-
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
Binomial Distribution
The binomial distribution models the number of successes in a sample of size n drawn with replacement from a population of size N. If the sampling is carried out without replacement, as is usually the case in research programmes, the draws are not independent. However, where the population N is much larger (at least 10 times larger) than n, the binomial distribution is a good approximation.
If the random variable X follows the binomial distribution with parameters n ∈ N and p ∈ [0,1], we write X ~ B(n, p). The probability of getting exactly k successes in n trials is given by the probability mass function (Exhibit 33.10):
$$ P(X=k) = \binom{n}{k}p^k(1-p)^{n-k} $$Where:
$$\binom{n}{k}=\frac{n!}{k!(n-k)!}$$The mean and variance of this distribution is:
$$mean,\, E(X)= \bar{p} = µ_X = np $$ $$variance,\, Var(X)=σ_X^2= np(1-p)$$Note: In Excel you may use the BINOM.DIST (k, n, p, cumulative) function to compute P(k).
Previous Next
Use the Search Bar to find content on MarketingMind.
Marketing Analytics Workshop
In an analytics-driven business environment, this analytics-centred consumer marketing workshop is tailored to the needs of consumer analysts, marketing researchers, brand managers, category managers and seasoned marketing and retailing professionals.
Digital Marketing Workshop
Unlock the Power of Digital Marketing: Join us for an immersive online experience designed to empower you with the skills and knowledge needed to excel in the dynamic world of digital marketing. In just three days, you will transform into a proficient digital marketer, equipped to craft and implement successful online strategies.
Contact | Privacy Statement | Disclaimer: Opinions and views expressed on www.ashokcharan.com are the author’s personal views, and do not represent the official views of the National University of Singapore (NUS) or the NUS Business School | © Copyright 2013-2024 www.ashokcharan.com. All Rights Reserved.