Rent 9780072984019 | Basic Statistics For Business & Economics: Basic Statistics For Business And Economics | Lind, Douglas A.; Marchal, William G.; Wathen, Samuel Adam @knetbooks.com

Summary Table of Contents

The Fifth Edition of Basic Statistics for Business and Economics is a shorter version of Lind/Marchal/Wathen's Statistical Techniques in Business and Economics, 12e. The authors of this text continue to provide a student-oriented approach to business statistics. In this book you will find step-by-step solved examples, realistic exercises, and up-to-date technology and illustrations. Book jacket.

What Is Statistics?	p. 1
Introduction	p. 2
Why Study Statistics?	p. 2
What Is Meant by Statistics?	p. 4
Types of Statistics	p. 6
Descriptive Statistics	p. 6
Inferential Statistics	p. 7
Types of Variables	p. 9
Levels of Measurement	p. 9
Nominal-Level Data	p. 10
Ordinal-Level Data	... MOREp. 11
Interval-Level Data	p. 12
Ratio-Level Data	p. 12
Exercises	p. 14
Statistics, Graphics, and Ethics	p. 15
Misleading Statistics	p. 15
Association Does Not Necessarily Imply Causation	p. 15
Graphs Can Be Misleading	p. 16
Become a Better Consumer and a Better Producer of Information	p. 17
Ethics	p. 17
Software Applications	p. 18
Chapter Outline	p. 19
Chapter Exercises	p. 19
exercises.com	p. 20
Dataset Exercises	p. 21
Answers to Self-Review	p. 22
Describing Data: Frequency Distributions and Graphic Presentation	p. 23
Introduction	p. 24
Constructing a Frequency Distribution	p. 25
Class Intervals and Class Midpoints	p. 29
A Software Example	p. 29
Relative Frequency Distribution	p. 30
Exercises	p. 31
Graphic Presentation of a Frequency Distribution	p. 32
Histogram	p. 32
Frequency Polygon	p. 34
Exercises	p. 37
Cumulative Frequency Distributions	p. 38
Exercises	p. 41
Other Graphic Presentations of Data	p. 42
Line Graphs	p. 42
Bar Charts	p. 43
Pie Charts	p. 44
Exercises	p. 46
Chapter Outline	p. 47
Chapter Exercises	p. 48
exercises.com	p. 53
Dataset Exercises	p. 53
Software Commands	p. 54
Answers to Self-Review	p. 56
Describing Data: Numerical Measures	p. 57
Introduction	p. 58
The Population Mean	p. 59
The Sample Mean	p. 60
Properties of the Arithmetic Mean	p. 61
Exercises	p. 62
The Weighted Mean	p. 63
Exercises	p. 64
The Median	p. 64
The Mode	p. 65
Exercises	p. 67
Software Solution	p. 68
The Relative Positions of the Mean, Median, and Mode	p. 68
Exercises	p. 70
The Geometric Mean	p. 71
Exercises	p. 72
Why Study Dispersion?	p. 73
Measures of Dispersion	p. 74
Range	p. 74
Mean Deviation	p. 75
Exercises	p. 76
Variance and Standard Deviation	p. 77
Exercises	p. 79
Software Solution	p. 80
Exercises	p. 81
Interpretation and Uses of the Standard Deviation	p. 82
Chebyshev's Theorem	p. 82
The Empirical Rule	p. 83
Exercises	p. 84
Chapter Outline	p. 84
Pronunciation Key	p. 86
Chapter Exercises	p. 86
exercises.com	p. 89
Dataset Exercises	p. 90
Software Commands	p. 90
Answers to Self-Review	p. 92
Describing Data: Displaying and Exploring Data	p. 93
Introduction	p. 94
Dot Plots	p. 94
Exercises	p. 96
Quartiles, Deciles, and Percentiles	p. 97
Exercises	p. 100
Box Plots	p. 100
Exercises	p. 102
Skewness	p. 103
Exercises	p. 107
Describing the Relationship between Two Variables	p. 107
Exercises	p. 110
Chapter Outline	p. 112
Pronunciation Key	p. 112
Chapter Exercises	p. 112
exercises.com	p. 116
Dataset Exercises	p. 116
Software Commands	p. 117
Answers to Self-Review	p. 119
A Survey of Probability Concepts	p. 120
Introduction	p. 121
What Is a Probability?	p. 122
Approaches to Assigning Probabilities	p. 124
Classical Probability	p. 124
Empirical Probability	p. 125
Subjective Probability	p. 126
Exercises	p. 127
Some Rules for Computing Probabilities	p. 128
Rules of Addition	p. 128
Exercises	p. 133
Rules of Multiplication	p. 134
Contingency Tables	p. 137
Tree Diagrams	p. 139
Exercises	p. 141
Principles of Counting	p. 142
The Multiplication Formula	p. 142
The Permutation Formula	p. 143
The Combination Formula	p. 145
Exercises	p. 146
Chapter Outline	p. 147
Pronunciation Key	p. 148
Chapter Exercises	p. 148
exercises.com	p. 152
Dataset Exercises	p. 152
Software Commands	p. 153
Answers to Self-Review	p. 154
Discrete Probability Distributions	p. 156
Introduction	p. 157
What Is a Probability Distribution?	p. 157
Random Variables	p. 159
Discrete Random Variable	p. 159
Continuous Random Variable	p. 160
The Mean, Variance, and Standard Deviation of a Probability Distribution	p. 160
Mean	p. 160
Variance and Standard Distribution	p. 161
Exercises	p. 163
Binomial Probability Distribution	p. 164
How Is a Binomial Probability Distribution Computed	p. 165
Binomial Probability Tables	p. 167
Exercises	p. 170
Cumulative Binomial Probability Distributions	p. 172
Exercises	p. 173
Poisson Probability Distribution	p. 174
Exercises	p. 177
Chapter Outline	p. 177
Chapter Exercises	p. 178
Dataset Exercises	p. 182
Software Commands	p. 182
Answers to Self-Review	p. 184
Continuous Probability Distributions	p. 185
Introduction	p. 186
The Family of Uniform Distributions	p. 186
Exercises	p. 189
The Family of Normal Probability Distributions	p. 190
The Standard Normal Distribution	p. 193
The Empirical Rule	p. 195
Exercises	p. 196
Finding Areas under the Normal Curve	p. 197
Exercises	p. 199
Exercises	p. 202
Exercises	p. 204
Chapter Outline	p. 204
Chapter Exercises	p. 205
Dataset Exercises	p. 208
Software Commands	p. 209
Answers to Self-Review	p. 210
Sampling Methods and the Central Limit Theorem	p. 211
Introduction	p. 212
Sampling Methods	p. 212
Reasons to Sample	p. 212
Simple Random Sampling	p. 213
Systematic Random Sampling	p. 216
Stratified Random Sampling	p. 216
Cluster Sampling	p. 217
Exercises	p. 218
Sampling "Error"	p. 220
Sampling Distribution of the Sample Mean	p. 222
Exercises	p. 225
The Central Limit Theorem	p. 226
Exercises	p. 232
Using the Sampling Distribution of the Sample Mean	p. 233
Exercises	p. 237
Chapter Outline	p. 237
Pronunciation Key	p. 238
Chapter Exercises	p. 238
exercises.com	p. 242
Dataset Exercises	p. 243
Software Commands	p. 243
Answers to Self-Review	p. 244
Estimation and Confidence Intervals	p. 245
Introduction	p. 246
Point Estimates and Confidence Intervals	p. 246
Known [sigma] or a Large Sample	p. 246
A Computer Simulation	p. 251
Exercises	p. 253
Unknown Population Standard Deviation and a Small Sample	p. 254
Exercises	p. 260
A Confidence Interval for a Proportion	p. 260
Exercises	p. 263
Finite-Population Correction Factor	p. 263
Exercises	p. 264
Choosing an Appropriate Sample Size	p. 265
Exercises	p. 267
Chapter Outline	p. 268
Pronunciation Key	p. 269
Chapter Exercises	p. 269
exercises.com	p. 272
Dataset Exercises	p. 273
Software Commands	p. 273
Answers to Self-Review	p. 275
One-Sample Tests of Hypothesis	p. 276
Introduction	p. 277
What Is a Hypothesis?	p. 277
What Is Hypothesis Testing?	p. 278
Five-Step Procedure for Testing a Hypothesis	p. 278
State the Null Hypothesis (H[subscript 0]) and the Alternate Hypothesis (H[subscript 1])	p. 278
Select a Level of Significance	p. 279
Select the Test Statistic	p. 279
Formulate the Decision Rule	p. 281
Make a Decision	p. 282
One-Tailed and Two-Tailed Tests of Significance	p. 283
Testing for a Population Mean with a Known Population Standard Deviation	p. 284
A Two-Tailed Test	p. 284
A One-Tailed Test	p. 288
p-Value in Hypothesis Testing	p. 288
Testing for a Population Mean: Large Sample, Population Standard Deviation Unknown	p. 290
Exercises	p. 291
Tests Concerning Proportions	p. 292
Exercises	p. 295
Testing for a Population Mean: Small Sample, Population Standard Deviation Unknown	p. 295
Exercises	p. 300
A Software Solution	p. 301
Exercises	p. 303
Chapter Outline	p. 304
Pronunciation Key	p. 305
Chapter Exercises	p. 305
exercises.com	p. 309
Dataset Exercises	p. 309
Software Commands	p. 310
Answers to Self-Review	p. 311
Two-Sample Tests of Hypothesis	p. 312
Introduction	p. 313
Two-Sample Tests of Hypothesis: Independent Samples	p. 313
Exercises	p. 318
Two-Sample Tests about Proportions	p. 319
Exercises	p. 321
Comparing Population Means with Small Samples	p. 323
Exercises	p. 326
Two-Sample Tests of Hypothesis: Dependent Samples	p. 327
Comparing Dependent and Independent Samples	p. 331
Exercises	p. 333
Chapter Outline	p. 334
Pronunciation Key	p. 335
Chapter Exercises	p. 335
exercises.com	p. 340
Dataset Exercises	p. 341
Software Commands	p. 341
Answers to Self-Review	p. 342
Analysis of Variance	p. 344
Introduction	p. 345
The F Distribution	p. 345
Comparing Two Population Variances	p. 346
Exercises	p. 349
ANOVA Assumptions	p. 350
The ANOVA Test	p. 352
Exercises	p. 359
Inferences about Pairs of Treatment Means	p. 360
Exercises	p. 362
Chapter Outline	p. 364
Pronunciation Key	p. 365
Chapter Exercises	p. 365
exercises.com	p. 370
Dataset Exercises	p. 370
Software Commands	p. 371
Answers to Self-Review	p. 373
Linear Regression and Correlation	p. 374
Introduction	p. 375
What Is Correlation Analysis?	p. 375
The Coefficient of Correlation	p. 377
The Coefficient of Determination	p. 381
Correlation and Cause	p. 382
Exercises	p. 382
Testing the Significance of the Correlation Coefficient	p. 384
Exercises	p. 386
Regression Analysis	p. 386
Least Squares Principle	p. 386
Drawing the Line of Regression	p. 389
Exercises	p. 390
The Standard Error of Estimate	p. 392
Assumptions Underlying Linear Regression	p. 395
Exercises	p. 396
Confidence and Prediction Intervals	p. 396
Exercises	p. 400
More on the Coefficient of Determination	p. 400
Exercises	p. 403
The Relationships among the Coefficient of Correlation, the Coefficient of Determination, and the Standard Error of Estimate	p. 403
Transforming Data	p. 405
Exercises	p. 407
Chapter Outline	p. 408
Pronunciation Key	p. 410
Chapter Exercises	p. 410
exercises.com	p. 417
Dataset Exercises	p. 417
Software Commands	p. 418
Answers to Self-Review	p. 420
Multiple Regression and Correlation Analysis	p. 421
Introduction	p. 422
Multiple Regression Analysis	p. 422
Inferences in Multiple Linear Regression	p. 423
Exercises	p. 426
Multiple Standard Error of Estimate	p. 428
Assumptions about Multiple Regression and Correlation	p. 429
The ANOVA Table	p. 430
Exercises	p. 432
Evaluating the Regression Equation	p. 432
Using a Scatter Diagram	p. 432
Correlation Matrix	p. 433
Global Test: Testing the Multiple Regression Model	p. 434
Evaluating Individual Regression Coefficients	p. 436
Qualitative Independent Variables	p. 439
Exercises	p. 441
Analysis of Residuals	p. 442
Chapter Outline	p. 447
Pronunciation Key	p. 448
Chapter Exercises	p. 448
exercises.com	p. 459
Dataset Exercises	p. 460
Software Commands	p. 461
Answers to Self-Review	p. 463
Chi-Square Applications	p. 464
Introduction	p. 464
Goodness-of-Fit Test: Equal Expected Frequencies	p. 465
Exercises	p. 470
Goodness-of-Fit Test: Unequal Expected Frequencies	p. 471
Limitations of Chi-Square	p. 473
Exercises	p. 475
Contingency Table Analysis	p. 746
Exercises	p. 450
Chapter Outline	p. 481
Pronunciation Key	p. 481
Chapter Exercises	p. 482
exercises.com	p. 484
Dataset Exercises	p. 485
Software Commands	p. 486
Answers to Self-Review	p. 487
CD Chapters
Statistical Quality Control
Time Series and Forecasting
Appendixes
Tables
Binomial Probability Distribution	p. 489
Critical Values of Chi-Square	p. 494
Poisson Distribution	p. 495
Areas under the Normal Curve	p. 496
Table of Random Numbers	p. 497
Student's t Distribution	p. 498
Critical Values of the F Distribution	p. 499
Wilcoxon T Values	p. 501
Factors for Control Charts	p. 502
Datasets
Real Estate	p. 503
Major League Baseball	p. 506
Wages and Wage Earners	p. 508
CIA International Economic and Demographic Data	p. 512
Whitner Autoplex	p. 515
Getting Started with Megastat	p. 516
Visual Statistics	p. 520
Answers to Odd-Numbered Exercises	p. 525
Photo Credits	p. 552
Index	p. 553
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