Statistics for the Life Sciences
2nd Edition
Published on 16. February 1999
Book
Hardback
683 pages
978-0-13-649211-5 (ISBN)
Description
For graduate or undergraduate courses in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences. The style of Statistics for the Life Sciences is informal, uses only minimal mathematical notation, and makes extensive use of graphics and illustrations to teach the concepts. Only Elementary Algebra is required.
More details
Edition
2nd edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Adult education
Dimensions
Height: 242 mm
Width: 195 mm
Thickness: 32 mm
Weight
1270 gr
ISBN-13
978-0-13-649211-5 (9780136492115)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Book
01/2003
3rd Edition
Pearson
€99.76
Article exhausted; check for reprint
Previous edition
Myra L. Samuels
Statistics for the Life Sciences
Book
08/1988
Macmillan USA
€52.13
Article exhausted; check for reprint
Content
1. Introduction.
Statistics and the Life Sciences. Examples and Overview.
2. Description of Populations and Samples.
Introduction. Frequency Distributions: Techniques for Data. Frequency Distributions: Shapes and Examples. Descriptive Statistics: Measures of Center. Boxplots. Measures of Dispersion. Effect of Transformation of Variables (Optional). Samples and Populations: Statistical Inference. Perspective.
3. Random Sampling, Probability, and the Binomial Distribution.
Probability and the Life Sciences. Random Sampling. Introduction to Probability. Probability Trees. Density Curves. The Binomial Distribution. Fitting a Binomial Distribution to Data (Optional).
4. The Normal Distribution.
Introduction. The Normal Curves. Areas Under a Normal Curve. Assessing Normality. The Continuity Correction (Optional). Perspective.
5. Sampling Distributions.
Basic Ideas. Dichotomous Observations. Quantitative Observations. Illustration of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distribution (Optional). Perspective.
6. Confidence Intervals.
Statistical Estimation. Standard Error of the Mean. Confidence Interval for u. Planning a Study to Estimate u. Conditions for Validity of Estimation Methods. Confidence Interval for a Population Proportion. Perspective and Summary.
7. Comparison of Two Independent Samples.
Introduction. Standard Error of (y1 - y2). Confidence Interval for (u1 - u2). Hypothesis Testing: The t-test. Further Discussion of the t-test. One-Tailed Tests. More on Interpretation of Statistical Significance. Planning for Adequate Power (Optional). Student's t: Conditions and Summary. More on Principles of Testing Hypotheses. The Wilcoxon-Mann-Whitney Test. Perspective.
8. Statistical Principles of Design.
Introduction. Observational Studies. Experiments. Restricted Randomization: Blocking and Stratification. Levels of Replication. Sampling Concerns (Optional). Perspective.
9. Comparison of Two Paired Samples.
Introduction. The Paired-Sample t-Test and Confidence Interval. The Paired Design. The Sign Test. Further Considerations in Paired Experiments. Perspective.
10. Analysis of Categorical Data.
Inference for Proportions: The Chi-Square Goodness-of-Fit Test. The Chi-Square Test for the 2 X 2 Contingency Table. Independence and Association in a 2 X 2 Contingency Table. Fisher's Exact Test (Optional). The r x k Contingency Table. Applicability of Methods. Confidence Interval for a Difference Between Proportions. Paired Data and 2 X 2 Tables (Optional). Relative Risks and the Odds Ratio (Optional). Summary of Chi-Square Tests.
11. Comparing the Means of k Independent Samples.
Introduction. The Basic Analysis of Variance. The Analysis of Variance Model (Optional). The Global F Test. Applicability of Methods. Linear Combinations of Means (Optional). Multiple Comparisons (Optional). Perspective.
12. Linear Regression and Correlation.
Introduction. The Fitted Regression Line. Parametric Interpretation of Regression: The Linear Model. Statistical Inference Concerning B1. Guidelines for Interpreting Regression and Correlation. Perspective. Summary of Formulas.
13. A Summary of Inference Methods.
Introduction. Data Analysis Samples.
Appendices.
Chapter Notes.
Statistical Tables.
Answers to Selected Exercises.
Index.
Index of Examples.
Statistics and the Life Sciences. Examples and Overview.
2. Description of Populations and Samples.
Introduction. Frequency Distributions: Techniques for Data. Frequency Distributions: Shapes and Examples. Descriptive Statistics: Measures of Center. Boxplots. Measures of Dispersion. Effect of Transformation of Variables (Optional). Samples and Populations: Statistical Inference. Perspective.
3. Random Sampling, Probability, and the Binomial Distribution.
Probability and the Life Sciences. Random Sampling. Introduction to Probability. Probability Trees. Density Curves. The Binomial Distribution. Fitting a Binomial Distribution to Data (Optional).
4. The Normal Distribution.
Introduction. The Normal Curves. Areas Under a Normal Curve. Assessing Normality. The Continuity Correction (Optional). Perspective.
5. Sampling Distributions.
Basic Ideas. Dichotomous Observations. Quantitative Observations. Illustration of the Central Limit Theorem (Optional). The Normal Approximation to the Binomial Distribution (Optional). Perspective.
6. Confidence Intervals.
Statistical Estimation. Standard Error of the Mean. Confidence Interval for u. Planning a Study to Estimate u. Conditions for Validity of Estimation Methods. Confidence Interval for a Population Proportion. Perspective and Summary.
7. Comparison of Two Independent Samples.
Introduction. Standard Error of (y1 - y2). Confidence Interval for (u1 - u2). Hypothesis Testing: The t-test. Further Discussion of the t-test. One-Tailed Tests. More on Interpretation of Statistical Significance. Planning for Adequate Power (Optional). Student's t: Conditions and Summary. More on Principles of Testing Hypotheses. The Wilcoxon-Mann-Whitney Test. Perspective.
8. Statistical Principles of Design.
Introduction. Observational Studies. Experiments. Restricted Randomization: Blocking and Stratification. Levels of Replication. Sampling Concerns (Optional). Perspective.
9. Comparison of Two Paired Samples.
Introduction. The Paired-Sample t-Test and Confidence Interval. The Paired Design. The Sign Test. Further Considerations in Paired Experiments. Perspective.
10. Analysis of Categorical Data.
Inference for Proportions: The Chi-Square Goodness-of-Fit Test. The Chi-Square Test for the 2 X 2 Contingency Table. Independence and Association in a 2 X 2 Contingency Table. Fisher's Exact Test (Optional). The r x k Contingency Table. Applicability of Methods. Confidence Interval for a Difference Between Proportions. Paired Data and 2 X 2 Tables (Optional). Relative Risks and the Odds Ratio (Optional). Summary of Chi-Square Tests.
11. Comparing the Means of k Independent Samples.
Introduction. The Basic Analysis of Variance. The Analysis of Variance Model (Optional). The Global F Test. Applicability of Methods. Linear Combinations of Means (Optional). Multiple Comparisons (Optional). Perspective.
12. Linear Regression and Correlation.
Introduction. The Fitted Regression Line. Parametric Interpretation of Regression: The Linear Model. Statistical Inference Concerning B1. Guidelines for Interpreting Regression and Correlation. Perspective. Summary of Formulas.
13. A Summary of Inference Methods.
Introduction. Data Analysis Samples.
Appendices.
Chapter Notes.
Statistical Tables.
Answers to Selected Exercises.
Index.
Index of Examples.