Statistics at square one [[electronic resource] /] / T.D.V. Swinscow and M.J. Campbell

• Autore: Swinscow T. D. V (Thomas Douglas Victor)
• Pubblicazione: London, : BMJ, 2002
• Edizione: 10th ed.
• Descrizione fisica: viii, 158 p. : ill
• Soggetto topico: Medical statistics
• Altri autori: CampbellMichael J., PhD.
• Note generali: Previous ed.: 1996.; Michael Campbell revised this edition.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Contents -- Preface -- 1 Data display and summary -- Types of data -- Stem and leaf plots -- Median -- Measures of variation -- Data display -- Histograms -- Bar charts -- Common questions -- What is the distinction between a histogram and a bar chart? -- How many groups should I have for a histogram? -- Displaying data in papers -- Exercises -- Exercise 1.1 -- Reference -- 2 Summary statistics for quantitative and binary data -- Mean and standard deviation -- Standard deviation from ungrouped data -- Calculator procedure -- Standard deviation from grouped data -- Data transformation -- Between subjects and within subjects standard deviation -- Summarising relationships between binary variables -- Choice of summary statistics for binary data -- Common questions -- When should I quote the mean and when should I quote the median to describe my data? -- When should I use a standard deviation to summarise variability? -- When should I quote an odds ratio and when should I quote a relative risk? -- Reading and displaying summary statistics -- Exercises -- Exercise 2.1 -- Exercise 2.2 -- Exercise 2.3 -- References -- 3 Populations and samples -- Populations -- Samples -- Unbiasedness and precision -- Randomisation -- Variation between samples -- Standard error of the mean -- Standard error of a proportion or a percentage -- Problems with non-random samples -- Common questions -- What is an acceptable response rate from a survey? -- Given measurements on a sample, what is the difference between a standard deviation and a standard error? -- When should I use a standard deviation to describe data and when should I use a standard error? -- Reading and reporting populations and samples -- Exercises -- Exercise 3.1 -- Exercise 3.2 -- Exercise 3.3 -- References -- 4 Statements of probability and confidence intervals -- Reference ranges. ; Confidence intervals -- Common questions -- What is the difference between a reference range and a confidence interval? -- Reading and reporting confidence intervals -- Exercises -- Exercise 4.1 -- Exercise 4.2 -- Reference -- 5 Differences between means: type I and type II errors and power -- Large-sample standard error of difference between means -- Large-sample confidence interval for the difference in two means -- Null hypothesis and type I error -- Testing for differences of two means -- Alternative hypothesis and type II error -- Common questions -- Why is the P value not the probability that the null hypothesis is true? -- What is the difference between a one sided and a two sided test? -- Reading and reporting P values -- Exercises -- Exercise 5·1 -- Exercise 5.2 -- Reference -- 6 Confidence intervals for summary statistics of binary data -- Standard error of difference between percentages or proportions -- Confidence interval for a difference in proportions or percentages -- Significance test for a difference in two proportions -- Confidence interval for an odds ratio -- Standard error of a total -- Paired alternatives -- Common questions -- Why is the standard error used for calculating a confidence interval for the difference in two proportions different from the standard error used for calculating the significance? -- Exercises -- Exercise 6.1 -- Exercise 6.2 -- Exercise 6.3 -- Exercise 6.4 -- Reference -- 7 The t tests -- Confidence interval for the mean from a small sample -- Difference of sample mean from population mean (one sample t test) -- Difference between means of two samples -- Unequal standard deviations -- Difference between means of paired samples (paired t test) -- Further methods -- Common questions -- Should I test my data for Normality before using the t test?. ; Should I test for equality of the standard deviations before using the usual t test? -- Why should I use a paired test if my data are paired? What happens if I don't? -- Reading and reporting t tests -- Exercises -- Exercise 7.1 -- Exercise 7.2 -- Exercise 7.3 -- Exercise 7.4 -- References -- 8 The .2 tests -- Quick method -- Fourfold tables -- Small numbers -- Comparing proportions -- Splitting of .2 -- .2 test for trend -- Comparison of an observed and a theoretical distribution -- McNemar's test -- Extensions of the .2 test -- Common questions -- I have matched data, but the matching criteria were very weak. Should I use McNemar's test? -- Reading and reporting chi squared tests -- Exercises -- Exercise 8.1 -- Exercise 8.2 -- Exercise 8.3 -- Exercise 8.4 -- Exercise 8.5 -- Exercise 8.6 -- Exercise 8.7 -- References -- 9 Exact probability test -- Common questions -- Why is Fisher's test called an exact test? -- Reading and reporting Fisher's Exact Test -- Exercise -- Exercise 9.1 -- References -- 10 Rank score tests -- Paired samples -- Unpaired samples -- Common questions -- Nonparametric tests are valid for both non-Normally distributed data and Normally distributed data, so why not use them all the time? -- Do nonparametric tests compare medians? -- How is the Mann-Whitney U test related to the t test? -- Reading and reporting rank score tests -- Exercises -- Exercise 10.1 -- Exercise 10.2 -- References -- 11 Correlation and regression -- Correlation coefficient -- Looking at data: scatter diagrams -- Calculation of the correlation coefficient -- Significance test -- Spearman rank correlation -- The regression equation -- More advanced methods -- Common questions -- If two variables are correlated are they causally related? -- How do I test the assumptions underlying linear regression?. ; When should I use correlation and when should I use regression? -- Reading and reporting correlation and regression -- Exercises -- Exercise 11.1 -- Exercise 11.2 -- Exercise 11.3 -- Exercise 11.4 -- References -- 12 Survival analysis -- Kaplan-Meier survival curve -- Method -- Example of calculation of survival curve -- Log rank test -- Further methods -- Common questions -- Do I need to test for a constant relative risk before doing the log rank test? -- If I don't have any censored observations, do I need to use survival analysis? -- Reading and reporting survival analysis -- Exercises -- Exercise 12.1 -- Exercise 12.2 -- References -- 13 Study design and choosing a statistical test -- Design -- Sample size -- Choice of test -- Reading and reporting on the design of a study -- Exercises -- Exercise 13.1 -- Exercise 13.2 -- Exercise 13.3 -- Exercise 13.4 -- Exercise 13.5 -- References -- Answers to exercises -- Appendix -- Index.
• Sommario/riassunto: This is one of the bestselling introductions to medical statistics of all time. The tenth edition has been revised throughout, especially in the areas of binary data to deal with relative risk, absolute risk and the evidence-based criteria of numbers need to treat. Each chapter now has a section on reading and reporting statistics, and self testing at the end of each section makes this an ideal learning tool.
• Titolo autorizzato: Statistics at square one
• ISBN: 1-280-19790-0; 9786610197903; 0-585-41499-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996320694203316
• Lo trovi qui: Univ. di Salerno