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4.3.1 Analysis of Sample Data

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- Module 1 Descriptive Statistics
  Module 1 Descriptive Statistics
- Module 2 Random Variables and Discrete Probability Distributions
  Module 2 Random Variables and Discrete Probability Distributions
  - 2.1 Introduction to Random Variables
    2.1 Introduction to Random Variables
    
    2.1.1 Introduction to Random Distribution
    
    2.1.2 Examples of Random Variables
  - 2.2 Linear Combination of Random Variables
    2.2 Linear Combination of Random Variables
    
    2.2.1 Linear Combinations of Random Variables
    
    2.2.2 Examples of Linear Combinations of Random Variables
  - 2.4 Binomial Distribution
    2.4 Binomial Distribution
    
    2.4.1 Bernoulli Random Variable and its Distribution
    
    2.4.2 Binomial Random Variables and Its Distribution
    
    2.4.3 Examples of Binomial Distributions
    
    2.4.4 Recap of Binomial Distribution
  - 2.5 Poisson Distribution
    2.5 Poisson Distribution
    
    2.5.1 Poisson Random Variable and its Distribution
    
    2.5.2 Examples of Poisson Distribution
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  Module 3 Continuous Probability Distributions
  - 3.1 Continuous Random Variable Introduction
    3.1 Continuous Random Variable Introduction
    
    3.1 Continuous Random Variables - Introduction
  - 3.2 Uniform Distribution
    3.2 Uniform Distribution
    
    3.2.1 Uniform Distribution
    
    3.2.2 Examples of Uniform Distribution
  - 3.3 Exponential Distribution
    3.3 Exponential Distribution
    
    3.3.1 Exponential Distribution
    
    3.3.2 Memoryless Property of Exponential Distribution
    
    3.3.3 Relation Between Exponential and Poisson Distribution
    
    3.3.4 Examples
  - 3.4 Normal Distribution
    3.4 Normal Distribution
    
    3.4.1 The Normal Random Variable and its Distribution
    
    3.4.2 Standard Normal Distribution
    
    3.4.3 Examples of Normal Distribution
    
    3.4.4 Normal Approximation to the Binomial Distribution
  - 3.5 Linear Combinations of Normal Random Variables
    3.5 Linear Combinations of Normal Random Variables
    
    3.5.1 Linear Combinations of Random Variables I
    
    3.5.2 Linear Combinations of Random Variables II
  - 3.6 Chi Square Distribution
    3.6 Chi Square Distribution
    
    3.6.1 Chi Square Distribution
  - 3.7 Student's t Distribution
    3.7 Student's t Distribution
    
    3.7.1 Student's t Distribution
  - 3.8 F Distribution
    3.8 F Distribution
    
    3.8.1 F Distribution
- Module 4 Sampling and Sampling Distributions
  Module 4 Sampling and Sampling Distributions
  - 4.1 Sampling Process
    4.1 Sampling Process
    
    4.1.1 Sampling Process
  - 4.2 Sampling Methods
    4.2 Sampling Methods
    
    4.2.1 Sampling Methods
  - 4.3 Analysis of Sample Data
    4.3 Analysis of Sample Data
    
    4.3.1 Analysis of Sample Data 4.3.1 Analysis of Sample Data
    Table of contents
    
    Introduction
    
    Understanding Population Parameters
    
    Example of Data Analysis
    
    Descriptive Statistics
    
    Estimating Population Parameters
    
    Discussion on Sample Statistics
    
    Conclusion
  - 4.4 Sampling Distribution
    4.4 Sampling Distribution
    
    4.4.1 Sampling Distribution
  - 4.5 Central Limit Theorem
    4.5 Central Limit Theorem
    
    4.5.1 Central Limit Theorem
  - 4.6 Sampling Distribution of Proportion
    4.6 Sampling Distribution of Proportion
    
    4.6.1 Sampling Distribution of Proportion
  - 4.7 Distribution of Sample Variance
    4.7 Distribution of Sample Variance
    
    4.7.1 Distribution of Sample Variance
  - 4.8 Properties of Point Estimator
    4.8 Properties of Point Estimator
    
    4.8.1 Properties of Point Estimator
  - 4.9 Sampling Error vs Non sampling errors
    4.9 Sampling Error vs Non sampling errors
    
    4.9.1 Sampling Error vs Non sampling errors
- Module 5 Statistical Inference
  Module 5 Statistical Inference
  - 5.1 Confidence Interval
    5.1 Confidence Interval
    
    5.1.1 Confidence Interval
    
    5.1.2 Examples of Confidence Interval
    
    5.1.3 Effect of Alpha and Sample Size on Confidence Interval
  - 5.2 Confidence Interval for Population Mean
    5.2 Confidence Interval for Population Mean
    
    5.2.1 Confidence Interval for Population Mean
    
    5.2.2 Confidence Interval for Population Mean - 𝜎 Unknown
    
    5.2.3 Examples of Confidence Interval - 𝜎 Unknown
    
    5.2.4 Determining the Sample Size
  - 5.3 Confidence Interval for Proportion
    5.3 Confidence Interval for Proportion
    
    5.3.1 Confidence Interval for Proportion
  - 5.4 Confidence Interval for Variance
    5.4 Confidence Interval for Variance
    
    5.4.1 Confidence Interval for Variance
  - 5.5 Hypothesis Testing
    5.5 Hypothesis Testing
    
    5.5.1 Hypothesis Testing - Introduction
    
    5.5.2 Null and Alternative Hypothesis
  - 5.6 Errors in Hypothesis Testing
    5.6 Errors in Hypothesis Testing
    
    5.6.1 Errors in Hypothesis Testing
  - 5.7 Hypothesis Testing for Population Mean
    5.7 Hypothesis Testing for Population Mean
    
    5.7.1 Hypothesis Testing for Population Mean
    
    5.7.2 Summary - Hypothesis Testing for Population Mean
  - 5.8 Hypothesis Testing for Population Proportion
    5.8 Hypothesis Testing for Population Proportion
    
    5.8.1 Hypothesis Testing for Population Proportion
    
    5.8.2 Examples of Hypothesis Testing for Population Proportion
  - 5.9 Hypothesis Testing for Population Variance
    5.9 Hypothesis Testing for Population Variance
    
    5.9.1 Hypothesis Testing for Population Variance
    
    5.9.2 Summary of Hypothesis Testing for Population Variance
- Module 6 Association between Random Variables and Simple Linear Regression
  Module 6 Association between Random Variables and Simple Linear Regression
  - 6.1 Covariance and Correlation
    6.1 Covariance and Correlation
    
    6.1.1 Explaining Covariance
    
    6.1.2 Correlation
    
    6.1.3 Covariance vs Correlation
  - 6.2 Simple Linear Regression
    6.2 Simple Linear Regression
    
    6.2.1 Simple Linear Regression - I
    
    6.2.2 Simple Linear Regression - II
  - 6.3 Application of Steps in Simple Linear Regression
    6.3 Application of Steps in Simple Linear Regression
    
    6.3.1 Application of Steps in Simple Linear Regression
    
    6.3.2 SLR - Coefficient of Determination
    
    6.3.3 SLR - Correlation Coefficient v-s Coefficient of Determination
    
    6.3.4 SLR - Assumptions on Error Term
    
    6.3.5 SLR - Test of Significance
    
    6.3.6 SLR - Residual Analysis
    
    6.3.7 SLR - Test of Significance and Residual Analysis
  - 6.4 SLR Application using Excel
    6.4 SLR Application using Excel
    
    6.4.1 SLR - Application using Examples - I
    
    6.4.2 SLR - Application using Examples - II
- Module 7 Multiple Linear Regression I
  Module 7 Multiple Linear Regression I
  - 7.1 Multiple Linear Regression
    7.1 Multiple Linear Regression
    
    7.1.1 Multiple Linear Regression
    
    7.1.2 MLR - OLS
    
    7.1.3 MLR - Coefficient of Determination
    
    7.1.4 MLR - Test of Significance using F-Test and t-Test
    
    7.1.5 MLR - Multicollinearity
    
    7.1.6 MLR - Residual Analysis
    
    7.1.7 MLR - Outliers and Influential Observations
  - 7.2 Recap
    7.2 Recap
    
    7.2.1 Recap
    
    7.2.2 MLR - Application Recap
  - 7.3 Applications using Excel
    7.3 Applications using Excel
    
    7.3.1 Applications with Examples - I
    
    7.3.2 Applications with Examples - II
    
    7.3.3 Applications with Examples - III
    
    7.3.4 Applications with Examples - IV
    
    7.3.5 Applications with Examples - V
  - 7.4 Multiple Linear Regression II
    7.4 Multiple Linear Regression II
    
    7.4.1 MLR - Intercept and OVB
- Module 8 Multiple Linear Regression II
  Module 8 Multiple Linear Regression II
  - 8.1 Introduction to MLR with Categorical Variables
  - 8.1 MLR with Categorical Variables
    8.1 MLR with Categorical Variables
    
    8.1.1 MLR: Categorical Variables
  - 8.2 Interpretation of Coefficients
    8.2 Interpretation of Coefficients
    
    8.2.1 Interpretation of Coefficients
  - 8.3 Examples of Categorical Variables
    8.3 Examples of Categorical Variables
    
    8.3 Examples of Categorical Variables
  - 8.4 Interaction Variables
    8.4 Interaction Variables
    
    8.4 Interactive Variables
Creative
Creative
- Important Questions
- Podcast
- Module 01 The Creativity Imperative
  Module 01 The Creativity Imperative
- Module 02 Anatomy and Psychology of Creativity
  Module 02 Anatomy and Psychology of Creativity
- Module 03 Problem Discovery and Definition
  Module 03 Problem Discovery and Definition
- Module 04 Problem Solving and Ideation Methods
  Module 04 Problem Solving and Ideation Methods
EE
EE
- Cheatsheets
- Podcast
- Module 1 Course Overview
  Module 1 Course Overview
- Module 2 Opportunity based and Necessity based Entrepreneurs
  Module 2 Opportunity based and Necessity based Entrepreneurs
  - Necessity-Based Entrepreneurship
  - Opportunity-Based Entrepreneurship
- Module 3 Social Entrepreneurship and Resources
  Module 3 Social Entrepreneurship and Resources
  - Social Entrepreneurship: Doing Well by Doing Good
  - Entrepreneurial Resources: Fueling Your Startup Journey
- Module 4 Starting Your Venture
  Module 4 Starting Your Venture
Eng
Eng
- Podcast
FSBP
FSBP
- Module 1 Introduction to Financial Accounting and Mechanics of Accounting
  Module 1 Introduction to Financial Accounting and Mechanics of Accounting
- Module 2 Preparation of Profit and Loss Account Balance Sheet and Cash Flow Statement
  Module 2 Preparation of Profit and Loss Account Balance Sheet and Cash Flow Statement
- Module 3 Accounting Concepts
  Module 3 Accounting Concepts
- Module 4 Revenue Recognition
  Module 4 Revenue Recognition
- Module 5 Inventory Accounting and Valuation
  Module 5 Inventory Accounting and Valuation
- Module 6 Fixed Assets and Depreciation Accounting
  Module 6 Fixed Assets and Depreciation Accounting
- Module 7 Reading Financial Statements
  Module 7 Reading Financial Statements
- Module 8 Assessing Business Performance through Financial Statements
  Module 8 Assessing Business Performance through Financial Statements
MF
MF
- Podcast
- Important Questions
  Important Questions
- Module 01 Basics of Marketing
  Module 01 Basics of Marketing
  - 1.1 Understanding Markets
    1.1 Understanding Markets
    
    What is a Market?
    
    Types of Markets
  - 1.2 Introduction to Marketing
    1.2 Introduction to Marketing
    
    The Social View on Marketing
    
    Introducing Maslow’s Hierarchy of Needs
    
    Satisfying Customer Needs and Wants
    
    Simplified Understanding of Marketing
  - 1.3 The Value Framework
    1.3 The Value Framework
    
    The Value Framework
  - 1.4 The Evolution of Marketing
    1.4 The Evolution of Marketing
    
    The Evolution of Marketing
  - 1.5 The Marketing Management
    1.5 The Marketing Management
    
    The Language of Marketing
    
    1.5.2 Introduction to the Marketing Management
    
    1.5.3 What can be Marketed?
    
    1.5.4 Social Marketing and Relationship Marketing
  - 1.6 Career Options in Marketing
    1.6 Career Options in Marketing
    
    1.6.1 Career options in Marketing
  - 1.7 Case Study Coca Cola
    1.7 Case Study Coca Cola
    
    1.7.2 Coca Cola - The origin
    
    1.7.3 Coca-Cola's Expansion (Part 1)
    
    1.7.4 The decline of Coca Cola
    
    1.7.5 Coca Cola - Rivalry Begins (Part 1)
    
    1.7.6 Coca Cola Case Study - Defining Value for the Customers
    
    1.7.7 The Rivalry - Coca Cola's downfall
    
    1.7.8 Coca Cola's Revival
    
    1.7.9 Conclusion of the case
- Module 02 Identifying Customers Segmentation and Targeting
  Module 02 Identifying Customers Segmentation and Targeting
  - 2.1 Identifying Target Market
    2.1 Identifying Target Market
    
    2.1.1 Identifying Target Market - Coke Ad 1
    
    2.1.2 Identifying Target Market - Coke Ad 2
    
    2.1.3 Identifying Target Market - Coke Ad 3
  - 2.2 Market Segmentation
    2.2 Market Segmentation
    
    2.2.1 Understanding STP
    
    2.2.2 Market Segmentation
    
    2.2.3 Differentiated and Undifferentiated Marketing
    
    2.2.4 Niche marketing
    
    2.2.5 Bases of Segmentation
  - 2.3 Targeting Market
    2.3 Targeting Market
    
    2.3.1 Targeting Markets
    
    2.3.2 Segment Size and Attrativeness
    
    2.3.3 Targeting Strategies
- Module 03 Positioning and Marketing Strategy
  Module 03 Positioning and Marketing Strategy
  - 3.1 Differentiation
    3.1 Differentiation
    
    3.1.1 The Foundation of Effective Positioning
  - 3.2 Positioning
    3.2 Positioning
    
    3.2.1 Executing Positioning
    
    3.2.2 Crafting an Impactful Positioning Statement
    
    3.2.3 Mastering Perceptual Mapping
  - 3.3 Market Analysis
    3.3 Market Analysis
    
    3.3.1 Beyond STP: Evaluating Market Size for Effective Marketing Strategy Development
    
    3.3.2 Understanding Market Share: Assessing Demand and Analyzing Market Patterns
  - 3.4 Corporate Strategy
    3.4 Corporate Strategy
    
    3.4.1 Exploring Corporate Strategy: The Foundation for Effective Marketing Approaches
    
    3.4.2 Unpacking the BCG Matrix
    
    3.4.3 Strategic Planning Essentials
    
    3.4.4 Functioning of the Organizational Strategy
  - 3.5 Marketing Strategy
    3.5 Marketing Strategy
    
    3.5.1 Foundations of Marketing Strategy: Concepts, Processes, and Key Elements
    
    3.5.2 Deep Dive into the 5Cs: Essential Elements of Marketing Strategy
  - 3.6 Case Study Coca Cola
    3.6 Case Study Coca Cola
    
    3.6.1 Case Study - Coca Cola
- Module 04 Understanding Consumer Behaviour
  Module 04 Understanding Consumer Behaviour
  - 4.1 Concept of Consumer Behaviour
    4.1 Concept of Consumer Behaviour
    
    4.1.1 Exploring Consumer Behaviour
    
    4.1.2 Delving Deeper into Consumer Behaviour: Understanding the Definition
  - 4.2 Making Purchase Decisions
    4.2 Making Purchase Decisions
    
    4.2.1 Understanding Consumer Roles in Decision-Making
    
    4.2.2 Exploring Consumer Types and Their Influence on Purchase Decisions
  - 4.3 A Detailed Guide to the Consumer Decision Making Process
    4.3 A Detailed Guide to the Consumer Decision Making Process
    
    4.3.1 Need Recognition
    
    4.3.2 Pre-purchase Information Search
    
    4.3.3 Evaluation of Alternatives
    
    4.3.4 The Purchase Decision
    
    4.3.5 Post Purchase Behaviour
  - 4.4 Factors affecting Consumer Behaviour
    4.4 Factors affecting Consumer Behaviour
    
    4.4.1 Decoding Consumer Behaviour: The Factors That Shape Our Choices
  - 4.5 Case Study. Kapoors buy a Car
    4.5 Case Study. Kapoors buy a Car
    
    4.5.1 Case Study Kapoors buy a car
    
    4.5.2 Analyzing Family Dynamics in Consumer Decision-Making: The Kapoor Family Case Study
    
    4.5.3 Understanding Individual Contributions in Family Purchase Decisions
- Module 05 Business Buying Behaviour and Strategy
  Module 05 Business Buying Behaviour and Strategy
  - 5.1 Introduction to B2B Markets
    5.1 Introduction to B2B Markets
    
    5.1.1 Introduction to B2B Markets
  - 5.2 A Comparative Analysis of Business vs Consumer Markets
    5.2 A Comparative Analysis of Business vs Consumer Markets
    
    5.2.1 A Comparative Analysis of B2B and B2C Markets
  - 5.3 Value Proposition in Business Markets
    5.3 Value Proposition in Business Markets
    
    5.3.1 Value Proposition in Business Markets
  - 5.4 Segmentation in Business Markets
    5.4 Segmentation in Business Markets
    
    5.4.1 STP in Business Markets
  - 5.5 Buying Benefits and Strategies in Business Markets
    5.5 Buying Benefits and Strategies in Business Markets
    
    5.5.1 Buying Benefits and Strategies in Business Markets
  - 5.6 Purchase Process in Business Markets
    5.6 Purchase Process in Business Markets
    
    5.6.1 Purchase Process in Business Markets
  - 5.7 Types of Buyers in Business Markets
    5.7 Types of Buyers in Business Markets
    
    5.7.1 Types of Buyers in Business Markets
  - 5.8 Case study Mediquip
    5.8 Case study Mediquip
    
    5.8.1 Mediquip Case Study: Navigating the B2B Sales Process
    
    5.8.3 Mediquip Sales Dynamics
- Module 06 Strategizing Products and Services
  Module 06 Strategizing Products and Services
  - 6.1 Product Concept
    6.1 Product Concept
    
    6.1.1 Product - A Conceptual Framework
    
    6.1.2 Classification of Consumer Goods
  - 6.2 Product Line Management
    6.2 Product Line Management
    
    6.2.1 Product Line Management
    
    6.2.2 Product Line Strategies
  - 6.3 Branding
    6.3 Branding
    
    6.3.2 Branding : The Conceptual Framework
    
    6.3.3 Roles of a brand
    
    6.3.4 Building strong brands: Equity, Culture & Value
    
    6.3.5 Building strong brand culture
    
    6.3.6 Building effective brand equity
    
    6.3.7 Generating high brand value
    
    6.3.8 Measuring the success of a brand
  - 6.4 Product Life Cycle (PLC)
    6.4 Product Life Cycle (PLC)
    
    6.4.1 Introduction to PLC
    
    6.4.2 Product Life Cycle : Conceptual Framework
    
    6.4.3 Product Introduction Phase
    
    6.4.4 Market Growth Phase
    
    6.4.5 Maturity Phase
    
    6.4.6 Market Decline Phase
  - 6.5 Service Concept
    6.5 Service Concept
    
    6.5.2 Evolution of Services
    
    6.5.3 Defining Services
    
    6.5.4 Product - Service Mix
    
    6.5.5 Characteristics of Service
    
    6.5.6 Service strategy
  - 6.6 Case Study Santoor
    6.6 Case Study Santoor
    
    6.6.2 Santoor's evolution
    
    6.6.3 Santoor's Repositioning Story : Using Ads Journey
- Module 07 Strategizing Pricing and Distribution
  Module 07 Strategizing Pricing and Distribution
  - 7.1 Price Mix
    7.1 Price Mix
    
    7.1.1 Framework of Pricing
    
    7.1.2 The Language of Pricing
    
    7.1.3 Price Setting Process
    
    7.1.4 Methods of Pricing
    
    7.1.5 Price Discrimination
  - 7.2 Distribution Mix
    7.2 Distribution Mix
    
    7.2.2 Distribution Channels
    
    7.2.3 The Language in Distribution
  - 7.3 Intermediaries in Distribution
    7.3 Intermediaries in Distribution
    
    7.3.1 Types of wholesaling intermediaries
    
    7.3.2 Types of Retailing Intermediaries
    
    7.3.3 Functions of Distribution Channels
  - 7.4 Distribution Channel Decisions
    7.4 Distribution Channel Decisions
    
    7.4.1 Channel Length
    
    7.4.2 Channel Breadth
    
    7.4.3 Channel Design Process
    
    7.4.4 Selecting Distribution Channel
    
    7.4.5 Managing Distribution Channels
    
    7.4.6 Horizontal VS Vertical Channels
    
    7.4.7 Omni-Channel and Multi-Channel Retailing
  - 7.5 Retailing
    7.5 Retailing
    
    7.5.1 Introduction to Retailing
    
    7.5.2 Roles and Responsibilities in Retailing
    
    7.5.3 Characteristics of Retailing
    
    7.5.4 Retail Formats
  - 7.6 D'Light Case Study
    7.6 D'Light Case Study
    
    7.6.1 Introduction to D'Light
    
    7.6.2 D'Light Analysis
    
    7.6.3 Market Context for D'Light
    
    7.6.4 Distribution Channels for D'Light
    
    7.6.6 Strategy for D'Light
- Module 08 Strategizing Promotions
  Module 08 Strategizing Promotions
  - 8.6.1 Digital Marketing : An Overview
  - 8.1 Introduction to Promotion
    8.1 Introduction to Promotion
    
    8.1.1 Introduction to Promotion
  - 8.2 Marketing Communications
    8.2 Marketing Communications
    
    8.2.1 Macro Communication
    
    8.2.2 Micro Communication
    
    8.2.3 Tools and Techniques of Effective Communication
    
    8.2.4 Sales Promotions
    
    8.2.5 Developing Effective Ad Campaigns
    
    8.2.6 Ideal Ad Campaigns : Objectives of Ad Communications
    
    8.2.7 Designing Marketing Communications
  - 8.3 Budgeting
    8.3 Budgeting
    
    8.3.1 Budgeting
  - 8.4 Personal Selling
    8.4 Personal Selling
    
    8.4.1 Personal Selling
    
    8.4.2 Sales Process in B2B Market
    
    8.4.3 Sales Process in B2C Market
  - 8.5 Sales Force Management
    8.5 Sales Force Management
    
    8.5.1 Designing Effective Sales Force
    
    8.5.2 Managing the Sales Force

4.3.1 Analysis of Sample Data

Introduction¶

Purpose: After learning about different sampling methods, this section focuses on the analysis of the sample data collected through these methods. The goal is to estimate various population parameters such as mean, standard deviation, and proportion based on sample data.

Understanding Population Parameters¶

Population Mean (μ): The average value for a particular characteristic within the entire population.
Population Standard Deviation (σ): Measures the spread or variability of the data within the population.
Population Proportion (P): The proportion of the population that meets a specific criterion.

Example of Data Analysis¶

Context: Basavaraju's chroma store customer satisfaction data.
Data Collected: Information on customer satisfaction, including brand, price, features like RAM, hard disk, operating system, and a satisfaction score from 0 to 100.

Descriptive Statistics¶

Mean: Calculated as the average of the satisfaction scores.
Median and Mode: Provides a middle value and the most frequently occurring value in the data, respectively.
Standard Deviation: Indicates how much the satisfaction scores vary from the mean.
Sample Size: Consists of data from 36 respondents.

Estimating Population Parameters¶

Estimation Process:
Sample Mean (x̄): Used as an estimator for the population mean, calculated from the sample data.
Sample Standard Deviation (S): Used to estimate the population standard deviation.
Sample Proportion (P̄): Estimated proportion of customers scoring above a satisfaction threshold (e.g., 60 out of 100).

Discussion on Sample Statistics¶

Importance: Sample statistics like mean, median, mode, and standard deviation help infer the population's characteristics from the sample.
Variability and Confidence: Discusses the variability in point estimates and the importance of constructing confidence intervals to understand how estimates may vary from actual population parameters.

Conclusion¶

Goal of Analysis: The primary objective is to use sample data to make inferences about the entire population, demonstrating how statistical tools can bridge the gap between a sample and the population it represents.

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