Statistics for Economics, Accounting, and Business Studies is an accessible introduction to the subject, featuring a plethora of features and recent examples that will offer your students the resources they need to excel in their course.

1. Descriptive statistics
   • Learning outcomes
   • Introduction
   • Summarising data using graphical techniques
   • Looking at cross-section data: wealth in the UK in 2005
   • Summarising data using numerical techniques
   • The box and whiskers diagram
   • Time-series data: investment expenditures 1977–2009
   • Graphing bivariate data: the scatter diagram
   • Data transformations
   • The information and data explosion
   • Writing statistical reports
   • Guidance to the student: how to measure your progress
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
   • Appendix 1A: S notation
   • Problems on S notation
   • Appendix 1B: E and V operators
   • Appendix 1C: Using logarithms
   • Problems on logarithms
   • References
2. Probability
   • Learning outcomes
   • Probability theory and statistical inference
   • The definition of probability
   • Probability theory: the building blocks
   • Bayes’ theorem
   • Decision analysis
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
3. Probability distributions
   • Learning outcomes
   • Introduction
   • Random variables
   • The Binomial distribution
   • The Normal distribution
   • The distribution of the sample mean
   • The relationship between the Binomial and Normal distributions
   • The Poisson distribution
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
4. Estimation and confidence intervals
   • Learning outcomes
   • Introduction
   • Point and interval estimation
   • Rules and criteria for finding estimates
   • Estimation with large samples
   • Precisely what is a confidence interval?
   • Estimation with small samples: the t distribution
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
   • Appendix: Derivations of sampling distributions
5. Hypothesis testing
   • Learning outcomes
   • Introduction
   • The concepts of hypothesis testing
   • The Prob-value approach
   • Significance, effect size and power
   • Further hypothesis tests
   • Hypothesis tests with small samples
   • Are the test procedures valid?
   • Hypothesis tests and confidence intervals
   • Independent and dependent samples
   • Issues with hypothesis testing
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
6. The c² and F distributions
   • Learning outcomes
   • Introduction
   • The c² distribution
   • The F distribution
   • Analysis of variance
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
   • Appendix: Use of c² and F distribution tables
7. Correlation and regression
   • Learning outcomes
   • Introduction
   • What determines the birth rate in developing countries?
   • Correlation
   • Regression analysis
   • Inference in the regression model
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
   • References
8. Multiple regression
   • Learning outcomes
   • Introduction
   • Principles of multiple regression
   • What determines imports into the UK?
   • Finding the right model
   • Chapter summary
   • Key terms and concepts
   • Problems
   • Answers to Exercises
   • References
9. Data collection and sampling methods
   • Learning outcomes
   • Introduction
   • Using secondary data sources
   • Collecting primary data
   • Random sampling
   • Calculating the required sample size
   • Collecting the sample
   • Case study: the UK Living Costs and Food Survey
   • Chapter summary
   • Key terms and concepts
   • Problems
   • References
10. Index numbers
    • Learning outcomes
    • Introduction
    • A simple index number
    • A price index with more than one commodity
    • Using expenditures as weights
    • Quantity and expenditure indices
    • The Consumer Price Index
    • Discounting and present values
    • Inequality indices
    • The Lorenz curve
    • The Gini coefficient
    • Concentration ratios
    • Chapter summary
    • Key terms and concepts
    • Problems
    • Answers to Exercises
    • Appendix: deriving the expenditure share form of the
    • Laspeyres price index
    • References
11. Seasonal adjustment of time series data
    • Learning outcomes
    • Introduction
    • The components of a time series
    • Isolating the trend
    • Isolating seasonal factors
    • Seasonal adjustment
    • An alternative method for finding the trend
    • Forecasting
    • Further issues
    • Chapter summary
    • Key terms and concepts
    • Problems
    • Answers to Exercises

Statistics for Economics, Accounting and Business Studies

Seventh Edition

Michael Barrow

“This is the best available text … for a first-year statistics course in economics” Emiliya Lazarova, University of East Anglia

Do you need to brush up on your statistical skills to truly excel in your economics or business course? If you want to increase your confidence in statistics then this is the perfect book for you. Statistics for Economics, Accounting and Business Studies presents a user-friendly and concise introduction to a variety of statistical tools and techniques which can be used to solve real-life problems.

New features:

·    New section on how to write statistical reports

·    Examples of good and bad graphs, and how to improve them

·    Illustration of graphing regression coefficients as a means of presentation

·    Probablity chapter expanded to improve exposition

·    More discussion and critique of hypothesis testing.

·    New Companion Website for students including quizzes to test your knowledge and Excel data files

Michael Barrow is a Senior Lecturer in Economics at the University of Sussex. He has acted as a consultant for major, industrial, commercial and government bodies

• Boxes highlight interesting issues and common mistakes, breaking up the text and keeping students engaged.
• Real-life worked examples reinforce practical learning using up-to-date real data.
• A focus on computing in statistics using industry-based software illustrates how the use of spreadsheets can solve problems, helping students think like a statistician.

• Chapter introductions set the scene and link the chapters together.
• Learning outcomes highlight the learning objectives of the chapter.
• Problems at the end of the chapter range in difficulty, providing a more in-depth practice of topics.

• New section in Chapter 1 examines how to write statistical reports.
• A demonstration of good and bad graphs, and how to improve them.
• Illustrations of graphing regression coefficients as a means of presentation.
• Expanded content in Chapter 2 makes exposition of probability clearer.
• Extended discussion and critique of hypothesis testing.