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Introductory Biological Statistics 4

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  • Formatas: 252 pages, aukštis x plotis x storis: 2687x2062x0.50 mm, weight: 1400 g
  • Išleidimo metai: 25-Apr-2019
  • Leidėjas: Waveland Pr Inc
  • ISBN-10: 1478638184
  • ISBN-13: 9781478638186
Kitos knygos pagal šią temą:
Introductory Biological Statistics 4
  • Formatas: 252 pages, aukštis x plotis x storis: 2687x2062x0.50 mm, weight: 1400 g
  • Išleidimo metai: 25-Apr-2019
  • Leidėjas: Waveland Pr Inc
  • ISBN-10: 1478638184
  • ISBN-13: 9781478638186
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781478638186.html
Preface to the Fourth Edition ix
one Statistics and Sampling
1(10)
1.1 What Is "Statistics"?
1(1)
1.2 Populations and Samples
2(1)
1.3 Sampling Error: Precision, Accuracy, and Bias
3(2)
1.4 Random Samples and Independence
5(1)
1.5 How to Collect a Random Sample
5(1)
1.5.1 Simple Random Sample
5(1)
1.5.2 Stratified Random Sample
6(1)
1.6 Poor Sampling Designs
6(1)
1.6.1 Convenience Samples
6(1)
1.6.2 Voluntary Response
7(1)
1.7 Conclusions
7(1)
Key Terms
7(1)
Practice Exercises
7(1)
Homework Exercises
8(1)
Applications in R: Generating Random Numbers
8(3)
two Variables and Data Management
11(14)
2.1 Variables
11(1)
2.2 Categorical Variables
11(1)
2.3 Numerical Variables
12(1)
2.4 Manipulating Numerical Data
13(5)
2.4.1 Transformed Variables
13(1)
2.4.2 Derived Variables
14(1)
2.4.3 Representing Data with Symbols
15(1)
2.4.4 Significant Figures and Rounding Rules
15(1)
2.4.5 Converting Data from Measurement to Ranks
16(2)
2.5 Explanatory and Response Variables
18(1)
2.6 Data Management
18(3)
2.6.1 Managing Raw Data
18(2)
2.6.2 Checking the Data for Errors
20(1)
2.6.3 What to Do about Missing Data
20(1)
2.6.4 Final Words of Wisdom
20(1)
Key Terms
21(1)
Practice Exercises
21(1)
Homework Exercises
22(1)
Applications in R: Data Transformation
22(3)
three Summarizing Data in Tables and Graphs
25(14)
3.1 Graphical Representation of Numerical Data
25(3)
3.2 How to Create Meaningful Tables
28(1)
3.3 How to Create Meaningful Graphs
29(1)
3.4 Frequency Distributions and Probability Distributions
30(1)
3.5 Frequency Distributions of Discrete Variables
30(1)
3.6 Frequency Distributions of Continuous Variables
31(2)
3.7 Histograms and Their Interpretation
33(2)
3.7.1 Creating a Histogram with Computer Software
33(1)
3.7.2 Data Inspection with Histograms
33(2)
3.8 Cumulative Frequency Distributions
35(1)
Key Terms
36(1)
Practice Exercises
36(1)
Homework Exercises
37(1)
Applications in R: Graphing
37(2)
four Descriptive Statistics: Measures of Central Tendency and Dispersion
39(14)
4.1 Sample Statistics and Population Parameters
39(1)
4.2 Measures of Central Tendency
40(5)
4.2.1 The Proportion
40(1)
4.2.2 The Mode
40(1)
4.2.3 The Median (θ, M)
40(1)
4.2.4 The Mean (μ, x)
41(1)
4.2.5 Weighted Mean
41(1)
4.2.6 Positions of Mean, Median, and Mode in Symmetric and Skewed Distributions
42(1)
4.2.7 Other Measures of Location
43(2)
4.3 Measures of Dispersion
45(4)
4.3.1 The Range
45(1)
4.3.2 Interquartile Range and the Boxplot
45(1)
4.3.3 Standard Deviation (σ, s)
46(2)
4.3.4 An Empirical Rule
48(1)
4.3.5 Coefficient of Variation
48(1)
Key Terms
49(1)
Exercises
49(1)
Practice Exercises
49(1)
Homework Exercises
50(1)
Applications in R: Descriptive Statistics
50(3)
five Probability Principles and Discrete Probability Distributions
53(20)
5.1 Classical and Empirical Probability
53(1)
5.2 Division and Subtraction Rules
54(1)
5.3 Counting Possibilities
55(1)
5.4 The Multiplication Rule, Independence, and Conditional Probability
56(2)
5.4.1 Independence of Two Events or Variables
57(1)
5.4.2 Conditional Probability
57(1)
5.5 Conditional Probability: Tree Diagrams and Bayes' Theorem
58(2)
5.6 Addition Rule and Mutually Exclusive Events
60(1)
5.7 The Binomial Distribution
61(5)
5.7.1 The Binomial Formula
63(1)
5.7.2 Cumulative Probability
64(1)
5.7.3 Expected Frequencies
64(1)
5.7.4 Comparison of Observations with Predictions from the Binomial Distribution
65(1)
5.8 The Poisson Distribution
66(2)
A Note about Working Exercises and Using Computer Statistical Packages
68(1)
Looking Ahead
68(1)
Key Terms
68(1)
Exercises
68(1)
Practice Exercises
69(1)
Homework Exercises
70(1)
Applications in R: Probability Functions
71(2)
six Statistical Inference and Hypothesis Testing
73(8)
6.1 Statistical Inference
74(1)
6.2 Statistical Hypotheses
74(2)
6.3 Statistical Decisions and Their Potential Errors
76(3)
6.3.1 The p-Value
76(1)
6.3.2 Decision to Reject or Fail to Reject, and the Problem of Type I and Type II Errors
76(1)
6.3.3 Power of the Test and What Influences Power
77(2)
6.3.4 What Is Meant by "Significant"?
79(1)
6.4 Application: Steps in Testing a Statistical Hypothesis
79(1)
6.5 Application of Statistics to Some Common Questions in Biology
80(1)
Key Terms
80(1)
seven Testing Hypotheses about Frequencies
81(12)
7.1 The Chi-Square Goodness-of-Fit Test
81(3)
7.2 The Chi-Square Test of Association
84(2)
7.3 The Fisher Exact Probability Test
86(2)
Key Terms
88(1)
Exercises
88(1)
Practice Exercises
88(2)
Homework Exercises
90(1)
Applications in R: Tests of Hypotheses about Frequencies
91(2)
eight The Normal Distribution
93(16)
8.1 The Normal Distribution and Its Properties
93(1)
8.1.1 Properties of the Normal Distribution
93(1)
8.2 The Standard Normal Distribution and Use of Z-Scores
94(3)
8.3 Sampling Distributions
97(3)
8.3.1 The Central Limit Theorem
98(1)
8.3.2 Standard Error of the Mean
98(2)
8.4 Testing for Normality
100(1)
8.5 Normal Approximation of the Binomial Distribution
101(2)
8.6 Using the Normal Approximation for Inferences about Proportions
103(1)
8.6.1 Confidence Interval for a Proportion
103(1)
8.6.2 Binomial Tests
104(1)
Key Terms
104(1)
Exercises
105(1)
Practice Exercises
105(1)
Homework Exercises
106(1)
Applications in R: Normality
107(2)
nine Inferences about a Single Population Mean: One-Sample and Paired Comparisons
109(18)
9.1 Questions about the Mean from a Single Population
109(1)
9.2 The t-Distribution
110(1)
9.3 Estimation: Confidence Interval for μ
111(2)
9.4 Reporting a Sample Mean and Its Variation
113(1)
9.5 Hypothesis Concerning a Single Population Mean
113(3)
9.5.1 One-Sample f-Test
113(2)
9.5.2 One-Tailed vs. Two-Tailed Hypothesis Tests
115(1)
9.6 Matched-Pairs Tests: Think Differences
116(1)
9.7 The Paired t-Test
116(1)
9.8 How to Proceed If the Normality Assumption Is Violated
117(1)
9.9 Nonparametric Tests for Two Related Samples
118(2)
9.9.1 The Wilcoxon Signed Rank Test
118(1)
9.9.2 The Sign Test
119(1)
Summary
120(1)
Key Terms
120(1)
Exercises
120(1)
Practice Exercises
121(1)
Homework Exercises
122(1)
Applications in R: Single Population Tests
123(4)
ten Inferences Concerning Two Population Means
127(12)
10.1 The t-Test for Two Independent Samples
127(2)
10.2 Confidence Interval for the Difference between Two Population Means
129(1)
10.3 A Nonparametric Test for Two Independent Samples: The Mann-Whitney U Test
130(2)
10.4 Power of the Test: How Large a Sample Is Sufficient?
132(2)
10.4.1 Determining the Sample Size Needed to Detect a Minimum Effect
133(1)
10.4.2 Determining the Minimum Detectable Difference
134(1)
10.5 Review: What Is the Appropriate Statistical Test?
134(1)
Key Terms
135(1)
Exercises
135(1)
Practice Exercises
135(1)
Homework Exercises
136(2)
Applications in R: Two-Sample Tests
138(1)
eleven Inferences Concerning Means from Multiple Populations: ANOVA
139(20)
11.1 The Rationale of ANOVA: An Illustration
139(3)
11.2 The Assumptions of ANOVA
142(1)
11.3 Fixed-Effects ANOVA
142(7)
11.3.1 Testing the Null Hypothesis That All Treatment Means Are Equal
144(2)
11.3.2 Multiple Comparisons (Post-hoc)
146(1)
11.3.3 Fixed-Effects ANOVA Using Survey Data
147(2)
11.3.4 One-Way ANOVA Design with Random Effects
149(1)
11.4 Testing the Assumptions of ANOVA
149(1)
11.5 Remedies for Failed Assumptions
150(3)
11.5.1 Transformations in ANOVA
151(1)
11.5.2 Nonparametric Alternative to One-Way ANOVA: The Kruskal-Wallis Test
151(2)
Key Terms
153(1)
Exercises
153(1)
Practice Exercises
153(2)
Homework Exercises
155(1)
Applications in R: ANOVA
156(3)
twelve More ANOVA: Randomized Block and Factorial Designs
159(14)
12.1 The Randomized Block Design
159(3)
12.2 The Friedman Test
162(1)
12.3 The Factorial Design
163(4)
12.4 Other ANOVA Designs
167(1)
Key Terms
167(1)
Exercises
167(1)
Practice Exercises
167(2)
Homework Exercises
169(1)
Applications in R: Complex ANOVAs and the Friedman Test
170(3)
thirteen Associations between Continuous Variables: Correlation
173(12)
13.1 Associations or Modeling: When to Use Correlation or Regression
174(1)
13.2 The Pearson Correlation Coefficient
174(3)
13.2.1 Testing the Significance of r
177(1)
13.3 A Correlation Matrix
177(2)
13.4 Nonparametric Correlation Analysis (Spearman's r)
179(1)
Key Terms
180(1)
Practice Exercises
180(2)
Homework Exercises
182(1)
Applications in R: Correlations
183(1)
Spearman Correlation
183(2)
fourteen Modeling the Effects of One Continuous Variable on Another: Regression Analysis
185(14)
14.1 Fundamentals of Simple Linear Regression
185(1)
14.2 Conceptualizing Regression Analysis
186(1)
14.3 Estimating the Regression Parameters
187(1)
14.4 Testing the Significance of the Regression Equation
188(2)
14.4.1 Sums of Squares
188(1)
14.4.2 Degrees of Freedom
189(1)
14.4.3 Mean Squares and F-test
189(1)
14.5 Confidence Interval for β
190(1)
14.6 The Coefficient of Determination (r2)
190(1)
14.7 Regression Analysis Using Survey Data
191(2)
14.8 Predicting y from x
193(1)
14.8.1 Extrapolation
193(1)
14.8.2 Confidence Interval for μy
193(1)
14.9 Checking Assumptions and Remedies for Their Failure
194(1)
14.10 Advanced Regression Techniques
195(1)
Key Terms
196(1)
Exercises
196(1)
Practice Exercises
196(1)
Homework Exercises
196(1)
Applications in R: Linear Regression
197(1)
Testing the Assumptions
198(1)
fifteen Selecting Appropriate Statistical Procedures
199(8)
15.1 General Process for a Complete Data Analysis
199(1)
15.2 Choosing the Appropriate Statistical Test
200(1)
15.3 Some Statistical Methods Not Covered in Previous
Chapters
201(5)
15.3.1 Multivariate Analysis of Variance---MANOVA
202(1)
15.3.2 Logistic Regression
202(1)
15.3.3 Ordination
203(1)
15.3.4 Analysis of Spatial Data
204(1)
15.3.5 Resampling Techniques
205(1)
15.3.6 Meta-Analysis
206(1)
Key Terms
206(1)
sixteen Experimental Design
207(8)
16.1 Research Questions, Surveys, and Experiments
207(2)
16.1.1 Observational Studies Can Show Pattern, but Not Causation
207(1)
16.1.2 Experimental Error Masks the True Effects We Wish to Uncover
208(1)
16.2 How to Avoid Bias While Testing for Treatment Effects
209(2)
16.2.1 Include Appropriate Controls
209(1)
16.2.2 Randomized Treatments
210(1)
16.2.3 Use Blinding to Prevent Bias in Assessment
210(1)
16.3 How to Reduce Sampling Error and Its Influences
211(3)
16.3.1 The Benefits of Replication
211(1)
16.3.2 Replicates Must Be Independent
211(1)
16.3.3 How Many Replicates Are Needed?
212(1)
16.3.4 Plan to Include More Replicates than You Think You Need
213(1)
16.3.5 Balance Replication among Treatments
213(1)
16.3.6 Use Blocking to Reduce Unexplained Error
213(1)
16.4 Seek Advice from Experts
214(1)
Key Terms
214(1)
Appendix A Statistical Tables 215(10)
Appendix B Answers to Practice Exercises 225(16)
Appendix C Installing and Running R 241(6)
Appendix D Literature Cited 247(2)
Index 249