overall model. It tests the hypothesis that the variation explained by regression model is more than the variation explained by the average value (ȳ).
$$ F =\frac{SS_{reg}/df_{reg}}{SS_{res}/df_{res}} $$ $$ = \frac{R^2/k}{(1-R^2)/(n-k-1)}$$Where:
k is the number of explanatory variables, and n is sample size.
SSreg: Explained sum of squared deviation.
SSres: Residual (error) sum of squares.
dfreg: Degrees of freedom (regression) = number of estimated coefficients including intercept − 1 = number of explanatory variables (k).
dfres: Degrees of freedom (residual) = sample size (n) − (k+1).
R2: Coefficient of Determination (R2) is the proportion of the variance in the dependent variable that is predictable from the independent variable.
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