Statistics for business and economics / Paul Newbold, William L. Carlson, Betty M. Thorne.

Format:

• Book

Author/Creator:

• Newbold, Paul.

Contributor:

• Carlson, William L. (William Lee), 1938-
• Thorne, Betty.

Language:

English

Subjects (All):

• Commercial statistics.
• Economics--Statistical methods.
• Statistics.

Physical Description:

1 volume (various pagings) : illustrations (some color) ; 26 cm + 1 CD-ROM (4 3/4 in.)

Edition:

Fifth edition.

Place of Publication:

Upper Saddle River, N.J. : Prentice Hall, [2003]

System Details:

System requirements for accompanying CD-ROM: Windows (Adobe Acrobat included).

Summary:

Accompanying CD-ROM contains ... "PHStat2 [software]; visual explorations; data files; TreePlan Decision Tree add-in." -- CD-ROM label.

Contents:

• Chapter 1 Why Study Statistics? 1
• 1.1 Decision Making in an Uncertain Environment 2
• 1.2 Statistical Thinking 5
• 1.3 Journey to Making Decisions 7
• Chapter 2 Describing Data 11
• 2.1 Classification of Variables 12
• 2.2 Tables and Graphs for Numerical Data 14
• 2.3 Tables and Graphs for Categorical Variables 23
• 2.4 Measures of Central Tendency 31
• 2.5 Measures of Variability 38
• 2.6 Numerical Summary of Grouped Data 47
• Chapter 3 Summarizing Descriptive Relationships 55
• 3.1 Scatter Plots 56
• 3.2 Covariance and Correlation Coefficient 60
• 3.3 Obtaining Linear Relationships 65
• 3.4 Cross Tables 70
• Chapter 4 Probability 79
• 4.1 Random Experiment, Outcomes, Events 80
• 4.2 Probability and Its Postulates 88
• 4.3 Probability Rules 96
• 4.4 Bivariate Probabilities 106
• 4.5 Bayes' Theorem 116
• Chapter 5 Discrete Random Variables and Probability Distributions 129
• 5.1 Random Variables 130
• 5.2 Probability Distributions for Discrete Random Variables 132
• 5.3 Descriptive Measures for Discrete Random Variables 134
• 5.4 Binomial Distribution 144
• 5.5 Hypergeometric Distribution 153
• 5.6 The Poisson Probability Distribution 156
• 5.7 Jointly Distributed Discrete Random Variables 160
• Chapter 6 Continuous Random Variables and Probability Distributions 179
• 6.1 Continuous Random Variables 180
• 6.2 Expectations for Continuous Random Variables 184
• 6.3 The Normal Distribution 187
• 6.4 Normal Distribution Approximation for Binomial Distribution 199
• 6.5 The Exponential Distribution 204
• 6.6 Jointly Distributed Continuous Random Variables 206
• Chapter 7 Sampling and Sampling Distributions 217
• 7.1 Sampling from a Population 218
• 7.2 Sampling Distribution of the Sample Mean 221
• 7.3 Sampling Distribution of a Sample Proportion 235
• 7.4 Sampling Distribution of the Sample Variance 240
• Chapter 8 Estimation 255
• 8.1 Point Estimators 256
• 8.2 Confidence Intervals for the Mean of a Normal Distribution: Population Variance Known 261
• 8.3 Confidence Intervals for the Mean of a Normal Distribution: Population Variance Unknown 269
• 8.4 Confidence Intervals for Population Proportion (Large Samples) 275
• 8.5 Confidence Intervals for Variance of a Normal Distribution 280
• 8.6 Confidence Intervals for the Difference Between Means of Two Normal Populations 283
• 8.7 Confidence Intervals for the Difference Between Two Population Proportions (Large Samples) 293
• 8.8 Sample Size Determination 296
• Chapter 9 Hypothesis Testing 305
• 9.1 Concepts of Hypothesis Testing 306
• 9.2 Tests of the Mean of a Normal Distribution: Population Variance Known 312
• 9.3 Tests of the Mean of a Normal Distribution: Population Variance Unknown 323
• 9.4 Tests for the Population Proportion (Large Samples) 327
• 9.5 Tests of the Variance of a Normal Distribution 330
• 9.6 Tests for the Difference Between Two Population Means 334
• 9.7 Tests for the Difference Between Two Population Proportions (Large Samples) 346
• 9.8 Testing of the Equality of the Variances Between Two Normally Distributed Populations 350
• 9.9 Assessing the Power of a Test 354
• 9.10 Some Comments on Hypothesis Testing 361
• Chapter 10 Simple Regression 369
• 10.1 Correlation Analysis 370
• 10.2 Linear Regression Model 374
• 10.3 Least Squares Coefficient Estimators 379
• 10.4 The Explanatory Power of a Linear Regression Equation 384
• 10.5 Statistical Inference: Hypothesis Tests and Confidence Intervals 390
• 10.6 Prediction 398
• 10.7 Graphical Analysis 404
• Chapter 11 Multiple Regression 413
• 11.1 The Multiple Regression Model 414
• 11.2 Estimation of Coefficients 421
• 11.3 Explanatory Power of a Multiple Regression Equation 426
• 11.4 Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients 432
• 11.5 Tests on Sets of Regression Parameters 443
• 11.6 Prediction 448
• 11.7 Transformations for Nonlinear Regression Models 450
• 11.8 Dummy Variables for Regression Models 459
• 11.9 Multiple Regression Analysis Application Procedure 466
• Chapter 12 Additional Topics in Regression Analysis 485
• 12.1 Model-Building Methodology 486
• 12.2 Dummy Variables and Experimental Design 489
• 12.3 Lagged Values of the Dependent Variables as Regressors 497
• 12.4 Specification Bias 502
• 12.5 Multicollinearity 505
• 12.6 Heteroscedasticity 508
• 12.7 Autocorrelated Errors 513
• Chapter 13 Nonparametric Statistics 531
• 13.1 Sign Test and Confidence Interval 532
• 13.2 Wilcoxon Signed Rank Test 539
• 13.3 Mann-Whitney U Test 543
• 13.4 Wilcoxon Rank Sum Test 547
• 13.5 Spearman Rank Correlation 551
• Chapter 14 Goodness-of-Fit Tests and Contingency Tables 557
• 14.1 Goodness-of-Fit Tests: Specified Probabilities 558
• 14.2 Goodness-of-Fit Tests: Population Parameters Unknown 562
• 14.3 Contingency Tables 566
• Chapter 15 Analysis of Variance 579
• 15.1 Comparison of Several Population Means 580
• 15.2 One-Way Analysis of Variance 582
• 15.3 The Kruskal-Wallis Test 594
• 15.4 Two-Way Analysis of Variance: One Observation per Cell, Randomized Blocks 596
• 15.5 Two-Way Analysis of Variance: More Than One Observation per Cell 606
• Chapter 16 Introduction to Quality 621
• 16.1 The Importance of Quality 622
• 16.2 Control Charts for Means and Standard Deviations 626
• 16.3 Process Capability 636
• 16.4 Control Chart for Proportions 638
• 16.5 Control Charts for Number of Occurrences 642
• 16.6 Computer Applications 645
• Chapter 17 Time Series Analysis and Forecasting 655
• 17.1 Index Numbers 657
• 17.2 A Nonparametric Test for Randomness 665
• 17.3 Components of a Time Series 668
• 17.4 Moving Averages 671
• 17.5 Exponential Smoothing 679
• 17.6 Autoregressive Models 690
• 17.7 Autoregressive Integrated Moving Average Models 696
• Chapter 18 Additional Topics in Sampling 699
• 18.1 Basic Steps of a Sampling Study 700
• 18.2 Sampling and Nonsampling Errors 705
• 18.3 Simple Random Sampling 706
• 18.4 Stratified Sampling 712
• 18.5 Determining Sample Size 723
• 18.6 Other Sampling Methods 728
• Chapter 19 Statistical Decision Theory 739
• 19.1 Decision Making Under Uncertainty 740
• 19.2 Solutions Not Involving Specification of Probabilities: Maximin Criterion, Minimax Regret Criterion 743
• 19.3 Expected Monetary Value: TreePlan 748
• 19.4 Sample Information: Bayesian Analysis and Value 758
• 19.5 Allowing for Risk: Utility Analysis 771
• 1. Cumulative Distribution Function of the Standard Normal Distribution 780
• 2. Probability Function of the Binomial Distribution 782
• 3. Cumulative Binomial Probabilities 787
• 4. Values of e-[superscript lambda] 792
• 5. Individual Poisson Probabilities 793
• 6. Cumulative Poisson Probabilities 801
• 7. Cutoff Points of the Chi-Square Distribution Function 810
• 8. Cutoff Points for the Student's t Distribution 811
• 9. Cutoff Points for the F Distribution 812
• 10. Cutoff Points for the Distribution of the Wilcoxon Test Statistic 814
• 11. Cutoff Points for the Distribution of Spearman Rank Correlation Coefficient 815
• 12. Cutoff Points for the Distribution of the Durbin-Watson Test Statistic 816
• 13. Factors for Control Charts 818
• 14. Cumulative Distribution Function of the Runs Test Statistic 819.

Notes:

Includes bibliographical references and index.

ISBN:

0130293202

OCLC:

48661192