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Statistics for Business and Economics and Student CD,9780132203845
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Statistics for Business and Economics and Student CD

by ; ;
Edition:
7th
ISBN13:

9780132203845

ISBN10:
0132203847
Format:
Hardcover
Pub. Date:
1/1/2010
Publisher(s):
Prentice Hall
List Price: $196.66
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Summary

This classic text is known for its accuracy and statistical precision. This text enables students to conduct serious analysis of applied problems in contrast to merely running simple"canned" applications to help students become stronger analysts and future managers. It is also at a mathematically higher level than most business statistics texts.

Table of Contents

Preface xix
Why Study Statistics?
1(8)
Decision Making in an Uncertain Environment
2(1)
Sampling
3(1)
Descriptive and Inferential Statistics
4(5)
Describing Data
5(1)
Making Inferences
5(4)
Describing Data: Graphical
9(37)
Classification of Variables
10(3)
Categorical or Numerical
10(1)
Measurement Levels
11(2)
Graphs to Describe Categorical Variables
13(7)
Tables
13(1)
Bar Charts and Pie Charts
14(2)
Pareto Diagrams
16(4)
Graphs to Describe Time-Series Data
20(3)
Graphs to Describe Numerical Variables
23(8)
Frequency Distributions
23(4)
Histograms and Ogives
27(2)
Stem-and-Leaf Displays
29(2)
Tables and Graphs to Describe Relationships Between Variables
31(7)
Scatter Plots
32(1)
Cross Tables
33(5)
Data Presentation Errors
38(8)
Misleading Histograms
38(2)
Misleading Time-Series Plots
40(6)
Describing Data: Numerical
46(32)
Measures of Central Tendency
46(5)
Mean, Median, Mode
46(3)
Shape of the Distribution
49(2)
Measures of Variability
51(9)
Range and Interquartile Range
52(1)
Variance and Standard Deviation
53(2)
Chebychev's Theorem and the Empirical Rule
55(2)
Coefficient of Variation
57(3)
Weighted Mean and Measures of Grouped Data
60(5)
Measures of Relationships Between Variables
65(5)
Obtaining Linear Relationships
70(8)
Probability
78(56)
Random Experiment, Outcomes, Events
79(8)
Probability and Its Postulates
87(9)
Classical Probability
88(2)
Relative Frequency
90(1)
Subjective Probability
91(5)
Probability Rules
96(13)
Conditional Probability
99(3)
Statistical Independence
102(7)
Bivariate Probabilities
109(11)
Odds
113(1)
Overinvolvement Ratios
114(6)
Bayes' Theorem
120(14)
Discrete Random Variables and Probability Distributions
134(53)
Random Variables
135(2)
Probability Distributions for Discrete Random Variables
137(3)
Properties of Discrete Random Variables
140(10)
Expected Value of a Discrete Random Variable
140(2)
Variance of a Discrete Random Variable
142(3)
Mean and Variance of Linear Functions of a Random Variable
145(5)
Binomial Distribution
150(8)
Hypergeometric Distribution
158(2)
The Poisson Probability Distribution
160(6)
Poisson Approximation to the Binomial Distribution
163(1)
Comparison of the Poisson and Binomial Probability Distributions
164(2)
Jointly Distributed Discrete Random Variables
166(21)
Computer Applications
170(1)
Covariance
170(1)
Correlation
171(3)
Linear Functions of Random Variables
174(2)
Portfolio Analysis
176(11)
Continuous Random Variables and Probability Distributions
187(45)
Continuous Random Variables
188(6)
The Uniform Distribution
191(3)
Expectations for Continuous Random Variables
194(3)
The Normal Distribution
197(13)
Normal Probability Plots
205(5)
Normal Distribution Approximation for Binomial Distribution
210(6)
Proportion Random Variable
214(2)
The Exponential Distribution
216(3)
Jointly Distributed Continuous Random Variables
219(13)
Linear Combinations of Random Variables
222(10)
Sampling and Sampling Distributions
232(43)
Sampling from a Population
233(5)
Sampling Distributions of Sample Means
238(16)
Central Limit Theorem
243(5)
Acceptance Intervals
248(6)
Sampling Distributions of Sample Proportions
254(6)
Sampling Distributions of Sample Variances
260(15)
Estimation: Single Population
275(28)
Properties of Point Estimators
276(6)
Unbiased Estimator
277(1)
Consistent Estimator
278(1)
Efficient Estimator
279(3)
Confidence Intervals for the Mean: Population Variance Known
282(107)
Intervals Based on the Normal Distribution
284(3)
Reducing Margin of Error
287(102)
Confidence Intervals for the Mean: Population Variance Unknown
389
Student's t Distribution
389
Intervals Based on the Student's t Distribution
291(4)
Confidence Intervals for Population Proportion (Large Samples)
295(8)
Estimation: Additional Topics
303(27)
Confidence Intervals for the Difference Between Two Normal Population Means
304(5)
Dependent Samples
304(2)
Independent Samples, Known Population Variances
306(3)
Confidence Intervals for the Difference Between Two Normal Population Means with Population Variances Unknown
309(6)
Independent Samples, Population Variances Assumed to be Equal
309(3)
Independent Samples, Population Variances Not Assumed to be Equal
312(3)
Confidence Intervals for the Difference Between Two Population Proportions (Large Samples)
315(2)
Confidence Intervals for the Variance of a Normal Distribution
317(4)
Sample Size Determination
321(9)
Mean of a Normally Distributed Population, Known Population Variance
321(2)
Population Proportion
323(7)
Hypothesis Testing
330(37)
Concepts of Hypothesis Testing
331(6)
Tests of the Mean of a Normal Distribution: Population Variance Known
337(11)
p-Value
339(6)
Two-Sided Alternative Hypothesis
345(3)
Tests of the Mean of a Normal Distribution: Population Variance Unknown
348(4)
Tests of the Population Proportion (Large Samples)
352(3)
Assessing the Power of a Test
355(12)
Tests of the Mean of a Normal Distribution: Population Variance Known
356(2)
Power of Population Proportion Tests (Large Samples)
358(9)
Hypothesis Testing II
367(35)
Tests of the Difference Between Two Population Means
369(12)
Two Means, Matched Pairs
369(3)
Two Means, Independent Samples, Known Population Variances
372(3)
Two Means, Independent Samples, Unknown Population Variances Assumed to Be Equal
375(3)
Two Means, Independent Samples, Unknown Population Variances Assumed to Be Not Equal
378(3)
Tests of the Difference Between Two Population Proportions (Large Samples)
381(4)
Tests of the Variance of a Normal Distribution
385(4)
Tests of the Equality of the Variances Between Two Normally Distributed Populations
389(4)
Some Comments on Hypothesis Testing
393(9)
Simple Regression
402(52)
Correlation Analysis
403(4)
Hypothesis Test for Correlation
404(3)
Linear Regression Model
407(6)
Least Squares Coefficient Estimators
413(5)
Computer Computation of Regression Coefficient
416(2)
The Explanatory Power of a Linear Regression Equation
418(8)
Coefficient of Determination R2
421(5)
Statistical Inference: Hypothesis Tests and Confidence Intervals
426(9)
Hypothesis Test for Population Slope Coefficient Using the F Distribution
433(2)
Prediction
435(6)
Graphical Analysis
441(13)
Multiple Regression
454(84)
The Multiple Regression Model
455(8)
Model Specification
456(2)
Model Development
458(3)
Three-Dimensional Graphing
461(2)
Estimation of Coefficients
463(7)
Least Squares Procedure
464(6)
Explanatory Power of a Multiple Regression Equation
470(7)
Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients
477(13)
Confidence Intervals
479(2)
Tests of Hypotheses
481(9)
Tests on Regression Coefficients
490(8)
Test on All Coefficients
491(2)
Test on a Subset of Regression Coefficients
493(1)
Comparison of F and t Tests
494(4)
Prediction
498(2)
Transformations for Nonlinear Regression Models
500(9)
Quadratic Transformations
501(3)
Logarithmic Transformations
504(5)
Dummy Variables for Regression Models
509(8)
Differences in Slope
513(4)
Multiple Regression Analysis Application Procedure
517(21)
Model Specification
518(2)
Multiple Regression
520(2)
Effect of Dropping a Statistically Significant Variable
522(1)
Analysis of Residuals
523(15)
Additional Topics in Regression Analysis
538(48)
Model-Building Methodology
539(3)
Model Specification
539(1)
Coefficient Estimation
540(1)
Model Verification
541(1)
Model Interpretation and Inference
542(1)
Dummy Variables and Experimental Design
542(11)
Experimental Design Models
546(7)
Lagged Values of the Dependent Variables as Regressors
553(4)
Specification Bias
557(4)
Multicollinearity
561(3)
Heteroscedasticity
564(5)
Autocorrelated Errors
569(17)
Estimation of Regressions with Autocorrelated Errors
573(4)
Autocorrelated Errors in Models with Lagged Dependent Variables
577(9)
Nonparametric Statistics
586(26)
Sign Test and Confidence Interval
587(8)
Sign Test for Paired or Matched Samples
587(3)
Normal Approximation
590(2)
Sign Test for Single Population Median
592(1)
Confidence Interval for the Median
593(2)
Wilcoxon Signed Rank Test
595(5)
Minitab (Wilcoxon Signed Rank Test)
596(1)
Normal Approximation
597(3)
Mann-Whitney U Test
600(4)
Wilcoxon Rank Sum Test
604(3)
Spearman Rank Correlation
607(5)
Goodness-of-Fit Tests and Contingency Tables
612(22)
Goodness-of-Fit Tests: Specified Probabilities
613(4)
Goodness-of-Fit Tests: Population Parameters Unknown
617(5)
A Test of Normality
619(3)
Contingency Tables
622(12)
Computer Applications
626(8)
Analysis of Variance
634(43)
Comparison of Several Population Means
635(2)
One-Way Analysis of Variance
637(10)
Population Model for One-Way Analysis of Variance
644(3)
The Kruskal-Wallis Test
647(3)
Two-Way Analysis of Variance: One Observation per Cell, Randomized Blocks
650(11)
Two-Way Analysis of Variance: More Than One Observation per Cell
661(16)
Introduction to Quality
677(33)
The Importance of Quality
678(4)
Quality Leaders
678(2)
Variation
680(2)
Control Charts for Means and Standard Deviations
682(11)
An Estimate of the Process Standard Deviation
683(3)
Control Charts for Means
686(2)
Control Charts for Standard Deviations
688(1)
Interpretation of Control Charts
689(4)
Process Capability
693(4)
Control Chart for Proportions
697(4)
Control Charts for Number of Occurrences
701(9)
Time-Series Analysis and Forecasting
710(46)
Index Numbers
712(9)
Price Index for a Single Item
713(1)
Unweighted Aggregate Price Index
714(1)
Weighted Aggregate Price Index
715(2)
Weighted Aggregate Quantity Index
717(1)
Change in Base Period
718(3)
A Nonparametric Test for Randomness
721(3)
Components of a Time Series
724(3)
Moving Averages
727(9)
Extraction of the Seasonal Component through Moving Averages
729(7)
Exponential Smoothing
736(11)
The Holt-Winters Exponential Smoothing Forecasting Model
738(5)
Forecasting Seasonal Time Series
743(4)
Autoregressive Models
747(5)
Autoregressive Integrated Moving Average Models
752(4)
Additional Topics in Sampling
756(42)
Basic Steps of a Sampling Study
758(4)
What Information Is Required?
758(1)
What Is the Relevant Population, and Is a Listing of It Available?
758(1)
How Should the Sample Members Be Selected?
759(1)
How Should Information Be Obtained from the Sample Members?
759(2)
How Should Sample Information Be Used to Make Inferences About the Population?
761(1)
What Conclusions Can Be Drawn About the Population?
761(1)
Sampling and Nonsampling Errors
762(1)
Simple Random Sampling
763(6)
Analysis of Results from Simple Random Sampling
764(5)
Stratified Sampling
769(12)
Analysis of Results from Stratified Random Sampling
771(6)
Allocation of Sample Effort Among Strata
777(4)
Determining Sample Size
781(6)
Sample Sizes for Simple Random Sampling: Estimation of Population Mean or Total
782(1)
Sample Sizes for Simple Random Sampling: Estimation of Population Proportion
783(1)
Sample Sizes for Stratified Random Sampling with Specified Degree of Precision
784(3)
Other Sampling Methods
787(11)
Cluster Sampling
787(4)
Two-Phase Sampling
791(2)
Nonprobabilistic Sampling Methods
793(5)
Statistical Decision Theory
798(83)
Decision Making Under Uncertainty
799(3)
Solutions Not Involving Specification of Probabilities Maximin Criterion
802(5)
Minimax Regret Criterion
802(1)
Maximin Criterion
803(2)
Minimax Regret Criterion
805(2)
Expected Monetary Value; TreePlan
807(11)
Decision Trees
809(2)
Using TreePlan to Solve a Decision Tree
811(3)
Sensitivity Analysis
814(4)
Sample Information: Bayesian Analysis and Value
818(13)
Use of Bayes' Theorem
818(5)
The Value of Sample Information
823(3)
The Value of Sample Information Viewed by Means of Decision Trees
826(5)
Allowing for Risk: Utility Analysis
831(10)
The Concept of Utility
832(4)
Expected Utility Criterion for Decision Making
836(5)
APPENDIX TABLES
Cumulative Distribution Function of the Standard Normal Distribution
841(2)
Probability Function of the Binomial Distribution
843(5)
Cumulative Binomial Probabilities
848(4)
Values of e-λ
852(1)
Individual Poisson Probabilities
853(8)
Cumulative Poisson Probabilities
861(8)
Cutoff Points of the Chi-Square Distribution Function
869(1)
Cutoff Points for the Student's t Distribution
870(1)
Cutoff Points for the F Distribution
871(3)
Cutoff Points for the Distribution of the Wilcoxon Test Statistic
874(1)
Cutoff Points for the Distribution of Spearman Rank Correlation Coefficient
875(1)
Cutoff Points for the Distribution of the Durbin-Watson Test Statistic
876(2)
Factors for Control Charts
878(1)
Cumulative Distribution Function of the Runs Test Statistic
879(2)
Answers to Selected Even-Numbered Exercises 881
Index 1


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