Practical Statistics for Field Biology

ISBN-10: 0471982962
ISBN-13: 9780471982968
Edition: 2nd 1998 (Revised)
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Description: An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real life"  More...

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Book details

List price: $59.00
Edition: 2nd
Copyright year: 1998
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/9/1998
Binding: Paperback
Pages: 272
Size: 6.50" wide x 9.75" long x 0.75" tall
Weight: 1.034
Language: English

An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real life" situations such as collecting insects with a sweep net or counting seagulls on a cliff face can hardly be expected to be as reliable or controllable as a laboratory-based experiment. Acknowledging the peculiarities of field-based data and its interpretation, Practical Statistics for Field Biology introduces readers to the principles and methods of statistical analysis allowing them to understand research reports in journals, decide on the most appropriate statistical tests for their own problems and finally to analyse and present their findings. To enhance the usefulness of this book, the new edition incorporates the more advanced method of multivariate analysis, introducing the nature of multivariate problems and describing the techniques of principal components analysis, cluster analysis and discriminant analysis which are all applied to biological examples. An appendix detailing the statistical computing packages available has also been included. Practical Statistics for Field biology will be of interest to undergraduates studying ecology, biology, and earth and environmental sciences. In addition, it will be useful for postgraduates who are not familiar with the application of multivariate techniques and practising field biologists working in these areas. Review of the First Edition "In summary, Practical Statistics for Field Biology is an excellent introductory text for first and second year undergraduate courses in biology." Trends in Ecology & Evolution What the lecturers said about the First Edition: "An excellent introductory guide to statistics." "Applicable examples in an understandable text."

Preface
Introduction
What do we mean by statistics?
Why is statistics necessary?
Statistics in field biology
The limitations of statistics
The purpose of this text
Measurement and Sampling Concepts
Populations, samples and observations
Counting things--the sampling unit
Random sampling
Random numbers
Independence
Statistics and parameters
Descriptive and inferential statistics
Parametric and non-parametric statistics
Processing Data
Scales of measurement
The nominal scale
The ordinal scale
The interval scale
The ratio scale
Conversion of interval observations to an ordinal scale
Derived variables
The precision of observations
How precise should we be?
The frequency table
Aggregating frequency classes
Frequency distribution of count observations
Dispersion
Bivariate data
Presenting Data
Introduction
Dot plot or line plot
Bar graph
Histogram
Frequency polygon and frequency curve
Scattergram (scatter plot)
Circle or pie graph
Measuring the Average
What is an average?
The mean
The median--a resistant statistic
The mode
Relationship between the mean, median and mode
Measuring Variability
Variability
The range
The standard deviation
Calculating the standard deviation
Calculating the standard deviation from grouped data
Variance
An alternative formula for calculating the variance and standard deviation
Obtaining the standard deviation, variance and the sum of squares from a calculator
Degrees of freedom
The coefficient of variation
Probability
The meaning of probability
Compound probabilities
Probability distribution
Models of probability distribution
The binomial probability distribution
The Poisson probability distribution
The negative binomial probability distribution
Critical probability
Probability Distributions as Models of Dispersion
Dispersion
An Index of Dispersion
Choosing a model of dispersion
The binomial model
Poisson model
The negative binomial model
Deciding the goodness of fit
The Normal Distribution
The normal curve
Some mathematical properties of the normal curve
Standardizing the normal curve
Two-tailed or one-tailed?
Small samples: the t-distribution
Are our data 'normal'?
Data Transformation
The need for transformation
The logarithmic transformation
When there are zero counts--the arcsinh transformation
The square root transformation
The arcsine transformation
Back-transforming transformed numbers
Is data transformation really necessary?
How Good are our Estimates?
Sampling error
The distribution of a sample mean
The confidence interval of the mean of a large sample
The confidence interval of the mean of a small sample
The confidence interval of the mean of a sample of count data
The difference between the means of two large samples
The difference between the means of two small samples
Estimating a proportion
Estimating a Lincoln Index
Estimating a diversity index
The distribution of a variance--chi-square distribution
The Basis of Statistical Testing
Introduction
The experimental hypothesis
The statistical hypothesis
Test statistics
One-tailed tests and two-tailed tests
Hypothesis testing and the normal curve
Type 1 and type 2 errors
Parametric and non-parametric statistics: some further observations
The power of a test
Analysing Frequencies
The chi-square test
Calculating the x[superscript 2] test statistic
A practical example of a test for homogeneous frequencies
The problem of independence
One degree of freedom--Yates' correction
Goodness of fit tests
Tests for association--the contingency table
The r [times] c contingency table
The G-test
Applying the G-test to a one-way classification of frequencies
Applying the G-test to a 2 [times] 2 contingency table
Applying the G-test to an r [times] c contingency table
Advice on analysing frequencies
Measuring Correlations
The meaning of correlation
Investigating correlation
The strength and significance of a correlation
Covariance
The Product Moment Correlation Coefficient
The coefficient of determination r[superscript 2]
The Spearman Rank Correlation Coefficient r[subscript s]
Advice on measuring correlations
Regression Analysis
Introduction
Gradients and triangles
Dependent and independent variables
A perfect rectilinear relationship
The line of least squares
Simple linear regression
Fitting the regression line to the scattergram
The error of a regression line
Confidence limits of an individual estimate
The significance of the regression line
The difference between two regression lines
Dealing with curved relationships
Transformation of both axes
Regression through the origin
An alternative line of best fit
Advice on using regression analysis
Comparing Averages
Introduction
Matched and unmatched observations
The Mann--Whitney U-test for unmatched samples
Advice on using the Mann--Whitney U-test
More than two samples--the Kruskal--Wallis test
Advice on using the Kruskal--Wallis test
The Wilcoxon test for matched pairs
Advice on using the Wilcoxon test for matched pairs
Comparing means--parametric tests
The F-test (two-tailed)
The z-test for comparing the means of two large samples
The t-test for comparing the means of two small samples
The t-test for matched pairs
Advice on comparing means
Analysis of Variance--ANOVA
Why do we need ANOVA?
How ANOVA works
Procedure for computing one-way ANOVA
Procedure for computing the Tukey test
Two-way ANOVA
Procedure for computing two-way ANOVA
Procedure for computing the Tukey test in two-way ANOVA
Two-way ANOVA with single observations
The randomized block design
The Latin square
Analysis of variance in regression
Advice on using ANOVA
Multivariate Analysis
Introduction
What is information?
Making large problems manageable
Are there three groups or four?
Learning from experience?
Variations on a theme
Summary
Appendices
Table of random numbers
t-distribution
X[superscript 2]-distribution
Critical values of Spearman's Rank Correlation Coefficient
Product moment correlation values at the 0.05 and 0.01 levels of significance
Mann--Whitney U-test values (two-tailed test) P = 0.05
Critical values of T in the Wilcoxon test for two matched samples
F-distribution, 0.05 level of significance, two-tailed test
Critical values of F[subscript max] 0.05 level of significance
F-distribution
Tukey test
Symbols
Matrices and vectors
Computer packages
Bibliography and further reading
Index

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