Boddy R., Smith G. Statistical Methods in Practice: for Scientists and Technologists

Chicheste: John Wiley & Sons, Ltd., 2009. — 236 p. — ISBN: 0470746645, 9780470746646This is a practical book on how to apply statistical methods successfully. The Authors have deliberately kept formulae to a minimum to enable the reader to concentrate on how to use the methods and to understand what the methods are for. Each method is introduced and used in a real situation from industry or research. Each chapter features situations based on the authors' experience and looks at statistical methods for analysing data and, where appropriate, discusses the assumptions of these methods. Key features: Provides a practical hands-on manual for workplace applications. Introduces a broad range of statistical methods from confidence intervals to trend analysis. Combines realistic case studies and examples with a practical approach to statistical analysis.Samples and populationsWhat a lottery!
No can do
Nobody is listening to me
How clean is my river?
Discussion
What is the true mean?Presenting data
Averages
Measures of variability
Relative standard deviation
Degrees of freedom
Confidence interval for the population mean
Sample sizes
How much moisture is in the raw material?
Problems
Exploratory data analysisHistograms: is the process capable of meeting specifications?
Box plots: how long before the lights go out?
The box plot in practice
Problems
Significance testingThe one-sample t –test
The significance testing procedure
Confidence intervals as an alternative to significance testing
Confidence interval for the population standard deviation
F-test for ratio of standard deviations
Problems
The normal distributionProperties of the normal distribution
Example
Setting the process mean
Checking for normality
Uses of the normal distribution
Problems
Tolerance intervalsExample
Confidence intervals and tolerance intervals
OutliersGrubbs’ test
Warning
Significance tests for comparing two meansExample: watching paint lose its gloss
The two-sample t -test for independent samples
An alternative approach: a confidence intervals for the difference between population means
Sample size to estimate the difference between two means
A production example
Confidence intervals for the difference between the two suppliers
Sample size to estimate the difference between two means
Conclusions
Problems
Significance tests for comparing paired measurementsComparing two fabrics
The wrong way
The paired sample t -test
Presenting the results of significance tests
One-sided significance tests
Problems
Regression and correlationObtaining the best straight line
Confidence intervals for the regression statistics
Extrapolation of the regression line
Correlation coefficient
Is there a significant relationship between the variables?
How good a fit is the line to the data?
Assumptions
Problems
The binomial distributionExample
An exact binomial test
A quality assurance example
What is the effect of the batch size?
Problems
The Poisson distributionFitting a Poisson distribution
Are the defects random? The Poisson distribution
Poisson dispersion test
Confidence intervals for a Poisson count
A significance test for two Poisson counts
How many black specks are in the batch?
How many pathogens are there in the batch?
Problems
The chi-squared test for contingency tablesTwo-sample test for percentages
Comparing several percentages
Where are the differences?
Assumptions
Problems
Non-parametric statisticsDescriptive statistics
A test for two independent samples: Wilcoxon–Mann–Whitney test
A test for paired data: Wilcoxon matched-pairs sign test
What type of data can be used?
Example: cracking shoes
Problems
Analysis of variance: Components of variabilityOverall variability
Analysis of variance
A practical example
Terminology
Calculations
Significance test
Variation less than chance?
When should the above methods not be used?
Between- and within-batch variability
How many batches and how many prawns should be sampled?
Problems
Cusum analysis for detecting process changesAnalysing past data
Intensity
Localised standard deviation
Significance test
Yield
Conclusions from the analysis
Problem
Rounding of resultsChoosing the rounding scale
Reporting purposes: deciding the amount of rounding
Reporting purposes: rounding of means and standard deviations
Recording the original data and using means and standard deviations in statistical analysisSolutions to Problems
Statistical Tables