Statistics for Terrified Biologists

ISBN-10: 1405149566

ISBN-13: 9781405149563

Edition: 2008

Authors: Helmut van Emden, Helmut Fritz Van Emden

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Written in a lively and engaging style, this textbook makes basic statistical methods and applications accessible to undergraduate biology and environmental science students. A lively and engaging textbook that makes basic statistical methods and applications accessible to undergraduate biology and environmental science students Based on a course created by an internationally renowned professor with over 30 years teaching experience Straight forward, jargon-free language demystifies statistical formulas for the average student Includes additional activities that can be tackled with a basic pocket calculator at the end of each chapter Features simple illustrations and useful case studies to help illustrate key concepts
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Book details

List price: $47.95
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/28/2008
Binding: Paperback
Pages: 360
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 1.188
Language: English

Preface
How to use this book
Introduction
The text of the chapters
What should you do if you run into trouble?
Elephants
The numerical examples in the text
Boxes
Spare-time activities
Executive summaries
Why go to all that bother?
The bibliography
Introduction
What are statistics?
Notation
Notation for calculating the mean
Summarizing variation
Introduction
Different summaries of variation
Range
Total deviation
Mean deviation
Variance
Why n - 1?
Why the squared deviations?
The standard deviation
The next chapter
Spare-time activities
When are sums of squares NOT sums of squares?
Introduction
Calculating machines offer a quicker method of calculating sums of squares
Added squares
The correction factor
Avoid being confused by the term "sum of squares"
Summary of the calculator method of calculating down to standard deviation
Spare-time activities
The normal distribution
Introduction
Frequency distributions
The normal distribution
What per cent is a standard deviation worth?
Are the percentages always the same as these?
Other similar scales in everyday life
The standard deviation as an estimate of the frequency of a number occurring in a sample
From per cent to probability
Executive summary 1 - The standard deviation
The relevance of the normal distribution to biological data
To recap
Is our observed distribution normal?
Checking for normality
What can we do about a distribution that clearly is not normal?
Transformation
Grouping samples
Doing nothing!
How many samples are needed?
Factors affecting how many samples we should take
Calculating how many samples are needed
Further calculations from the normal distribution
Introduction
Is "A" bigger than "B"?
The yardstick for deciding
Derivation of the standard error of a difference between two means
from variance of single data to variance of means
from variance of single data to "variance of differences"
The combination of Steps 1 and 2; the standard error of difference between means (s.e.d.m.)
Recap of the calculation of s.e.d.m. from the variance calculated from the individual values
The importance of the standard error of differences between means
Summary of this chapter
Executive summary 2 - Standard error of a difference between two means
Spare-time activities
The t-test
Introduction
The principle of the t-test
The t-test in statistical terms
Why t?
Tables of the t-distribution
The standard t-test
The procedure
The actual t-test
t-test for means associated with unequal variances
The s.e.d.m. when variances are unequal
A worked example of the t-test for means associated with unequal variances
The paired t-test
Pair when possible
Executive summary 3 - The t-test
Spare-time activities
One tail or two?
Introduction
Why is the analysis of variance F-test one-tailed?
The two-tailed F-test
How many tails has the t-test?
The final conclusion on number of tails
Analysis of variance - What is it? How does it work?
Introduction
Sums of squares in the analysis of variance
Some "made-up" variation to analyze by Anova
The sum of squares table
Using Anova to sort out the variation in Table C
Phase 1
Phase 2
SqADS - an important acronym
Back to the sum of squares table
How well does the analysis reflect the input?
End Phase
Degrees of freedom in Anova
The completion of the End Phase
The variance ratio
The relationship between "t" and "F"
Constraints on the analysis of variance
Adequate size of experiment
Equality of variance between treatments
Testing the homogeneity of variance
The element of chance: randomization
Comparison between treatment means in the analysis of variance
The least significant difference
A caveat about using the LSD
Executive summary 4 - The principle of the analysis of variance
Experimental designs for analysis of variance
Introduction
Fully randomized
Data for analysis of a fully randomized experiment
Prelims
Phase 1
Phase 2
End Phase
Randomized blocks
Data for analysis of a randomized block experiment
Prelims
Phase 1
Phase 2
End Phase
Incomplete blocks
Latin square
Data for the analysis of a Latin square
Prelims
Phase 1
Phase 2
End Phase
Further comments on the Latin square design
Split plot
Executive summary 5 - Analysis of a randomized block experiment
Spare-time activities
Introduction to factorial experiments
What is a factorial experiment?
Interaction
If there is no interaction
What if there is interaction?
How about a biological example?
Measuring any interaction between factors is often the main/only purpose of an experiment
How does a factorial experiment change the form of the analysis of variance?
Degrees of freedom for interactions
The similarity between the "residual" in Phase 2 and the "interaction" in Phase 3
Sums of squares for interactions
2-Factor factorial experiments
Introduction
An example of a 2-factor experiment
Analysis of the 2-factor experiment
Prelims
Phase 1
Phase 2
End Phase (of Phase 2)
Phase 3
End Phase (of Phase 3)
Two important things to remember about factorials before tackling the next chapter
Analysis of factorial experiments with unequal replication
Executive summary 6 - Analysis of a 2-factor randomized block experiment
Spare-time activity
Factorial experiments with more than two factors
Introduction
Different "orders" of interaction
Example of a 4-factor experiment
Prelims
Phase 1
Phase 2
Phase 3
To the End Phase
Addendum - Additional working of sums of squares calculations
Spare-time activity
Factorial experiments with split plots
Introduction
Deriving the split plot design from the randomized block design
Degrees of freedom in a split plot analysis
Main plots
Sub-plots
Numerical example of a split plot experiment and its analysis
Calculating the sums of squares
End Phase
Comparison of split plot and randomized block experiment
Uses of split plot designs
Spare-time activity
The t-test in the analysis of variance
Introduction
Brief recap of relevant earlier sections of this book
Least significant difference test
Multiple range tests
Operating the multiple range test
Testing differences between means
Suggested "rules" for testing differences between means
Presentation of the results of tests of differences between means
The results of the experiments analyzed by analysis of variance in Chapters 11-15
Spare-time activities
Linear regression and correlation
Introduction
Cause and effect
Other traps waiting for you to fall into
Extrapolating beyond the range of your data
Is a straight line appropriate?
The distribution of variability
Regression
Independent and dependent variables
The regression coefficient (b)
Calculating the regression coefficient (b)
The regression equation
A worked example on some real data
The data (Box 17.2)
Calculating the regression coefficient (b) - i.e. the slope of the regression line
Calculating the intercept (a)
Drawing the regression line
Testing the significance of the slope (b) of the regression
How well do the points fit the line? - the coefficient of determination (r[superscript 2])
Correlation
Derivation of the correlation coefficient (r)
An example of correlation
Is there a correlation line?
Extensions of regression analysis
Nonlinear regression
Multiple linear regression
Multiple nonlinear regression
Analysis of covariance
Executive summary 7 - Linear regression
Spare-time activities
Chi-square tests
Introduction
When and where not to use x[superscript 2]
The problem of low frequencies
Yates' correction for continuity
The x[superscript 2] test for "goodness of fit"
The case of more than two classes
x[superscript 2] with heterogeneity
Heterogeneity x[superscript 2] analysis with "covariance"
Association (or contingency) x[superscript 2]
2 x 2 contingency table
Fisher's exact test for a 2 x 2 table
Larger contingency tables
Interpretation of contingency tables
Spare-time activities
Nonparametric methods (what are they?)
Disclaimer
Introduction
Advantages and disadvantages of the two approaches
Where nonparametric methods score
Where parametric methods score
Some ways data are organized for nonparametric tests
The sign test
The Kruskal-Wallis analysis of ranks
Kendall's rank correlation coefficient
The main nonparametric methods that are available
How many replicates
Statistical tables
Solutions to "Spare-time activities"
Bibliography
Index
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