Business Statistics : An Applied Orientation

Business Statistics An Applied Orientation

Business Statistics: An Applied Orientation provides with a conceptual framework of business, develops skills in applying concepts into decision situations, and helps understand the nitty-gritty of business statistics. This book will also be useful to professionals who would like to acquire basic kn...

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Detalles Bibliográficos
Autor principal: Vishwanathan, P. K. (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Noida : Pearson India 2006.
Edición:1st ed
Materias:
Commercial statistics.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009815725106719
Tabla de Contenidos:
  • Cover
  • About the Author
  • Foreword
  • Preface
  • Contents
  • Chapter 1: An Overview of Statistics
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 1.1 Why Should I Study Statistics?
  • 1.2 What is Statistics?
  • 1.3 Some Typical Application Areas
  • Quality Management
  • Finance
  • Materials Management
  • Marketing
  • 1.4 Types of Statistics
  • Example for Descriptive Statistics
  • Example for Inferential Statistics
  • 1.5 Some Key Terms and Definitions
  • Population (Universe)
  • Sample
  • Variable
  • Parameter
  • Statistic
  • 1.6 Types of Data
  • 1.7 Data Measurement Scales
  • 1.8 Sources of Data
  • 1.9 Step-by-Step Approach to Statistical Investigation
  • 1. Problem Identification
  • 2. Objectives of the Study
  • 3. Type of Study
  • 4. Sampling Plan
  • 5. Data Collection
  • 6. Data Analysis and Interpretations
  • 7 Findings of the Study
  • 1.10 Chapter Summary
  • Glossary
  • Review Questions
  • Case Study-Savvy Fast Food
  • Questions
  • Answers to Review Questions
  • Chapter 2: Classifying Data to Convey Meaning
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 2.1 Meaning and Examples of Raw Data
  • 2.2 Frequency Distribution
  • Guidelines for Constructing a Frequency Distribution Table
  • Construction of a Frequency Distribution - An Example
  • 2.3 Histogram
  • Uses of Histogram
  • Computer and Histogram
  • Some Fine Tuning
  • Some Fine Tuning
  • 2.4 Cumulative Frequency Distribution and Ogive Curve
  • 2.5 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • 1. Case Study -Waiting Time in ATM Counters
  • 2. Case Study-Shaft Diameter
  • 3. For problems 1) and 2), construct the cumulative distribution curves and give your comments
  • 4. Case Study-Electricity Charges
  • 5. Case Study- Money Spent On Fast Food
  • Chapter 3: Measures of Central Tendency and Dispersion.
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 3.1 Measures of Central Tendency
  • What is Central Tendency?
  • Measures of Central Tendency
  • Arithmetic Mean
  • Solution for Mean
  • Comparative Picture of Mean, Median, Mode
  • 3.2 Measures of Dispersion (Variation)
  • Measures of Dispersion (Spread)
  • 3.3 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • Chapter 4: Probability-A Conceptual Framework
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 4.1 Meaning and Concepts of Probability
  • An Example
  • Progressive Test Question
  • 4.2 Types of Probability
  • Relative Frequency Probability
  • Subjective Probability
  • 4.3 Mutually Exclusive Events
  • 4.4 Independent Events
  • 4.5 Rules for Calculating Probability
  • Example Problem for Addition Rule
  • Solution to the Problem
  • 4.6 Use of Probability Tree
  • Probability Tree-Example Problem
  • Comprehensive Example
  • 4.7 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • Chapter 5: Probability Distributions
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 5.1 What is a Probability Distribution?
  • Another Example for a Probability Distribution
  • 5.2 The Binomial Distribution
  • Example for Mean and Standard Deviation
  • Example from Quality Control Function
  • Questions
  • Solution
  • 5.3 The Poisson Distribution
  • Application in Airlines
  • 5.4 The Normal Distribution
  • Example Problem for the Normal Distribution
  • Critical Thinking Skills
  • The Normal Approximation to the Binomial Distribution
  • 5.5 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • 1. Case Study: Business Statistics Course
  • 2. Case Study: Automobile Components
  • 3. Case Study: Credit Cards
  • 4. Case Study: Motorcar Accidents.
  • Chaptert 6: Basics of Sampling and Sampling Distribution
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 6.1 What is Sampling and Why Do You Need Sampling?
  • Why do you need Sampling?
  • 6.2 Types of Sampling
  • Probability Sampling (Random Sampling)
  • 6.3 Sampling Distribution -A Conceptual Pramework
  • Sampling Distribution -Definition
  • 6.4 The Concept of Standard Error
  • 6.5 Sampling Distribution of the Mean from Normal population
  • 6.6 Sampling Distribution of the Mean - Non-Normal Population
  • Diagram depicting the Central Limit Theorem
  • 6.7 Chapter Summary
  • Glossary
  • Review Questions
  • Mini Case
  • Answers to Review Questions
  • Practice Problems
  • 1. Case Study - Tire Life
  • 2. Case Study - Book Exposure on Students
  • Questions
  • 4. Case Study- Fallacy in Sampling
  • Questions
  • Chapter 7: Estimation
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 7.1 Point Estimation
  • Point Estimation - Population Mean
  • Point Estimation-Population Proportion
  • 7.2 Interval Estimation
  • 7.3 Confidence Interval for Population Mean and Proportion- Large Sample
  • 7.4 Confidence Interval for Population Mean - Small Sample ('t'-Distribution)
  • Characteristics of the t Distribution
  • Confidence Interval for Mean using t Distribution
  • 7.5 How to Determine Sample Size Using Confidence Interval
  • Sample Size Determination - Population Mean
  • Sample Size Determination - Population Proportion
  • 7.6 Chapter Summary
  • Glossar
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • Chapter 8: Hypothesis Testing
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 8.1 Statistical Hypothesis-A Conceptual Framework
  • What is a Statistical Hypothesis?
  • The Type I and Type II Errors
  • 8.2 Hypothesis Testing -Univariate Case (One Sample
  • Hypothesis Testing - Population Mean (Single Mean).
  • P-Value
  • 8.3 Hypothesis Testing -Bivariate Case (Two Sample
  • Hypothesis Testing - Two Population Means
  • 8.4 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • 1. Case Study- New Product Introduction
  • 2. Case Study -Test of Analytical Ability
  • 3. Case Study - Readymade Garment
  • 4. Case Study - Sales Incentive Scheme
  • Chapter 9: Chi-Square Test and Analysis of Variance (ANOVA)
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 9.1 Chi-Square (x2) Analysis-Basics
  • 9.2 Chi-Square Test-Goodness of Fit
  • 9.3 Chi-Square Test of Independence
  • 9.4 ANOVA-Basics
  • 9.5 ANOVA-One-Way Classification
  • How One-Way Classification Works in Practice?
  • Meaning of the formulas
  • 9.6 ANOVA-Two -Way Classification
  • Interpretation of the Results
  • 9.7 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • 2. Case Study- Ointment to Treat Fungus Problem on Human Skin
  • 3. Case Study-Do Color and Size of Package Design Boost the Sales?
  • 4. Case Study-Comparison of Life of Different Brands of Tire
  • Chapter 10: Correlation and Regression
  • Learning Objective
  • Introduction
  • Chapter Outline
  • 10.1 What is Correlation?
  • 10.2 Insights into Correlation
  • Properties of Correlation Coefficient
  • 10.3 Basics of Regression
  • Need for Regression
  • 10.4 Regression Model
  • Historical Perspective
  • How does Simple Linear Regression work in practice?
  • The concept of Coefficient of Determination for Statistical Validity
  • Regression with Pleasure from Microsoft Excel
  • Explanation on the Output
  • Item of interest on the ANOVA output
  • Multiple Linear Regression Model
  • 10.5 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • 2. Case Study-Monthly Sales Forecast.
  • 3. Case Study- Fuel Consumption for Car
  • Question
  • 4. Case Study- Are Sales influenced by Sales Promotion and Advertising?
  • Chapter 11: Decision Analysis
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 11.1 Steps in Systematic Problem Solving
  • Case Study-Product Mix Decision
  • Steps in Systematic Problem Solving Explained for the Case
  • Define and Analyze the Problem
  • Determine a Set of Alternative Solutions
  • Establish Criteria for Evaluating the Alternatives
  • Evaluate the Alternatives
  • Choose the Best Alternative
  • Implement the Best Alternative
  • Evaluate the Results and Check that things are working all right
  • 11.2 How to Structure a Decision Problem
  • Pay off Matrix for the Example:
  • 11.3 Expected Monetary Value (EMV
  • Expected Value of Perfect Information (EVPI)
  • Opportunity Loss Table
  • 11.4 Decision Tree
  • Decision Tree - A comprehensive case problem
  • Case Study-Product Mix to Maximize Expected Contribution
  • Question
  • 11.5 Value of Sample Information
  • 11.6 Chapter Summary
  • Glossary
  • Review Questions
  • Answers to Review Questions
  • Practice Problems
  • Questions
  • Questions
  • 3. Case Study- Launch or not to Launch
  • Questions
  • Chapter 12: Forecasting
  • Learning Objectives
  • Introduction
  • Chapter Outline
  • 12.1 Forecasting-Basics
  • Forecasting Methods in Practice
  • 12.2 Qualitative Methods of Forecasting
  • Methods Used in Qualitative Forecasting
  • 12.3 Quantitative Methods of Forecasting
  • Time Series Analysis
  • Moving Average
  • Moving Average using Microsoft Excel
  • Exponential Smoothing
  • New Forecast = (0.3)(50) + (1-0.3)(55) = 53.5.
  • Spreadsheet Showing Basic Calculations
  • Smooth Exponential Smoothing Using Microsoft
  • Trend Projection
  • Forecasting Using Multiple Regression Model
  • Case Example-Sales Forecasting
  • A Brief Note on Accuracy of Forecast.
  • 12.4 Chapter Summary.

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