Statistics with JMP

Statistics with JMP: Hypothesis Tests, ANOVA and Regression Peter Goos, University of Leuven and University of Antwerp, Belgium David Meintrup, University of Applied Sciences Ingolstadt, Germany A first course on basic statistical methodology using JMP This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic. Promotes the use of graphs and confidence intervals in addition to p-values. Course materials and tutorials for teaching are available on the book's companion website. Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.

Statistics with JMP: Hypothesis Tests, ANOVA and RegressionPeter Goos, University of Leuven and University of Antwerp, BelgiumDavid Meintrup, University of Applied Sciences Ingolstadt, GermanyA first course on basic statistical methodology using JMPThis book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software.Key features:* Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested.* Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values).* Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic.* Promotes the use of graphs and confidence intervals in addition to p-values.* Course materials and tutorials for teaching are available on the book's companion website.Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.

Dedication iiiPreface xiiiAcknowledgements xviiPart One Estimators and tests 11 Estimating population parameters 32 Interval estimators 373 Hypothesis tests 71Part Two One population 1034 Hypothesis tests for a population mean, proportion or variance 1055 Two hypothesis tests for the median of a population 1496 Hypothesis tests for the distribution of a population 175Part Three Two populations7 Independent versus paired samples 2138 Hypothesis tests for means, proportions and variances of two independent samples 2199 A nonparametric hypothesis test for the medians of two independent samples 26310 Hypothesis tests for the population mean of two paired samples 28511 Two nonparametric hypothesis tests for paired samples 305Part Four More than two populations 32512 Hypothesis tests for more than two population means: one-way analysis of variance 32713 Nonparametric alternatives to an analysis of variance 37514 Hypothesis tests for more than two population variances 401Part Five More useful tests and procedures 41715 Design of experiments and data collection 41916 Testing equivalence 42717 Estimation and testing of correlation and association 44518 An introduction to regression modeling 48119 Simple linear regression 493A Binomial distribution 589B Standard normal distribution 593C X2-distribution 595D Student's t-distribution 597E Wilcoxon signed-rank test 599F Critical values for the Shapiro-Wilk test 605G Fisher's F-distribution 607H Wilcoxon rank-sum test 615I Studentized range or Q-distribution 625J Two-sided Dunnett test 629K One-sided Dunnett test 633L Kruskal-Wallis-Test 637M Rank correlation test 641Index 643