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.
- 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.
Part One Estimators and tests 1
1 Estimating population parameters 3
2 Interval estimators 37
3 Hypothesis tests 71
Part Two One population 103
4 Hypothesis tests for a population mean, proportion or variance 105
5 Two hypothesis tests for the median of a population 149
6 Hypothesis tests for the distribution of a population 175
Part Three Two populations
7 Independent versus paired samples 213
8 Hypothesis tests for means, proportions and variances of two independent samples 219
9 A nonparametric hypothesis test for the medians of two independent samples 263
10 Hypothesis tests for the population mean of two paired samples 285
11 Two nonparametric hypothesis tests for paired samples 305
Part Four More than two populations 325
12 Hypothesis tests for more than two population means: one-way analysis of variance 327
13 Nonparametric alternatives to an analysis of variance 375
14 Hypothesis tests for more than two population variances 401
Part Five More useful tests and procedures 417
15 Design of experiments and data collection 419
16 Testing equivalence 427
17 Estimation and testing of correlation and association 445
18 An introduction to regression modeling 481
19 Simple linear regression 493
A Binomial distribution 589
B Standard normal distribution 593
C X2-distribution 595
D Student’s t-distribution 597
E Wilcoxon signed-rank test 599
F Critical values for the Shapiro-Wilk test 605
G Fisher’s F-distribution 607
H Wilcoxon rank-sum test 615
I Studentized range or Q-distribution 625
J Two-sided Dunnett test 629
K One-sided Dunnett test 633
L Kruskal-Wallis-Test 637
M Rank correlation test 641