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Jmp in 3, Windows Version [Diskette]

  • Formaat: Diskette, 543 pages, kaal: 1066 g, Illustrations
  • Ilmumisaeg: 18-Aug-1997
  • Kirjastus: Duxbury Press
  • ISBN-10: 0534265642
  • ISBN-13: 9780534265649
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Jmp in 3, Windows Version
  • Formaat: Diskette, 543 pages, kaal: 1066 g, Illustrations
  • Ilmumisaeg: 18-Aug-1997
  • Kirjastus: Duxbury Press
  • ISBN-10: 0534265642
  • ISBN-13: 9780534265649
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780534265649.html
Märksõnad:
JMP IN is the most complete and modern data-analysis program and combines outstanding graphics and amazing data analysis. JMP-INs spreadsheets, data tables, and graphing capabilities are designed to help the student visulaize and learn important statistical concepts while it handles all calculations. JMP IN, developed by SAS Institute is the student version of the professional package JMP.
Preface xiii
Preliminaries xvii
Part I JMPing IN WITH BOTH FEET
Jump Right in
1(16)
First Session
3(4)
Open a JMP Data Table
3(1)
Launch an Analysis Platform
4(1)
Interact with the Surface of the Platform
5(2)
Modeling Type
7(1)
Analyze and Graph
8(5)
The Analyze Menu
9(1)
The Graph Menu
10(1)
Navigating the Platforms, Building the Context
10(2)
The Personality of JMP
12(1)
Getting Help: The JMP Help System
13(4)
Help from the About JMP Screen
14(1)
Help From the JMP Statistical Guide
14(1)
Help from Buttons in Dialogs
15(1)
Help from a Platform Window
15(1)
Help by Clicking an Item
15(1)
Additional Help Commands under Windows
16(1)
JMP Data Tables
17(24)
The Ins and Outs of a JMP Data Table
19(9)
Selecting and Deselecting Rows and Columns
19(1)
Mousing Around a Spreadsheet: Cursor Forms
20(1)
Creating a New JMP Table
21(5)
Importing Data
26(1)
Cut, Copy, and Paste Spreadsheet Data
27(1)
Moving Data and Results Out of JMP
28(4)
The Save As Command
28(1)
Cut, Copy, and Paste Graphs and Reports
29(1)
Journal Data and Results
30(2)
Print Data, Reports, and Journals
32(1)
Juggling Data Tables
32(3)
Correct a Sort Problem: Subset, Sort, and Join
32(2)
Give Your Table a New Shape: Stack Columns
34(1)
The Group/Summary Command
35(6)
Create a Table of Summary Statistics
36(1)
Analyze Subsets of Data
37(2)
Plot and Chart Summary Data
39(2)
Calculator Adventures
41(30)
The Calculator Window
43(1)
A Quick Example
44(2)
Calculator Pieces and Parts
46(5)
Terminology
46(1)
The Calculator Work Panel
47(2)
The Formula Display
49(1)
Function Browser Definitions
49(2)
Terms Functions
51(2)
Using a Subscript
52(1)
Conditional Expressions and Comparison Operators
53(2)
Using the If, Otherwise Condition Function
53(2)
Summarize Down a Column or Summarize Across Rows
55(5)
The Nuts and Bolts of the Quantile Function
55(1)
A Quantile Function Challenge Problem
56(1)
Using the Summation Function
57(2)
The of Function Computes Statistics Across Rows
59(1)
Random Number Functions
60(3)
The Uniform Distribution
60(2)
The Normal Distribution
62(1)
The Shuffle Function
63(1)
Parameters
63(1)
Tips on Building Formulas
64(5)
Constant Expressions
66(1)
Focused Work Areas
66(1)
Cutting and Pasting Formulas
66(1)
Selecting Expressions
67(1)
Dragging Expressions
67(1)
Changing a Formula
68(1)
Caution and Error Messages
69(2)
Part II STATISTICAL SLEUTHING
What are Statistics?
71(14)
Ponderings
72(4)
The Business of Statistics
72(1)
Two Sides of Statistics
73(1)
The Faces of Statistics
74(1)
Don't Panic
75(1)
Preparations
76(4)
Three Levels of Uncertainty
76(1)
Probability and Randomness
77(1)
Assumptions
78(1)
Data-Mining?
79(1)
Statistical Terms
80(5)
Univariate Distribution: One Variable, One Sample
85(30)
Looking at Distributions
87(2)
Review: Probability Distributions
89(2)
True Distribution Function versus Real-World Sample Distribution
89(1)
The Normal Distribution
90(1)
Describing Distributions of Values
91(11)
Generating Random Data
92(1)
Histograms
93(1)
Outlier and Quantile Box Plots
94(1)
Normal Quantile Plots
95(3)
Stem-and-Leaf Plots
98(1)
Mean and Standard Deviation
99(1)
Median and Standard Deviation
99(1)
Mean versus Median
100(1)
Higher Moments: Skewness and Kurtosis
101(1)
Extremes, Tail Detail
101(1)
Statistical Inference on the Mean
102(10)
Standard Error of the Mean
102(1)
Confidence Intervals for the Mean
102(2)
Testing Hypotheses: Terminology
104(1)
The Normal Z Test for the Mean
105(3)
Student's t Test
108(1)
Curiosity: A Significant Difference?
109(3)
Special Topic: Testing for Normality
112(1)
Special Topic: Simulating the Central Limit Theorem
113(2)
Differences Between Two Means
115(34)
Two Independent Groups
117(16)
When the Difference Isn't Significant
117(4)
Inside the Student's t Test:
121(1)
Analysis of Variance and the All-Purpose F Test
122(3)
How Sensitive is the Test? How Many More Observations Needed?
125(2)
When the Difference Is Significant
127(1)
Special Topic: Are the Variances Equal Across the Groups?
128(4)
Special Topic: Normality and Normal Quantile Plots
132(1)
Testing Means for Matched Pairs
133(10)
Thermometer Tests
133(3)
An Alternative Approach for the Paired t Test
136(2)
An Equivalent Test For Stacked Data
138(1)
Special Topic: Examining the Normality Assumption
139(1)
Two Extremes of Neglecting the Pairing Situation: A Dramatization
140(3)
Review
143(1)
Mouse Mystery
144(1)
A Nonparametric Approach
144(5)
Introduction to Nonparametric Methods
144(1)
Paired Means: The Wilcoxon Signed-Rank Test
145(1)
Independent Means: The Wilcoxon Rank Sum Test
146(3)
Comparing Many Means: One-Way Analysis of Variance
149(22)
What Is a One-Way Layout?
151(1)
Comparing and Testing Means
152(9)
Means Diamonds: A Graphical Description of Group Means
154(1)
Statistical Tests to Compare Means
154(3)
Means Comparisons for Balanced Data
157(1)
Special Topic: Means Comparisons for Unbalanced Data
157(4)
Special Topic: Adjusting for Multiple Comparisons
161(1)
Special Topic: Power
162(4)
Special Topic: Unequal Variances
166(2)
Special Topic: Nonparametric Methods
168(3)
Review of Rank-Based Nonparametric Methods
168(1)
The Three Rank Tests in JMP
169(2)
Fitting Curves Through Points: Regression
171(24)
Regression
173(12)
Least Squares
173(1)
Fitting the Line and Testing the Slope
174(6)
Examine Residuals
180(1)
Polynomial Models
181(2)
Transformed Fits
183(1)
Spline Fit
184(1)
Why Graphics Are Important
185(2)
Why It's Called Regression:
187(4)
Curiosities
191(4)
Sometimes It's the Picture That Fools You
191(1)
High Order Polynomial Pitfall
191(1)
The Pappus Mystery on the Obliquity of the Ecliptic
192(3)
Categorical Distributions
195(18)
Categorical Situations
197(1)
Categorical Responses and Count Data: Two Outlooks
197(3)
A Simulated Categorical Response
200(5)
Simulating Some Categorical Response Data
200(1)
Variability in the Estimates
201(2)
Larger Sample Sizes
203(1)
Monte Carlo Simulations for the Estimators
203(1)
Distribution of the Estimates
204(1)
The X2 Pearson Chi-Square Test Statistic
205(1)
The G2 Likelihood Ratio Chi-Square Test Statistic
206(3)
Likelihood Ratio Tests
207(1)
The G2 Likelihood Ratio Chi-Square Test
208(1)
Univariate Categorical Chi-Square Tests
209(4)
Comparing Univariate Distributions
209(2)
Charting to Compare Results
211(2)
Categorical Models
213(26)
Fitting Categorical Responses to Categorical Factors:
215(7)
Looking at Survey Data
216(4)
If You Have a Perfect Fit
220(2)
Correspondence Analysis: Looking at Data with Many Levels
222(2)
Continuous Factors for Categorical Responses: Logistic Regression
224(4)
Fitting a Logistic Model
224(3)
Degrees of Fit
227(1)
Special Topics
228(6)
A Discriminant Alternative
228(1)
Inverse Prediction
229(2)
Polytomous: More Than 2 Response Levels
231(1)
Ordinal Responses: Cumulative Ordinal Logistic Regression
232(2)
Surprise: Simpson's Paradox: Aggregate Data versus Grouped Data
234(5)
Multiple Regression
239(24)
Parts of a Regression Model
241(1)
A Multiple Regression Example
242(7)
Residuals and Predicted Values
244(1)
The Analysis of Variance Table
245(1)
The Whole Model F Test
246(1)
Whole Model Leverage Plot
246(1)
Details on Effect Tests
247(1)
Effect Leverage Plots
248(1)
Special Topic: Collinearity
249(7)
Exact Collinearity, Singularity, Linear Dependency
252(2)
The Longley Data: An Example of Collinearity
254(2)
Special Topic: The Case of the Hidden Leverage Point
256(2)
Special Topic: Mining Data with Stepwise Regression
258(5)
Fitting Linear Models
263(36)
The General Linear Model
265(18)
Kinds of Effects in Linear Models
266(1)
Coding Scheme to Fit a One-Way Anova as a Linear Model
267(5)
Analysis of Covariance: Putting Continuous and Classification Terms in the Same Model
272(11)
Two-way Analysis of Variance and Interactions
283(5)
With Interaction
285(3)
Optional Topic: Random Effects and Nested Effects
288(11)
Nesting
289(2)
Repeated Measures
291(8)
Bivariate and Multivariate Relationships
299(20)
Bivariate Distributions
301(5)
Density Estimation
301(1)
Bivariate Density Estimation
302(1)
Mixtures, Modes, and Clusters
303(1)
The Elliptical Contours of the Normal Distribution
304(2)
Correlations and the Bivariate Normal
306(5)
Simulation Exercise
306(2)
Correlations Across Many Variables
308(1)
Bivariate Outliers
309(2)
Three and More Dimensions
311(8)
Principal Components
312(1)
Principal Components for Six Variables
313(2)
Correlation Patterns in Biplots
315(1)
Outliers in Six Dimensions
315(2)
Strategies for High-Dimensional Exploration
317(2)
Design of Experiments
319(34)
Introduction
321(1)
Generating an Experimental Design in JMP
322(1)
Two-Level Screening Designs
322(2)
Screening for Main Effects: The Flour Paste Experiment
324(12)
Name the Factors
325(1)
Select the Design and Generate the Table
325(5)
Run a Screening Model
330(6)
Screening for Interactions
336(5)
Response Surface Designs
341(12)
The Response Surface Design Dialog
341(12)
Statistical Quality Control
353(22)
Control Charts and Shewhart Charts
355(1)
Variables Charts
356(1)
Attributes Charts
356(1)
The Control Chart Dialog
356(12)
Process Information Panel
357(1)
Chart Type Information Panel
358(1)
Limits Specification Panel
359(1)
Tests Selection Panel
359(1)
Types of Control Charts for Variables
360(2)
Types of Control Charts for Attributes
362(1)
Moving Average Charts
363(3)
Tailoring the Horizontal Axis
366(1)
Tests for Special Causes
366(2)
Pareto Charts
368(7)
A Simple Pareto Chart
369(1)
Before-and-After Pareto Chart
370(2)
Two-Way Comparative Pareto Chart
372(3)
Time Series
375(22)
Introduction
376(1)
Graphing and Fitting by Time
377(8)
Creating Time Columns
377(1)
Graphing by Time
378(1)
Trend and Seasonal Factors
379(6)
Lagging and Autocorrelation
385(4)
Creating Columns with Lagged Values
385(1)
Autocorrelation
386(1)
Optional Topic: Correlograms
387(2)
Autoregressive Models
389(2)
Special Topic: Fitting an AR Model in the Nonlinear Platform
391(2)
Special Topic: Moving Average and ARMA Models
393(1)
Special Topic: Simulating Time Series Processes
394(1)
Autoregressive Errors in Regression Models
395(2)
Machines of Fit
397(14)
Machines of Fit
398(1)
Springs for Continuous Responses
398(7)
Fitting a Mean
399(1)
Testing a Hypothesis
399(1)
One-Way Layout
400(1)
Sample Size's Effect on Significance
400(1)
Error Variance's Effect on Significance
401(1)
Experimental Design's Effect on Significance
402(1)
Simple Regression
403(1)
Leverage
404(1)
Multiple Regression
404(1)
Summary: Significance and Power
404(1)
Machine of Fit for Categorical Responses
405(6)
How Do Pressure Cylinders Behave?
405(1)
Estimating Probabilities
406(1)
One-Way Layout for Categorical Data
407(2)
Logistic Regression
409(2)
Part III REFERENCE DOCUMENTATION
Appendix A The JMP Main Menu 411(30)
Appendix B Analyze and Graph Platform Commands and Options 441(36)
Appendix C Calculator Functions 477(20)
Appendix D What's In JMP That's Not In JMP IN 497(4)
References 501(4)
Index 505