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Essentials of Modern Business Statistics : With Microsoft Excel

by ; ;
Edition:
3rd
ISBN13:

9780324312843

ISBN10:
0324312849
Format:
Hardcover
Pub. Date:
4/7/2006
Publisher(s):
South-Western College Pub

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Summary

From the renowned author team that has been writing market-leading business statistics textbooks for more than 20 years, ESSENTIALS OF MODERN BUSINESS STATISTICS, 3e provides a brief introduction to business statistics that balances a conceptual understanding of statistics with the real-world application of statistical methodology. The latest version of Microsoft Excel is integrated throughout the text, showing step-by-step instructions and screen captures to enhance student learning. The Third Edition contains the same student learning features that have made ASW products best-sellers for years; features include the problem-scenario approach and real-world examples that introduce statistical techniques.

Table of Contents

Preface xvii
About the Authors xxiii
Data and Statistics
1(29)
Statistics in Practice: Business Week
2(1)
Applications in Business and Economics
3(2)
Accounting
3(1)
Finance
4(1)
Marketing
4(1)
Production
4(1)
Economics
4(1)
Data
5(3)
Elements, Variables, and Observations
6(1)
Scales of Measurement
6(1)
Qualitative and Quantitative Data
7(1)
Cross-Sectional and Time Series Data
7(1)
Data Sources
8(4)
Existing Sources
8(1)
Statistical Studies
9(3)
Data Acquisition Errors
12(1)
Descriptive Statistics
12(2)
Statistical Inference
14(1)
Statistical Analysis Using Microsoft Excel
15(15)
Data Sets and Excel Worksheets
16(2)
Using Excel for Statistical Analysis
18(1)
Summary
19(1)
Glossary
20(1)
Supplementary Exercises
21(6)
Appendix 1.1 An Introduction to SWStat+
27(3)
Descriptive Statistics: Tabular and Graphical Presentations
30(59)
Statistics in Practice: Colgate-Palmolive Company
31(1)
Summarizing Qualitative Data
32(9)
Frequency Distribution
32(1)
Using Excel's Countif Function to Construct a Frequency Distribution
33(1)
Relative Frequency and Percent Frequency Distributions
34(1)
Using Excel to Construct Relative Frequency and Percent Frequency Distributions
35(1)
Bar Graphs and Pie Charts
36(1)
Using Excel's Chart Wizard to Construct Bar Graphs and Pie Charts
36(5)
Summarizing Quantitative Data
41(17)
Frequency Distribution
41(2)
Using Excel's Frequency Function to Construct a Frequency Distribution
43(2)
Relative Frequency and Percent Frequency Distributions
45(1)
Histogram
45(1)
Using Excel's Chart Wizard to Construct a Histogram
46(2)
Cumulative Distributions
48(3)
Using Excel's Histogram Tool to Construct a Frequency Distribution and Histogram
51(7)
Exploratory Data Analysis: The Stem-and-Leaf Display
58(5)
Crosstabulations and Scatter Diagrams
63(26)
Crosstabulation
63(3)
Using Excel's PivotTable Report to Construct a Crosstabulation
66(3)
Simpson's Paradox
69(1)
Scatter Diagram and Trendline
70(2)
Using Excel's Chart Wizard to Construct a Scatter Diagram and a Trendline
72(6)
Summary
78(2)
Glossary
80(1)
Key Formulas
80(1)
Supplementary Exercises
81(6)
Case Problem Pelican Stores
87(2)
Descriptive Statistics: Numerical Measures
89(66)
Statistics in Practice: Small Fry Design
90(1)
Measures of Location
91(12)
Mean
91(1)
Median
92(1)
Mode
93(1)
Using Excel to Compute the Mean, Median, and Mode
94(1)
Percentiles
95(1)
Quartiles
96(1)
Using Excel's Rank and Percentile Tool to Compute Percentiles and Quartiles
97(6)
Measures of Variability
103(10)
Range
104(1)
Interquartile Range
104(1)
Variance
105(1)
Standard Deviation
106(2)
Using Excel to Compute the Sample Variance and Sample Standard Deviation
108(1)
Coefficient of Variation
108(1)
Using Excel's Descriptive Statistics Tool
108(5)
Measures of Distribution Shape, Relative Location, and Detecting Outliers
113(7)
Distribution Shape
113(2)
z-Scores
115(1)
Chebyshev's Theorem
116(1)
Empirical Rule
116(1)
Detecting Outliers
117(3)
Exploratory Data Analysis
120(5)
Five-Number Summary
120(1)
Box Plot
121(4)
Measures of Association Between Two Variables
125(10)
Covarlance
125(2)
Interpretation of the Covariance
127(2)
Correlation Coefficient
129(1)
Interpretation of the Correlation Coefficient
130(2)
Using Excel to Compute the Covariance and Correlation Coefficient
132(3)
The Weighted Mean and Working with Grouped Data
135(20)
Weighted Mean
135(1)
Grouped Data
136(5)
Summary
141(1)
Glossary
141(1)
Key Formulas
142(2)
Supplementary Exercises
144(5)
Case Problem 1 Pelican Stores
149(1)
Case Problem 2 National Health Care Association
150(1)
Case Problem 3 Business Schools of Asia-Pacific
151(1)
Appendix 3.1 Constructing a Box Plot Using SWStat+
151(4)
Introduction to Probability
155(45)
Statistics in Practice: Morton International
156(1)
Experiments, Counting Rules, and Assigning Probabilities
157(10)
Counting Rules, Combinations, and Permutations
157(5)
Assigning Probabilities
162(2)
Probabilities for the KP&L Project
164(3)
Events and Their Probabilities
167(4)
Some Basic Relationships of Probability
171(6)
Complement of an Event
171(1)
Addition Law
172(5)
Conditional Probability
177(8)
Independent Events
181(1)
Multiplication Law
181(4)
Bayes' Theorem
185(15)
Tabular Approach
188(1)
Using Excel to Compute Posterior Probabilities
189(3)
Summary
192(1)
Glossary
192(1)
Key Formulas
193(1)
Supplementary Exercises
194(4)
Case Problem Hamilton County Judges
198(2)
Discrete Probability Distributions
200(40)
Statistics in Practice: Citibank
201(1)
Random Variables
201(3)
Discrete Random Variables
202(1)
Continuous Random Variables
203(1)
Discrete Probability Distributions
204(6)
Expected Value and Variance
210(5)
Expected Value
210(1)
Variance
210(1)
Using Excel to Compute the Expected Value, Variance, and Standard Deviation
211(4)
Binomial Probability Distribution
215(11)
A Binomial Experiment
216(1)
Martin Clothing Store Problem
216(5)
Using Excel to Compute Binomial Probabilities
221(2)
Expected Value and Variance for the Binomial Probability Distribution
223(3)
Poisson Probability Distribution
226(5)
An Example Involving Time Intervals
226(1)
An Example Involving Length or Distance Intervals
227(1)
Using Excel to Compute Poisson Probabilities
228(3)
Hypergeometric Probability Distribution
231(9)
Using Excel to Compute Hypergeometric Probabilities
233(2)
Summary
235(1)
Glossary
235(1)
Key Formulas
236(1)
Supplementary Exercises
237(3)
Continuous Probability Distributions
240(31)
Statistics in Practice: Procter & Gamble
241(1)
Uniform Probability Distribution
242(4)
Area as a Measure of Probability
243(3)
Normal Probability Distribution
246(15)
Normal Curve
246(2)
Standard Normal Probability Distribution
248(5)
Computing Probabilities for Any Normal Probability Distribution
253(1)
Grear Tire Company Problem
254(2)
Using Excel to Compute Normal Probabilities
256(5)
Exponential Probability Distribution
261(10)
Computing Probabilities for the Exponential Distribution
262(1)
Relationship Between the Poisson and Exponential Distributions
263(1)
Using Excel to Compute Exponential Probabilities
263(3)
Summary
266(1)
Glossary
266(1)
Key Formulas
266(1)
Supplementary Exercises
267(2)
Case Problem Specialty Toys
269(2)
Sampling and Sampling Distribution
271(37)
Statistics in Practice: MeadWestvaco Corporation
272(1)
The Electronics Associates Sampling Problem
273(1)
Simple Random Sampling
274(6)
Sampling from a Finite Population
274(4)
Sampling from an Infinite Population
278(2)
Point Estimation
280(3)
Introduction to Sampling Distributions
283(3)
Sampling Distribution of x
286(10)
Expected Value of x
286(1)
Standard Deviation of x
287(1)
Form of the Sampling Distribution of x
288(2)
Sampling Distribution of x for the EAI Problem
290(1)
Practical Value of the Sampling Distribution of x
290(2)
Relationship Between Sample Size and the Sampling Distribution of x
292(4)
Sampling Distribution of p
296(5)
Expected Value of p
296(1)
Standard Deviation of p
297(1)
Form of the Sampling Distribution of p
297(1)
Practical Value of the Sampling Distribution of p
298(3)
Sampling Methods
301(7)
Stratified Random Sampling
301(1)
Cluster Sampling
301(1)
Systematic Sampling
302(1)
Convenience Sampling
303(1)
Judgment Sampling
303(1)
Summary
304(1)
Glossary
304(1)
Key Formulas
305(1)
Supplementary Exercises
305(3)
Interval Estimation
308(39)
Statistics in Practice: Food Lion
309(1)
Population Mean: σ Known
310(8)
Margin of Error and the Interval Estimate
310(4)
Using Excel
314(2)
Practical Advice
316(2)
Population Mean: σ Unknown
318(10)
Margin of Error and the Interval Estimate
319(3)
Using Excel
322(1)
Practical Advice
323(1)
Using a Small Sample
323(2)
Summary of Interval Estimation Procedures
325(3)
Determining the Sample Size
328(3)
Population Proportion
331(16)
Using Excel
332(2)
Determining the Sample Size
334(4)
Summary
338(1)
Glossary
339(1)
Key Formulas
339(1)
Supplementary Exercises
340(3)
Case Problem 1 Bock Investment Services
343(1)
Case Problem 2 Gulf Real Estate Properties
343(3)
Case Problem 3 Metropolitan Research, Inc.
346(1)
Hypothesis Tests
347(46)
Statistics in Practice: John Morrell & Company
348(1)
Developing Null and Alternative Hypotheses
349(2)
Testing Research Hypotheses
349(1)
Testing the Validity of a Claim
349(1)
Testing in Decision-Making Situations
350(1)
Summary of Forms for Null and Alternative Hypotheses
350(1)
Type I and Type II Errors
351(3)
Population Mean: σ Known
354(16)
One-Tailed Test
354(6)
Two-Tailed Test
360(3)
Using Excel
363(1)
Summary and Practical Advice
364(2)
Relationship Between Interval Estimation and Hypothesis Testing
366(4)
Population Mean: σ Unknown
370(10)
One-Tailed Test
371(1)
Two-Tailed Test
372(2)
Using Excel
374(2)
Summary and Practical Advice
376(4)
Population Proportion
380(13)
Using Excel
382(2)
Summary
384(2)
Summary
386(1)
Glossary
387(1)
Key Formulas
387(1)
Supplementary Exercises
388(2)
Case Problem 1 Quality Associates, Inc.
390(2)
Case Problem 2 Unemployment Study
392(1)
Comparisons Involving Means
393(57)
Statistics in Practice: Fisons Corporation
394(1)
Inferences About the Difference Between Two Population Means: σ1 and σ2 Known
395(10)
Interval Estimation of μ1 - μ2
395(2)
Using Excel to Construct a Confidence Interval
397(2)
Hypothesis Tests About μ1 - μ2
399(2)
Using Excel to Conduct a Hypothesis Test
401(2)
Practical Advice
403(2)
Inferences About the Difference Between Two Population Means: σ1 and σ2 Unknown
405(12)
Interval Estimation of μ1 - μ2
406(1)
Using Excel to Construct a Confidence Interval
407(2)
Hypothesis Tests About μ1 - μ2
409(2)
Using Excel to Conduct a Hypothesis Test
411(2)
Practical Advice
413(4)
Inferences About the Difference Between Two Population Means: Matched Samples
417(7)
Using Excel to Conduct a Hypothesis Test
419(5)
Introduction to Analysis of Variance
424(4)
Assumptions for Analysis of Variance
425(1)
Conceptual Overview
425(3)
Analysis of Variance: Testing for the Equality of k Population Means
428(22)
Between-Treatments Estimate of Population Variance
429(1)
Within-Treatments Estimate of Population Variance
430(1)
Comparing the Variance Estimates: The F Test
430(3)
ANOVA Table
433(1)
Using Excel
433(6)
Summary
439(1)
Glossary
439(1)
Key Formulas
440(2)
Supplementary Exercises
442(4)
Case Problem 1 Par, Inc.
446(1)
Case Problem 2 Wentworth Medical Center
447(1)
Case Problem 3 Compensation for ID Professionals
448(2)
Comparisons Involving Proportions and a Test of Independence
450(34)
Statistics in Practice: United Way
451(1)
Inferences About the Difference Between Two Population Proportions
452(9)
Interval Estimation of p1 - p2
452(2)
Using Excel to Construct a Confidence Interval
454(2)
Hypothesis Tests About p1 - p2
456(1)
Using Excel to Conduct a Hypothesis Test
457(4)
Hypothesis Test for Proportions of a Multinomial Population
461(8)
Using Excel to Conduct a Goodness of Fit Test
466(3)
Test of Independence
469(15)
Using Excel to Conduct a Test of Independence
473(4)
Summary
477(1)
Glossary
477(1)
Key Formulas
478(1)
Supplementary Exercises
478(5)
Case Problem A Bipartisan Agenda for Change
483(1)
Simple Linear Regression
484(75)
Statistics in Practice: Alliance Data Systems
485(1)
Simple Linear Regression Model
486(3)
Regression Model and Regression Equation
486(1)
Estimated Regression Equation
487(2)
Least Squares Method
489(12)
Using Excel to Develop a Scatter Diagram and Compute the Estimated Regression Equation
493(8)
Coefficient of Determination
501(9)
Using Excel to Compute the Coefficient of Determination
505(1)
Correlation Coefficient
505(5)
Model Assumptions
510(1)
Testing for Significance
511(10)
Estimate of σ2
512(1)
tTest
512(2)
Confidence Interval for β1
514(1)
F Test
515(2)
Some Cautions About the Interpretation of Significance Tests
517(4)
Excel's Regression Tool
521(6)
Using Excel's Regression Tool for the Armand's Pizza Parlors Problem
521(2)
Interpretation of Estimated Regression Equation Output
523(1)
Interpretation of ANOVA Output
523(1)
Interpretation of Regression Statistics Output
524(3)
Using the Estimated Regression Equation for Estimation and Prediction
527(8)
Point Estimation
527(1)
Interval Estimation
527(1)
Confidence Interval Estimate of the Mean Value of y
527(2)
Prediction Interval Estimate of an Individual Value of y
529(2)
Using Excel to Develop Confidence and Prediction Interval Estimates
531(4)
Residual Analysis: Validating Model Assumptions
535(24)
Residual Plot Against x
536(3)
Residual Plot Against y
539(1)
Using Excel's Regression Tool to Construct a Residual Plot
539(3)
Summary
542(1)
Glossary
543(1)
Key Formulas
543(2)
Supplementary Exercises
545(6)
Case Problem 1 Spending and Student Achievement
551(1)
Case Problem 2 U.S. Department of Transportation
552(1)
Case Problem 3 Alumni Giving
553(2)
Case Problem 4 Major League Baseball Teams Values
555(1)
Appendix 12.1 Regression Analysis with SWStat+
555(4)
Multiple Regression
559(50)
Statistics in Practice: International Paper
560(1)
Multiple Regression Model
561(1)
Regression Model and Regression Equation
561(1)
Estimated Multiple Regression Equation
561(1)
Least Squares Method
562(10)
An Example: Butler Trucking Company
563(3)
Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation
566(1)
Note on Interpretation of Coefficients
567(5)
Multiple Coefficient of Determination
572(3)
Model Assumptions
575(2)
Testing for Significance
577(7)
F Test
578(2)
t Test
580(1)
Multicollinearity
581(3)
Using the Estimated Regression Equation for Estimation and Prediction
584(2)
Qualitative Independent Variables
586(23)
An Example: Johnson Filtration. Inc.
587(2)
Interpreting the Parameters
589(2)
More Complex Qualitative Variables
591(4)
Summary
595(1)
Glossary
596(1)
Key Formulas
596(1)
Supplementary Exercises
597(6)
Case Problem 1 Consumer Research, Inc.
603(1)
Case Problem 2 Predicting Student Proficiency Test Scores
604(1)
Case Problem 3 Alumni Giving
605(2)
Appendix 13.1 Multiple Regression Analysis with SWStat+
607(2)
Statistical Methods for Quality Control
609(37)
Statistics in Practice: Dow Chemical
610(1)
Philosophies and Frameworks
611(3)
Malcolm Baldrige National Quality Award
611(1)
ISO 9000
612(1)
Six Sigma
612(2)
Statistical Process Control
614(17)
Control Charts
615(1)
x Chart: Process Mean and Standard Deviation Known
616(2)
x Chart: Process Mean and Standard Deviation Unknown
618(3)
R Chart
621(2)
Using Excel to Construct an R Chart and an x Chart
623(3)
p Chart
626(2)
np Chart
628(1)
Interpretation of Control Charts
629(2)
Acceptance Sampling
631(15)
KALI, Inc.: An Example of Acceptance Sampling
633(1)
Computing the Probability of Accepting a Lot
633(2)
Selecting an Acceptance Sampling Plan
635(2)
Multiple Sampling Plans
637(2)
Summary
639(1)
Glossary
640(1)
Key Formulas
641(1)
Supplementary Exercises
642(4)
Appendix A: References and Bibliography 646(2)
Appendix B: Tables 648(11)
Appendix C: Summation Notation 659(2)
Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises 661(30)
Appendix E: Using Excel Functions 691(6)
Index 697


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