ISBN 9780340808443,An Introduction To Statistical Analysis For Business And Industry: A Problem Solving Approach

An Introduction To Statistical Analysis For Business And Industry: A Problem Solving Approach

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ISBN 9780340808443

Hodder Stoughton Educational

Publication Year 2009

ISBN 9780340808443

ISBN-10 0340808446


Number of Pages 397 Pages
Language (English)

Management & management techniques

Contents : Preface
Chapter 1. Introduction
Learning Objectives
1.1 Sales and Shortages in a sports and social club
1.2 Statistical variation in the stock Exchange
1.3 Variation in manufacturing: charts, graphs and stratification as aids to understanding
1.4 Chance and assignable causes of variation, the Normal model for chance variation and frequency distribution
1.5 Modelling assignable causes; relations between variables
1.6 Introducing the multiple regression model for forecasting postage stamp sales; a case study in problem formulation
1.7 A strategic forecasting problem
1.8 Researching youth technology markets; sample surveys
1.9 Statistical assessment of a process change; design issues in experimentation
Chapter 2. Data display and summary
Learning objectives
2.1 Graphical display of measured data
2.2 Numerical summaries of measured data
2.3 How to read a table
2.4 Graphical display of summary data
2.5 Review exercises
Chapter 3. The Normal model for chance variation
Learning objectives
3.1 Calculating non-conformance rates
3.2 Using the Normal model; the standard Normal distribution
3.3 Normal model calculations using Excel
3.4 'What-if' analysis
3.5 Models and data; parameters and statistics
3.6 Checking the Normal model; the Normal diagnostic plot
3.7 Review exercises
3.8 Laboratory exercise: 'What-if' analysis for non-conformance rates
Chapter 4. Process Monitoring, Charts and Statistical Inference
Learning objectives
4.1 Process monitoring using control charts
4.2 Mean and range charts
4.3 The statistical basis for x-bar charts; the sampling distribution of the mean
4.4 Assessing process capability; statistical estimation
4.5 Monitoring counts
4.6 Review exercises
4.7 Laboratory exercise: Estimation sigma from data with possible exceptional cases
Chapter 5. Principles of Statistical Inference
Learning objectives
5.1 Statistical significance testing
5.2 False alarm rates and significance levels
5.3 Detection rates and power
5.4 Confidence intervals
5.5 Review exercises
5.6 Laboratory exercise: Simulating sampling distributions, significance tests and confidence intervals
Chapter 6. Simple linear regression
Learning objectives
6.1 The simple linear regression model
6.2 The regression control chart
6.3 Reporting the results of simple linear regression
6.4 Correlation
6.5 Pitfalls with regression and correlation
6.6 Review exercises
6.7 Laboratory exercise: Exploring and explaining computer maintenance costs
Chapter 7. Frequency data analysis
Learning objectives
7.1 A market penetration study
7.2 Statistical inference for frequency data
7.3 The chi-square test
7.4 Multiply classified frequency data
7.5 Review exercises
7.6 Laboratory exercise: Frequency data analysis
Chapter 8. Multiple linear regression
Learning objectives
8.1 A prediction problem
8.2 The multiple linear regression model
8.3 Regression diagnostics
8.4 Iterating the analysis
8.5 Alternative measures of goodness of fit
8.6 Outlining the model fitting and testing procedure
8.7 Another application: the stamp sales case study
8.8 General issues
8.9 Review exercises
8.10 Laboratory exercise: Prediction of meter sales
Chapter 9. Time series
Learning objectives
9.1 A sales forecasting problem
9.2 Modelling trend and seasonality
9.3 Smoothing for exploratory analysis
9.4 Forecasting with smoothers; the Holt-Winters method
9.5 Advanced time series analysis
9.6 Review exercises
9.7 Laboratory exercise: Housing completions
Chapter 10. Simple statistical models in finance
10.1 The Random walk model for variation in the stock Exchange
10.2 Risk and return
10.3 The capital asset pricing model
10.4 Evaluating B
10.5 Testing the CAPM
10.6 Laboratory exercise: Statistical analysis of a portfolio of stocks
Chapter 11. Data production; surveys, experiments, archives
Learning objectives
11.1 Sample surveys
11.2 The survey process
11.3 Errors in surveys
11.4 Observational studies and experimentation
11.5 Multi-factor experiments
11.6 Implementing experiments
11.7 Archive data
Chapter 12. Statistical analysis in context
Learning objectives
12.1 The management context
12.2 The role of mathematics
12.3 The role of computing
12.4 Extensions and developments
12.5 Conclusions