AS Statistics

By Mary Brace & Anthony Eccles, et al

June 2007

Pearson Education

ISBN: 9780340940525

512 pages, Illustrated, 7 1/2" x 9 3/4"

$77.50 Paper Original

This accessible, comprehensive textbook is designed to support any student studying AS Statistics. This book covers all three AS modules: Z1, Z2 and Z3.

It has been designed especially for students with a non-mathematical background, but who nevertheless will need to understand some mathematical concepts when studying their other A levels. These students include those following Business Studies, Psychology, Geography and Biology courses.

This book is the only book supporting MEI AS Statistics and has all the benefits of being part of the MEI series:

Accessible both in design and content

Worked examples guide students into new

Topics and concepts within real world contexts (particularly important for these candidates)Activities, investigations and graded exercises

The quality assurance of MEI

Full support from the MEI network

In addition, there is an IT investigation at the end of each chapter.

Table of Contents:

Introduction

Key to symbols in this book

Unit 1

Exploring data

Looking at the data

Stem-and-leaf diagrams

Categorical or qualitative data

Numerical or quantitative data

Measures of central tendancy

Frequency distributions

Grouped data

Measures of spread

Linear coding

Data presentation and related measures of centre and spread

Bar charts and vertical line charts

Pie charts

Histograms

Measures of central tendancy and of spread using quartiles

Cumulative frequency curves

Probability

Measuring probability

Estimating probability

Expectation

The probability of either one event or another

The probability of events from two trials

Conditional probability

Discrete random variables

Discrete random variables

Expectation and variance

Further probability

Factorials

Permutations

Combinations

The bonomial coefficients, nCr

Calculating probabilities in less simple cases

The binomial distribution

The binomial distribution

The expectation of B(n, p)

Using the binomial distribution

Does the binomial distribution really work?

Hypothesis testing using the binomial distribution

Defining terms

Hypothesis testing checklist

Choosing the significance level

Critical values and critical regions

1-tailed and 2-tailed tests

Asymmetrical cases

Unit 2

The Poisson distribution

Conditions for modelling data with a Poisson distribution

The sum of two or more Poisson distributions

The Poisson approximation to the binomial distribution

The Normal distribution

The key features of a Normal distribution

The sum and difference of Normal variables

The chi-squared test

The X2 test for association

Degrees of freedom

Goodness of fit tests

Using the Normal distribution to interpret sample data

A hypothesis test for the mean using the Normal distribution

Confidence intervals for a population mean

Small samples and the t distribution

The t distribution

Confidence intervals from small samples

The Wilcoxon signed rank test

The Wilcoxon signed rank test for a single sample

Unit 3

Sampling and experimental design

Sampling

Experiments and surveys

Sampling methods

Experimental design

Design of experiments

Hypothesis tests on paired samples

Paired and unpaired experiments

The paired-sample t test

The Wilcoxon signed rank test for paired samples

Hypothesis tests on unpaired samples

Selecting the appropriate test for unpaired samples

The Normal test for unpaired samples

The t test for unpaired samples

The Wilcoxon rank sum test

Correlation

Bivariate data

Interpreting scatter diagrams

Pearson's product moment correlation coefficient

The meaning of a sample correlation coefficient

Interpreting correlation

Rank correlation

Spearman's rank correlation coefficient

Appendices

The derivation of the alternative form of the sum of squares, Sxx

The binomial theorem

Answers

Index

Return to main page of Trans-Atlantic Publications