About the Authors |
|
xvii | |
Preface |
|
xxi | |
|
Chapter 1 Data and Statistics |
|
|
1 | (32) |
|
Statistics in Practice: Bloomberg Businessweek |
|
|
2 | (1) |
|
1.1 Applications in Business and Economics |
|
|
3 | (2) |
|
|
3 | (1) |
|
|
3 | (1) |
|
|
4 | (1) |
|
|
4 | (1) |
|
|
4 | (1) |
|
|
4 | (1) |
|
|
5 | (5) |
|
Elements, Variables, and Observations |
|
|
5 | (1) |
|
|
5 | (2) |
|
Categorical and Quantitative Data |
|
|
7 | (1) |
|
Cross-Sectional and Time Series Data |
|
|
8 | (2) |
|
|
10 | (3) |
|
|
10 | (1) |
|
|
11 | (1) |
|
|
12 | (1) |
|
|
13 | (1) |
|
|
13 | (1) |
|
1.4 Descriptive Statistics |
|
|
13 | (2) |
|
1.5 Statistical Inference |
|
|
15 | (1) |
|
|
16 | (1) |
|
1.7 Big Data and Data Mining |
|
|
17 | (2) |
|
1.8 Computers and Statistical Analysis |
|
|
19 | (1) |
|
1.9 Ethical Guidelines for Statistical Practice |
|
|
19 | (2) |
|
|
21 | (1) |
|
|
21 | (1) |
|
|
22 | (8) |
|
Appendix 1.1 Opening and Saving DATA Files and Converting to Stacked form with JMP |
|
|
30 | (3) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Getting Started with R and RStudio |
|
|
|
Appendix: Basic Data Manipulation with R |
|
|
|
Chapter 2 Descriptive Statistics: Tabular and Graphical Displays |
|
|
33 | (74) |
|
Statistics in Practice: Colgate-Palmolive Company |
|
|
34 | (1) |
|
2.1 Summarizing Data for a Categorical Variable |
|
|
35 | (7) |
|
|
35 | (1) |
|
Relative Frequency and Percent Frequency Distributions |
|
|
36 | (1) |
|
Bar Charts and Pie Charts |
|
|
37 | (5) |
|
2.2 Summarizing Data for a Quantitative Variable |
|
|
42 | (15) |
|
|
42 | (2) |
|
Relative Frequency and Percent Frequency Distributions |
|
|
44 | (1) |
|
|
45 | (1) |
|
|
45 | (2) |
|
|
47 | (1) |
|
|
47 | (10) |
|
2.3 Summarizing Data for Two Variables Using Tables |
|
|
57 | (8) |
|
|
57 | (2) |
|
|
59 | (6) |
|
2.4 Summarizing Data for Two Variables Using Graphical Displays |
|
|
65 | (6) |
|
Scatter Diagram and Trendline |
|
|
65 | (1) |
|
Side-by-Side and Stacked Bar Charts |
|
|
66 | (5) |
|
2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays |
|
|
71 | (6) |
|
Creating Effective Graphical Displays |
|
|
71 | (1) |
|
Choosing the Type of Graphical Display |
|
|
72 | (1) |
|
|
73 | (2) |
|
Data Visualization in Practice: Cincinnati Zoo and Botanical Garden |
|
|
75 | (2) |
|
|
77 | (1) |
|
|
78 | (1) |
|
|
79 | (1) |
|
|
80 | (5) |
|
Case Problem 1 Pelican Stores |
|
|
85 | (1) |
|
Case Problem 2 Movie Theater Releases |
|
|
86 | (1) |
|
Case Problem 3 Queen City |
|
|
87 | (1) |
|
Case Problem 4 Cut-Rate Machining, Inc. |
|
|
88 | (2) |
|
Appendix 2.1 Creating Tabular and Graphical Presentations with JMP |
|
|
90 | (3) |
|
Appendix 2.2 Creating Tabular and Graphical Presentations with Excel |
|
|
93 | (14) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Creating Tabular and Graphical Presentations with R |
|
|
|
Chapter 3 Descriptive Statistics: Numerical Measures |
|
|
107 | (70) |
|
Statistics in Practice: Small Fry Design |
|
|
108 | (1) |
|
|
109 | (13) |
|
|
109 | (2) |
|
|
111 | (1) |
|
|
112 | (1) |
|
|
113 | (2) |
|
|
115 | (1) |
|
|
115 | (1) |
|
|
116 | (6) |
|
3.2 Measures of Variability |
|
|
122 | (7) |
|
|
123 | (1) |
|
|
123 | (1) |
|
|
123 | (2) |
|
|
125 | (1) |
|
|
126 | (3) |
|
3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers |
|
|
129 | (8) |
|
|
129 | (1) |
|
|
130 | (1) |
|
|
131 | (1) |
|
|
132 | (2) |
|
|
134 | (3) |
|
3.4 Five-Number Summaries and Boxplots |
|
|
137 | (5) |
|
|
138 | (1) |
|
|
138 | (1) |
|
Comparative Analysis Using Boxplots |
|
|
139 | (3) |
|
3.5 Measures of Association Between Two Variables |
|
|
142 | (8) |
|
|
142 | (2) |
|
Interpretation of the Covariance |
|
|
144 | (2) |
|
|
146 | (1) |
|
Interpretation of the Correlation Coefficient |
|
|
147 | (3) |
|
3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness |
|
|
150 | (3) |
|
|
153 | (1) |
|
|
154 | (1) |
|
|
155 | (1) |
|
|
156 | (6) |
|
Case Problem 1 Pelican Stores |
|
|
162 | (1) |
|
Case Problem 2 Movie Theater Releases |
|
|
163 | (1) |
|
Case Problem 3 Business Schools of Asia-Pacific |
|
|
164 | (1) |
|
Case Problem 4 Heavenly Chocolates Website Transactions |
|
|
164 | (2) |
|
Case Problem 5 African Elephant Populations |
|
|
166 | (2) |
|
Appendix 3.1 Descriptive Statistics with JMP |
|
|
168 | (3) |
|
Appendix 3.2 Descriptive Statistics with Excel |
|
|
171 | (6) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Descriptive Statistics with R |
|
|
|
Chapter 4 Introduction to Probability |
|
|
177 | (46) |
|
Statistics in Practice: National Aeronautics and Space Administration |
|
|
178 | (1) |
|
4.1 Random Experiments, Counting Rules, and Assigning Probabilities |
|
|
179 | (10) |
|
Counting Rules, Combinations, and Permutations |
|
|
180 | (4) |
|
|
184 | (1) |
|
Probabilities for the KP&L Project |
|
|
185 | (4) |
|
4.2 Events and Their Probabilities |
|
|
189 | (4) |
|
4.3 Some Basic Relationships of Probability |
|
|
193 | (6) |
|
|
193 | (1) |
|
|
194 | (5) |
|
4.4 Conditional Probability |
|
|
199 | (8) |
|
|
202 | (1) |
|
|
202 | (5) |
|
|
207 | (6) |
|
|
210 | (3) |
|
|
213 | (1) |
|
|
213 | (1) |
|
|
214 | (1) |
|
|
215 | (5) |
|
Case Problem 1 Hamilton County Judges |
|
|
220 | (1) |
|
Case Problem 2 Rob's Market |
|
|
220 | (3) |
|
Chapter 5 Discrete Probability Distributions |
|
|
223 | (58) |
|
Statistics in Practice: 5.1 Random Variables |
|
|
225 | (3) |
|
Discrete Random Variables |
|
|
225 | (1) |
|
Continuous Random Variables |
|
|
225 | (3) |
|
5.2 Developing Discrete Probability Distributions |
|
|
228 | (5) |
|
5.3 Expected Value and Variance |
|
|
233 | (5) |
|
|
233 | (1) |
|
|
233 | (5) |
|
5.4 Bivariate Distributions, Covariance, and Financial Portfolios |
|
|
238 | (6) |
|
A Bivariate Empirical Discrete Probability Distribution |
|
|
238 | (3) |
|
|
241 | (3) |
|
|
244 | (3) |
|
5.5 Binomial Probability Distribution |
|
|
247 | (11) |
|
|
248 | (1) |
|
Martin Clothing Store Problem |
|
|
249 | (4) |
|
Using Tables of Binomial Probabilities |
|
|
253 | (1) |
|
Expected Value and Variance for the Binomial Distribution |
|
|
254 | (4) |
|
5.6 Poisson Probability Distribution |
|
|
258 | (4) |
|
An Example Involving Time Intervals |
|
|
259 | (1) |
|
An Example Involving Length or Distance Intervals |
|
|
260 | (2) |
|
5.7 Hypergeometric Probability Distribution |
|
|
262 | (3) |
|
|
265 | (1) |
|
|
266 | (1) |
|
|
266 | (2) |
|
|
268 | (4) |
|
Case Problem 1 Go Bananas. Breakfast Cereal |
|
|
272 | (1) |
|
Case Problem 2 McNeil's Auto Mall |
|
|
273 | (1) |
|
Case Problem 3 Grievance Committee at Tuglar Corporation |
|
|
273 | (2) |
|
Appendix 5.1 Discrete Probability Distributions with JMP |
|
|
275 | (3) |
|
Appendix 5.2 Discrete Probability Distributions with Excel |
|
|
278 | (3) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Discrete Probability Distributions with R |
|
|
|
Chapter 6 Continuous Probability Distributions |
|
|
281 | (38) |
|
Statistics in Practice: Procter & Gamble |
|
|
282 | (1) |
|
6.1 Uniform Probability Distribution |
|
|
283 | (4) |
|
Area as a Measure of Probability |
|
|
284 | (3) |
|
6.2 Normal Probability Distribution |
|
|
287 | (12) |
|
|
287 | (2) |
|
Standard Normal Probability Distribution |
|
|
289 | (5) |
|
Computing Probabilitiesfor Any Normal Probability Distribution |
|
|
294 | (1) |
|
Grear Tire Company Problem |
|
|
294 | (5) |
|
6.3 Normal Approximation of Binomial Probabilities |
|
|
299 | (3) |
|
6.4 Exponential Probability Distribution |
|
|
302 | (3) |
|
Computing Probabilities for the Exponential Distribution |
|
|
303 | (1) |
|
Relationship Between the Poisson and Exponential Distributions |
|
|
303 | (2) |
|
|
305 | (1) |
|
|
306 | (1) |
|
|
306 | (1) |
|
|
306 | (4) |
|
Case Problem 1 Specialty Toys |
|
|
310 | (1) |
|
Case Problem 2 Gebhardt Electronics |
|
|
311 | (1) |
|
Appendix 6.1 Continuous Probability Distributions with JMP |
|
|
312 | (6) |
|
Appendix 6.2 Continuous Probability Distributions with Excel |
|
|
318 | (1) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Continuous Probability Distributions with R |
|
|
|
Chapter 7 Sampling and Sampling Distributions |
|
|
319 | (56) |
|
Statistics in Practice: The Food and Agriculture Organization |
|
|
320 | (2) |
|
7.1 The Electronics Associates Sampling Problem |
|
|
322 | (1) |
|
|
322 | (5) |
|
Sampling from a Finite Population |
|
|
322 | (2) |
|
Sampling from an Infinite Population |
|
|
324 | (3) |
|
|
327 | (4) |
|
|
329 | (2) |
|
7.4 Introduction to Sampling Distributions |
|
|
331 | (2) |
|
7.5 Sampling Distribution of x |
|
|
333 | (10) |
|
|
334 | (1) |
|
|
334 | (1) |
|
Form of the Sampling Distribution of x |
|
|
335 | (2) |
|
Sampling Distribution of x for the EAI Problem |
|
|
337 | (1) |
|
Practical Value of the Sampling Distribution of x |
|
|
338 | (1) |
|
Relationship Between the Sample Size and the Sampling Distribution of x |
|
|
339 | (4) |
|
7.6 Sampling Distribution of p |
|
|
343 | (6) |
|
|
344 | (1) |
|
|
344 | (1) |
|
Form of the Sampling Distribution of p |
|
|
345 | (1) |
|
Practical Value of the Sampling Distribution of p |
|
|
345 | (4) |
|
7.7 Properties of Point Estimators |
|
|
349 | (2) |
|
|
349 | (1) |
|
|
350 | (1) |
|
|
351 | (1) |
|
7.8 Other Sampling Methods |
|
|
351 | (3) |
|
Stratified Random Sampling |
|
|
352 | (1) |
|
|
352 | (1) |
|
|
353 | (1) |
|
|
353 | (1) |
|
|
354 | (1) |
|
7.9 Big Data and Standard Errors of Sampling Distributions |
|
|
354 | (6) |
|
|
354 | (1) |
|
|
355 | (1) |
|
|
356 | (1) |
|
Understanding What Big Data Is |
|
|
356 | (1) |
|
Implications of Big Data for Sampling Error |
|
|
357 | (3) |
|
|
360 | (1) |
|
|
361 | (1) |
|
|
362 | (1) |
|
|
363 | (3) |
|
Case Problem 1 Marion Dairies |
|
|
366 | (1) |
|
Case Problem 2 Profitability of Small Restaurants |
|
|
367 | (1) |
|
Appendix 7.1 The Expected Value and Standard Deviation of x |
|
|
368 | (1) |
|
Appendix 7.2 Random Sampling with JMP |
|
|
369 | (3) |
|
Appendix 7.3 Random Sampling with Excel |
|
|
372 | (3) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Random Sampling with R |
|
|
|
Chapter 8 Interval Estimation |
|
|
375 | (46) |
|
Statistics in Practice: Food Lion |
|
|
376 | (1) |
|
8.1 Population Mean: cr Known |
|
|
377 | (6) |
|
Margin of Error and the Interval Estimate |
|
|
377 | (4) |
|
|
381 | (2) |
|
8.2 Population Mean: a Unknown |
|
|
383 | (5) |
|
Margin of Error and the Interval Estimate |
|
|
384 | (3) |
|
|
387 | (1) |
|
|
387 | (1) |
|
Summary of Interval Estimation Procedures |
|
|
388 | (4) |
|
8.3 Determining the Sample Size |
|
|
392 | (3) |
|
8.4 Population Proportion |
|
|
395 | (5) |
|
Determining the Sample Size |
|
|
396 | (4) |
|
8.5 Big Data and Confidence Intervals |
|
|
400 | (3) |
|
Big Data and the Precision of Confidence Intervals |
|
|
400 | (1) |
|
Implications of Big Data for Confidence Intervals |
|
|
401 | (2) |
|
|
403 | (1) |
|
|
404 | (1) |
|
|
404 | (1) |
|
|
405 | (4) |
|
Supplementary Case Problem 1: Young Professional Magazine |
|
|
408 | (1) |
|
Case Problem 2 Gulf Real Estate Properties |
|
|
409 | (2) |
|
Case Problem 3 Garza Research, Inc. |
|
|
411 | (1) |
|
Case Problem 4 Go-Fer Meal Delivery Service |
|
|
412 | (1) |
|
Appendix 8.1 Interval Estimation with JMP |
|
|
413 | (4) |
|
Appendix 8.2 Interval Estimation with Excel |
|
|
417 | (4) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Interval Estimation of a Population Mean, Known Standard Deviation with R |
|
|
|
Appendix: Interval Estimation of a Population Mean, Unknown Standard Deviation with R |
|
|
|
Appendix: Interval Estimation of a Population Proportion with R |
|
|
|
Chapter 9 Hypothesis Tests |
|
|
421 | (66) |
|
Statistics in Practice: John Morrell & Company |
|
|
422 | (1) |
|
9.1 Developing Null and Alternative Hypotheses |
|
|
423 | (2) |
|
The Alternative Hypothesis as a Research Hypothesis |
|
|
423 | (1) |
|
The Null Hypothesis as an Assumption to Be Challenged |
|
|
424 | (1) |
|
Summary of Forms for Null and Alternative Hypotheses |
|
|
425 | (1) |
|
9.2 Type I and Type II Errors |
|
|
426 | (3) |
|
9.3 Population Mean: a Known |
|
|
429 | (5) |
|
|
429 | (5) |
|
|
434 | (3) |
|
|
434 | (3) |
|
Summary and Practical Advice |
|
|
437 | (6) |
|
Relationship Between Interval Estimation and Hypothesis Testing |
|
|
438 | (5) |
|
9.4 Population Mean: cr Unknown |
|
|
443 | (2) |
|
|
443 | (1) |
|
|
444 | (1) |
|
Summary and Practical Advice |
|
|
445 | (4) |
|
9.5 Population Proportion |
|
|
449 | (2) |
|
|
451 | (3) |
|
9.6 Hypothesis Testing and Decision Making |
|
|
454 | (1) |
|
9.7 Calculating the Probability of Type II Errors |
|
|
454 | (5) |
|
9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean |
|
|
459 | (4) |
|
9.9 Big Data and Hypothesis Testing |
|
|
463 | (3) |
|
Big Data, Hypothesis Testing, and p Values |
|
|
463 | (1) |
|
Implications of Big Data in Hypothesis Testing |
|
|
464 | (2) |
|
|
466 | (1) |
|
|
466 | (1) |
|
|
467 | (1) |
|
|
467 | (4) |
|
Case Problem 1 Quality Associates, Inc. |
|
|
471 | (2) |
|
Case Problem 2 Ethical Behavior of Students at Bayview University |
|
|
473 | (2) |
|
Appendix 9.1 Hypothesis Testing with JMP |
|
|
475 | (6) |
|
Appendix 9.2 Hypothesis Testing with Excel |
|
|
481 | (6) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Hypothesis Testing of a Population Mean, Known Standard Deviation with R |
|
|
|
Appendix: Hypothesis Testing of a Population Mean, Unknown Standard Deviation with R |
|
|
|
Appendix: Hypothesis Testing of a Population Proportion with R |
|
|
|
Chapter 10 Inference About Means and Proportions with Two Populations |
|
|
487 | (44) |
|
Statistics in Practice: U.S. Food and Drug Administration |
|
|
488 | (1) |
|
10.1 Inferences About the Difference Between Two Population Means: σ1 and σ2 Known |
|
|
489 | (6) |
|
Interval Estimation of μ1 - μ2 |
|
|
489 | (2) |
|
Hypothesis Tests About μ1 - μ2 |
|
|
491 | (2) |
|
|
493 | (2) |
|
10.2 Inferences About the Difference Between Two Population Means: σ1, and σ2 Unknown |
|
|
495 | (8) |
|
Interval Estimation of μ1 - μ2 |
|
|
496 | (1) |
|
Hypothesis Tests About μ1 - μ2 |
|
|
497 | (2) |
|
|
499 | (4) |
|
10.3 Inferences About the Difference Between Two Population Means: Matched Samples |
|
|
503 | (6) |
|
10.4 Inferences About the Difference Between Two Population Proportions |
|
|
509 | (6) |
|
Interval Estimation of p1 -- p2 |
|
|
509 | (2) |
|
Hypothesis Tests About p1 -- p2 |
|
|
511 | (4) |
|
|
515 | (1) |
|
|
515 | (1) |
|
|
515 | (2) |
|
|
517 | (3) |
|
|
520 | (1) |
|
Appendix 10.1 Inferences About Two Populations with JMP |
|
|
521 | (5) |
|
Appendix 10.2 Inferences About Two Populations with Excel |
|
|
526 | (5) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Inferences About Two Populations with R |
|
|
|
Chapter 11 Inferences About Population Variances |
|
|
531 | (28) |
|
Statistics in Practice: U.S. Government Accountability Office |
|
|
532 | (1) |
|
11.1 Inferences About a Population Variance |
|
|
533 | (10) |
|
|
533 | (4) |
|
|
537 | (6) |
|
11.2 Inferences About Two Population Variances |
|
|
543 | (7) |
|
|
550 | (1) |
|
|
550 | (1) |
|
|
550 | (2) |
|
Case Problem 1 Air Force Training Program |
|
|
552 | (1) |
|
Case Problem 2 Meticulous Drill & Reamer |
|
|
553 | (2) |
|
Appendix 11.1 Population Variances with JMP |
|
|
555 | (2) |
|
Appendix 11.2 Population Variances with Excel |
|
|
557 | (2) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Population Variances with R |
|
|
|
Chapter 12 Comparing Multiple Proportions, Test of Independence, and Goodness of Fit |
|
|
559 | (44) |
|
Statistics in Practice: United Way |
|
|
560 | (1) |
|
12.1 Testing the Equality of Population Proportions for Three or More Populations |
|
|
561 | (10) |
|
A Multiple Comparison Procedure |
|
|
566 | (5) |
|
12.2 Test of Independence |
|
|
571 | (8) |
|
12.3 Goodness of Fit Test |
|
|
579 | (9) |
|
Multinomial Probability Distribution |
|
|
579 | (3) |
|
Normal Probability Distribution |
|
|
582 | (6) |
|
|
588 | (1) |
|
|
588 | (1) |
|
|
589 | (1) |
|
|
589 | (4) |
|
Case Problem 1 A Bipartisan Agenda for Change |
|
|
593 | (1) |
|
Case Problem 2 Fuentes Salty Snacks, Inc. |
|
|
594 | (1) |
|
Case Problem 3 Fresno Board Games |
|
|
594 | (2) |
|
Appendix 12.1 Chi-Square Tests with Jmp |
|
|
596 | (4) |
|
Appendix 12.2 Chi-Square Tests with Excel |
|
|
600 | (3) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Chi-Square Tests with R |
|
|
|
Chapter 13 Experimental Design and Analysis of Variance |
|
|
603 | (56) |
|
Statistics in Practice: Burke, Inc. |
|
|
604 | (1) |
|
13.1 An Introduction to Experimental Design and Analysis of Variance |
|
|
605 | (5) |
|
|
606 | (1) |
|
Assumptions for Analysis of Variance |
|
|
607 | (1) |
|
Analysis of Variance: A Conceptual Overview |
|
|
607 | (3) |
|
13.2 Analysis of Variance and the Completely Randomized Design |
|
|
610 | (11) |
|
Between-Treatments Estimate of Population Variance |
|
|
611 | (1) |
|
Within-Treatments Estimate of Population Variance |
|
|
612 | (1) |
|
Comparing the Variance Estimates: The FTest |
|
|
612 | (2) |
|
|
614 | (1) |
|
Computer Results for Analysis of Variance |
|
|
615 | (1) |
|
Testing for the Equality of k Population Means: An Observational Study |
|
|
616 | (5) |
|
13.3 Multiple Comparison Procedures |
|
|
621 | (6) |
|
|
621 | (2) |
|
|
623 | (4) |
|
13.4 Randomized Block Design |
|
|
627 | (6) |
|
Air Traffic Controller Stress Test |
|
|
627 | (2) |
|
|
629 | (1) |
|
Computations and Conclusions |
|
|
629 | (4) |
|
13.5 Factorial Experiment |
|
|
633 | (8) |
|
|
635 | (1) |
|
Computations and Conclusions |
|
|
635 | (6) |
|
|
641 | (1) |
|
|
641 | (1) |
|
|
642 | (2) |
|
|
644 | (4) |
|
Case Problem 1 Wentworth Medical Center |
|
|
648 | (1) |
|
Case Problem 2 Compensation for Sales Professionals |
|
|
649 | (1) |
|
Case Problem 3 Touristopia Travel |
|
|
650 | (2) |
|
Appendix 13.1 Analysis of Variance with JMP |
|
|
652 | (3) |
|
Appendix 13.2 Analysis of Variance with Excel |
|
|
655 | (4) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Analysis of Variance with R |
|
|
|
Chapter 14 Simple Linear Regression |
|
|
659 | (82) |
|
Statistics in Practice: Alliance Data Systems |
|
|
660 | (1) |
|
14.1 Simple Linear Regression Model |
|
|
661 | (3) |
|
Regression Model and Regression Equation |
|
|
661 | (1) |
|
Estimated Regression Equation |
|
|
662 | (2) |
|
14.2 Least Squares Method |
|
|
664 | (10) |
|
14.3 Coefficient of Determination |
|
|
674 | (7) |
|
|
677 | (4) |
|
|
681 | (1) |
|
14.5 Testing for Significance |
|
|
682 | (8) |
|
|
682 | (1) |
|
|
683 | (2) |
|
Confidence Interval for σ3 |
|
|
685 | (1) |
|
|
685 | (2) |
|
Some Cautions About the Interpretation of Significance Tests |
|
|
687 | (3) |
|
14.6 Using the Estimated Regression Equation for Estimation and Prediction |
|
|
690 | (7) |
|
|
691 | (1) |
|
Confidence Interval for the Mean Value of y |
|
|
691 | (1) |
|
Prediction Interval for an Individual Value of y |
|
|
692 | (5) |
|
|
697 | (3) |
|
14.8 Residual Analysis: Validating Model Assumptions |
|
|
700 | (9) |
|
|
701 | (2) |
|
|
703 | (1) |
|
|
704 | (1) |
|
|
705 | (4) |
|
14.9 Residual Analysis: Outliers and Influential Observations |
|
|
709 | (7) |
|
|
709 | (1) |
|
Detecting Influential Observations |
|
|
710 | (6) |
|
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression |
|
|
716 | (1) |
|
|
717 | (1) |
|
|
717 | (1) |
|
|
718 | (2) |
|
|
720 | (10) |
|
Case Problem 1 Measuring Stock Market Risk |
|
|
730 | (1) |
|
Case Problem 2 U.S. Department of Transportation |
|
|
731 | (1) |
|
Case Problem 3 Selecting a Point-and-Shoot Digital Camera |
|
|
731 | (1) |
|
Case Problem 4 Finding the Best Car Value |
|
|
732 | (1) |
|
Case Problem 5 Buckeye Creek Amusement Park |
|
|
733 | (2) |
|
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas |
|
|
735 | (1) |
|
Appendix 14.2 A Test for Significance Using Correlation |
|
|
736 | (1) |
|
Appendix 14.3 Simple Linear Regression with JMP |
|
|
736 | (2) |
|
Appendix 14.4 Regression Analysis with Excel |
|
|
738 | (3) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Simple Linear Regression with R |
|
|
|
Chapter 15 Multiple Regression |
|
|
741 | (71) |
|
Statistics in Practice: 84.51° |
|
|
742 | (51) |
|
15.1 Multiple Regression Model Regression Model and Regression Equation Estimated Multiple Regression Equation |
|
|
|
15.2 Least Squares Method |
|
|
|
An Example: Butler Trucking Company Note on Interpretation of Coefficients |
|
|
|
15.3 Multiple Coefficient of Determination |
|
|
|
|
|
15.5 Testing for Significance |
|
|
|
|
|
|
|
|
|
15.6 Using the Estimated Regression Equation for Estimation and Prediction |
|
|
|
15.7 Categorical Independent Variables |
|
|
|
An Example: Johnson Filtration, Inc. |
|
|
|
Interpreting the Parameters |
|
|
|
More Complex Categorical Variables |
|
|
|
|
|
|
|
Studentized Deleted Residuals and Outliers Influential Observations Using Cook's Distance Measure to Identify Influential Observations |
|
|
|
|
|
Logistic Regression Equation |
|
|
|
Estimating the Logistic Regression Equation |
|
|
|
|
|
|
|
Interpreting the Logistic Regression Equation Logit Transformation |
|
|
|
15.10 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression |
|
|
|
|
793 | (1) |
|
|
793 | (1) |
|
|
794 | (2) |
|
|
796 | (5) |
|
Case Problem 1 Consumer Research, Inc. |
|
|
801 | (1) |
|
Case Problem 2 Predicting Winnings for Nascar Drivers |
|
|
802 | (2) |
|
Case Problem 3 Finding the Best Car Value |
|
|
804 | (2) |
|
Appendix 15.1 Multiple Linear Regression with Jmp |
|
|
806 | (2) |
|
Appendix 15.2 Logistic Regression with Jmp |
|
|
808 | (1) |
|
Appendix 15.3 Multiple Regression with Excel |
|
|
809 | (3) |
|
Available in the Cengage eBook |
|
|
|
Appendix: Multiple Linear Regression with R |
|
|
|
Appendix: Logistic Regression with R |
|
|
Appendix A References and Bibliography |
|
812 | (2) |
Appendix B Tables |
|
814 | (27) |
Appendix C Summation Notation |
|
841 | (2) |
Appendix D Microsoft Excel and Tools for Statistical Analysis |
|
843 | (8) |
Appendix E Computing p-Values with JMP and Excel |
|
851 | (4) |
Appendix F Microsoft Excel Online and Tools for Statistical Analysis |
|
855 | (8) |
Appendix G Solutions to Even-Numbered Exercises (Cengage eBook) Index |
|
863 | |