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:



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






Measures of central tendancy and of spread using quartiles



Cumulative frequency curves





Measuring probability



Estimating probability






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












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






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





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




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



The binomial theorem




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