Jmp Start Statistics: A Guide to Statistics and Data Analysis Using Jmp, Sixth Edition

Description

This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises. Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including: The redesigned Formula Editor. New and improved ways to create formulas in JMP directly from the data table or dialogs. Interface updates, including improved menu layout. Updates and enhancements in many analysis platforms. New ways to get data into JMP and to save and share JMP results. Many new features that make it easier to use JMP. Contents......Page 3 Preface......Page 15 …on your own with JMP Help......Page 20 …reading about JMP......Page 21 Chapter Organization......Page 22 Typographical Conventions......Page 24 Hello!......Page 26 The JMP Starter (Macintosh)......Page 28 The JMP Home Window (Windows)......Page 29 Open a JMP Data Table......Page 31 Launch an Analysis Platform......Page 33 Interact with the Report Surface......Page 34 Special Tools......Page 37 Customize JMP......Page 38 Modeling Type......Page 40 Navigating Platforms and Building Context......Page 41 Contexts for the t-Test......Page 42 Contexts for Nonparametric Statistics......Page 43 The Personality of JMP......Page 44 Overview......Page 46 The Ins and Outs of a JMP Data Table......Page 48 Mousing around a Data Table: Cursor Forms......Page 49 Creating a New JMP Table......Page 51 Define Rows and Columns......Page 52 Enter Data......Page 54 The New Column Command......Page 55 Plot the Data......Page 56 Importing Data......Page 58 Importing Text Files......Page 60 Importing Other File Types......Page 63 Copy, Paste, and Drag Data......Page 65 Moving Data Out of JMP......Page 66 Copy and Paste......Page 67 Create Interactive Web Reports......Page 68 Pop-up Menu Commands......Page 69 Data Management......Page 70 Give New Shape to a Table: Stack Columns......Page 71 Create Summary Statistics with the Summary Command......Page 74 Create Summary Statistics with Tabulate......Page 77 Running Data Table Scripts......Page 79 Opening and Running Stand-alone Scripts......Page 80 Overview......Page 82 The Formula Editor Window......Page 84 The Formula Editor and the JMP Scripting Language......Page 85 A Quick Example: Standardizing Data......Page 86 Making a New Formula Column......Page 88 Using Popular Formula Functions......Page 90 Writing Conditional Expressions......Page 91 Summarizing Data with the Formula Editor......Page 96 Generating Random Data......Page 101 Local Variables and Table Variables......Page 106 Working with Dates......Page 108 Cutting, Dragging, and Pasting Formulas......Page 109 Exercises......Page 110 Overview......Page 114 The Yin and Yang of Statistics......Page 116 The Faces of Statistics......Page 117 Don’t Panic......Page 118 Three Levels of Uncertainty......Page 120 Assumptions......Page 121 Data Mining?......Page 122 Statistical Terms......Page 123 Overview......Page 127 Rolling Dice......Page 129 Flipping Coins, Sampling Candy, or Drawing Marbles......Page 132 Probability of Making a Triangle......Page 133 Confidence Intervals......Page 138 Data Table-Based Simulations......Page 139 Other JMP Simulators......Page 140 Exercises......Page 141 Overview......Page 143 Looking at Distributions......Page 146 Probability Distributions......Page 148 True Distribution Function or Real-World Sample Distribution......Page 149 The Normal Distribution......Page 151 Generating Random Data......Page 152 Histograms......Page 153 Stem-and-Leaf Plots......Page 155 Dot Plots......Page 156 Outlier and Quantile Box Plots......Page 157 Mean and Standard Deviation......Page 159 Mean versus Median......Page 160 Extremes, Tail Detail......Page 161 Confidence Intervals for the Mean......Page 162 Testing Hypotheses: Terminology......Page 165 The Normal z-Test for the Mean......Page 167 Case Study: The Earth’s Ecliptic......Page 168 Student’s t-Test......Page 170 Comparing the Normal and Student’s t Distributions......Page 171 Testing the Mean......Page 172 The p-Value Animation......Page 173 Power of the t-Test......Page 175 Practical Significance versus Statistical Significance......Page 177 Examining for Normality......Page 179 Normal Quantile Plots......Page 180 Statistical Tests for Normality......Page 183 Special Topic: Practical Difference......Page 185 Special Topic: Simulating the Central Limit Theorem......Page 188 Seeing Kernel Density Estimates......Page 190 Exercises......Page 191 Overview......Page 194 When the Difference Isn’t Significant......Page 196 Check the Data......Page 197 Launch the Fit Y by X Platform......Page 198 Examine the Plot......Page 199 Display and Compare the Means......Page 200 Inside the Student’s t-Test......Page 201 Equal or Unequal Variances?......Page 202 One-Sided Version of the Test......Page 204 Analysis of Variance and the All-Purpose F-Test......Page 205 How Sensitive Is the Test? How Many More Observations Are Needed?......Page 207 When the Difference Is Significant......Page 209 Normality and Normal Quantile Plots......Page 211 Testing Means for Matched Pairs......Page 213 Thermometer Tests......Page 214 Look at the Data......Page 215 Student’s t-Test......Page 216 The Matched Pairs Platform for a Paired t-Test......Page 217 Optional Topic: An Equivalent Test for Stacked Data......Page 220 Two Extremes of Neglecting the Pairing Situation: A Dramatization......Page 222 Paired Means: The Wilcoxon Signed-Rank Test......Page 228 Independent Means: The Wilcoxon Rank Sum Test......Page 230 Exercises......Page 231 Overview......Page 234 What Is a One-Way Layout?......Page 236 Comparing and Testing Means......Page 238 Means Diamonds: A Graphical Description of Group Means......Page 239 Statistical Tests to Compare Means......Page 240 Means Comparisons for Balanced Data......Page 243 Means Comparisons for Unbalanced Data......Page 244 Adjusting for Multiple Comparisons......Page 249 Are the Variances Equal across the Groups?......Page 252 Testing Means with Unequal Variances......Page 255 Review of Rank-Based Nonparametric Methods......Page 256 The Three Rank Tests in JMP......Page 257 Exercises......Page 259 Overview......Page 262 Least Squares......Page 264 Seeing Least Squares......Page 265 Fitting a Line and Testing the Slope......Page 267 Testing the Slope By Comparing Models......Page 269 The Distribution of the Parameter Estimates......Page 272 Confidence Intervals on the Estimates......Page 273 Exclusion of Rows......Page 275 Polynomial Models......Page 277 Higher-Order Polynomials......Page 278 Distribution of Residuals......Page 279 Transformed Fits......Page 280 Spline Fit......Page 282 Are Graphics Important?......Page 283 Why It’s Called Regression......Page 286 What Happens When X and Y Are Switched?......Page 288 Sometimes It’s the Picture That Fools You......Page 291 High-Order Polynomial Pitfall......Page 292 The Pappus Mystery on the Obliquity of the Ecliptic......Page 293 Exercises......Page 294 Overview......Page 297 Categorical Responses and Count Data: Two Outlooks......Page 299 A Simulated Categorical Response......Page 302 Simulating Some Categorical Response Data......Page 303 Variability in the Estimates......Page 304 Larger Sample Sizes......Page 306 Monte Carlo Simulations for the Estimators......Page 307 Distribution of the Estimates......Page 308 The X2 Pearson Chi-Square Test Statistic......Page 309 The G2 Likelihood-Ratio Chi-Square Test Statistic......Page 310 Likelihood Ratio Tests......Page 311 Univariate Categorical Chi-Square Tests......Page 312 Comparing Univariate Distributions......Page 313 Charting to Compare Results......Page 315 Exercises......Page 317 Overview......Page 318 Testing with G2 and X2 Statistic......Page 320 Looking at Survey Data......Page 321 Car Brand by Marital Status......Page 325 Car Brand by Size of Vehicle......Page 326 Two-Way Tables: Entering Count Data......Page 327 Expected Values under Independence......Page 328 Testing for Independence......Page 329 If You Have a Perfect Fit......Page 331 Special Topic: Correspondence Analysis— Looking at Data with Many Levels......Page 333 Fitting a Logistic Model......Page 336 Degrees of Fit......Page 340 A Discriminant Alternative......Page 341 Inverse Prediction......Page 342 Polytomous (Multinomial) Responses: More Than Two Levels......Page 345 Ordinal Responses: Cumulative Ordinal Logistic Regression......Page 346 Surprise: Simpson’s Paradox: Aggregate Data versus Grouped Data......Page 349 Generalized Linear Models......Page 352 Exercises......Page 357 Overview......Page 359 Regression Definitions......Page 361 A Multiple Regression Example......Page 362 Residuals and Predicted Values......Page 365 The Whole Model F-Test......Page 368 Whole-Model Leverage Plot......Page 369 Effect Leverage Plots......Page 370 Collinearity......Page 372 Exact Collinearity, Singularity, and Linear Dependency......Page 376 The Longley Data: An Example of Collinearity......Page 378 The Case of the Hidden Leverage Point......Page 380 Mining Data with Stepwise Regression......Page 383 Exercises......Page 387 Overview......Page 390 The General Linear Model......Page 392 Types of Effects in Linear Models......Page 393 Coding Scheme to Fit a One-Way anova as a Linear Model......Page 394 Regressor Construction......Page 397 Parameters and Means......Page 398 Analysis of Covariance: Continuous and Categorical Terms in the Same Model......Page 399 The Prediction Equation......Page 402 The Whole-Model Test and Leverage Plot......Page 403 Effect Tests and Leverage Plots......Page 404 Least Squares Means......Page 406 Lack of Fit......Page 407 Separate Slopes: When the Covariate Interacts with a Categorical Effect......Page 409 Two-Way Analysis of Variance and Interactions......Page 413 Optional Topic: Random Effects and Nested Effects......Page 419 Nesting......Page 420 Method 1: Random Effects-Mixed Model......Page 422 Method 2: Reduction to the Experimental Unit......Page 427 Method 3: Correlated Measurements-Multivariate Model......Page 429 Closing Thoughts......Page 431 Exercises......Page 432 Overview......Page 434 Key Concepts......Page 437 JMP DOE......Page 438 The Experiment......Page 439 Enter the Response and Factors......Page 440 Define the Model......Page 442 Perform Experiment and Enter Data......Page 445 Analyze the Model......Page 446 Details of the Design: Confounding Structure......Page 452 How the Custom Designer Works......Page 453 Choices in the Custom Designer......Page 454 An Interaction Model: The Reactor Data......Page 455 Analyzing the Reactor Data......Page 457 Where Do We Go from Here?......Page 463 Main Effects Only (a Review)......Page 465 All Two-Factor Interactions Involving a Single Factor......Page 466 Alias Optimal Designs......Page 468 Response Surface Designs in JMP......Page 469 Analyzing the Odor Response Surface Design......Page 471 Plotting Surface Effects......Page 474 Specifying Response Surface Effects Manually......Page 475 The Custom Designer versus the Response Surface Design Platform......Page 476 Split-Plot Designs......Page 477 Designing the Experiment......Page 478 Analysis of Split-Plot Designs......Page 480 Design Strategies......Page 484 DOE Glossary of Key Terms......Page 485 Exercises......Page 489 Overview......Page 492 Density Estimation......Page 494 Bivariate Density Estimation......Page 495 Mixtures, Modes, and Clusters......Page 497 The Elliptical Contours of the Normal Distribution......Page 498 Simulating Bivariate Correlations......Page 500 Correlations across Many Variables......Page 503 Bivariate Outliers......Page 504 Outliers in Three and More Dimensions......Page 507 Identify Variation with Principal Components Analysis......Page 509 Principal Components for Six Variables......Page 512 How Many Principal Components?......Page 514 Discriminant Analysis......Page 515 Canonical Plot......Page 516 Discriminant Scores......Page 517 Stepwise Discriminant Variable Selection......Page 520 Hierarchical Clustering: How Does It Work?......Page 521 A Real-World Example......Page 524 Some Final Thoughts......Page 527 Exercises......Page 528 Overview......Page 530 Recursive Partitioning (Decision Trees)......Page 532 Growing Trees......Page 534 Exploratory Modeling with Partition......Page 541 Saving Columns and Formulas......Page 543 Neural Nets......Page 544 A Simple Example......Page 545 Saving Columns......Page 548 Profiles in Neural......Page 550 Exercises......Page 554 Overview......Page 558 What Does a Control Chart Look Like......Page 561 Types of Control Charts......Page 562 Variables Charts......Page 563 Control Chart Basics......Page 564 Control Charts for Variables Data......Page 565 The Control Chart Builder Work Space......Page 566 Control Chart Builder Examples......Page 567 Control Charts for Attributes Data......Page 570 Presummarize Charts......Page 573 Uniformly Weighted Moving Average (UWMA) Charts......Page 574 Exponentially Weighted Moving Average (EWMA) Chart......Page 576 What Is Process Capability?......Page 577 Capability for One Process Measurement......Page 580 Capability for Many Process Measurements......Page 582 Capability for Time-Ordered Data......Page 585 Exercises......Page 587 Overview......Page 590 Fitting a Mean......Page 592 Testing a Hypothesis......Page 593 Effect of Sample Size Significance......Page 594 Effect of Error Variance on Significance......Page 595 Experimental Design’s Effect on Significance......Page 596 Simple Regression......Page 597 Leverage......Page 598 Mechanics of Fit for Categorical Responses......Page 599 How Do Pressure Cylinders Behave?......Page 600 Estimating Probabilities......Page 601 One-Way Layout for Categorical Data......Page 602 Logistic Regression......Page 604 Answers......Page 606 Chapter 7, “Univariate Distributions: One Variable, One Sample”......Page 609 Chapter 8, “The Difference Between Two Means”......Page 613 Chapter 9, “Comparing Many Means: One-Way Analysis of Variance”......Page 615 Chapter 10, “Fitting Curves through Points: Regression”......Page 619 Chapter 11, “Categorical Distributions”......Page 622 Chapter 12, “Categorical Models”......Page 623 Chapter 13, “Multiple Regression”......Page 625 Chapter 14, “Fitting Linear Models”......Page 626 Chapter 15, “Design of Experiments”......Page 627 Chapter 16, “Bivariate and Multivariate Relationships”......Page 628 Chapter 18, “Control Charts and Capability”......Page 630 Refs & Data Sources......Page 632 Index......Page 637 Blank Page......Page 1