Statistics for Terrified Biologists

Name: Statistics for Terrified Biologists
Price: 35.86 USD
Availability: InStock
ISBN: 9781405149563

ISBN-10: 1405149566

ISBN-13: 9781405149563

Edition: 2008

Authors: Helmut van Emden, Helmut Fritz Van Emden

List price: $49.75

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Description:

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…

Book details

List price: $49.75
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