Introduction to Business Statistics A Microsoft Excel Integrated Approach
by Kvanli, Alan H.; Pavur, Robert J.; Keeling, Kellie B.Edition: 6th
Format: Hardcover
Pub. Date: 2002-08-05
Publisher(s): South-Western College Pub
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Summary
As with earlier editions, this text fully integrates the use of computers with statistics. This edition has retained the non-intimidating approach to describing the concepts and applications of statistics while giving students the opportunity to observe and actually carry out computer-generated solutions using statistics packages like SPSS and MINITAB, or a spreadsheet package like Excel. The text has also been designed so that those requiring a more traditional calculator-based approach will find an abundance of exercises and examples that can be solved in that manner. A CD-ROM presenting data sets and special Excel macros that Robert Pavur (Professor, U of North Texas) and Kellie Keeling (Assistant Professor, Virginia Tech) have created will be bound into the back of the book.
Table of Contents
| A First Look at Statistics and Data Collection | |
| Statistics in Action: Online Surveys: E-xpress Yourself | p. 1 |
| Using the Computer | p. 3 |
| Uses of Statistics in Business | p. 4 |
| Some Basic Definitions | p. 4 |
| Discrete and Continuous Numerical Data | p. 6 |
| Level of Measurement for Numerical Data | p. 7 |
| Sources of Data | p. 9 |
| Business Research Questions in Practice | |
| Designing and Coding a Questionnaire | |
| Data Presentation Using Descriptive Graphs | |
| Statistics in Action: Lean Philosophy Drives Productivity Growth | p. 25 |
| Frequency Distributions | p. 26 |
| Histograms and Stem-and-Leaf Diagrams | p. 32 |
| Frequency Polygons | p. 40 |
| Cumulative Frequencies (Ogives) | p. 41 |
| Bar Charts | p. 46 |
| Pie Charts | p. 47 |
| Deceptive Graphs | p. 54 |
| Using Computer Graphics | p. 56 |
| Data Summary Using Descriptive Measures | |
| Statistics in Action: Semiconductor Business: When the Chips Are Down | p. 69 |
| Various Types of Descriptive Measures | p. 70 |
| Measures of Central Tendency | p. 71 |
| Measures of Variation | p. 78 |
| Measures of Position | p. 86 |
| Measures of Shape | p. 89 |
| Interpreting X and s | p. 93 |
| Grouped Data | p. 97 |
| Box Plots | p. 101 |
| Probability Concepts | |
| Statistics in Action: Cable and DSL Connections: Wired for Speed | p. 119 |
| Events and Probability | p. 120 |
| Basic Concepts | p. 122 |
| Going Beyond the Contingency Table | p. 129 |
| Applying the Concepts | p. 136 |
| Tree Diagrams | p. 144 |
| Probabilities for More than Two Events | p. 148 |
| Counting Rules | p. 150 |
| Simple Random Samples | p. 155 |
| Discrete Probability Distributions | |
| Statistics in Action: AACSB Accreditation: A Commitment to Excellence in Business Education | p. 167 |
| Random Variables | p. 169 |
| Representing Probability Distributions for Discrete Random Variables | p. 174 |
| Mean and Variance of Discrete Random Variables | p. 177 |
| Binomial Random Variables | p. 183 |
| The Hypergeometric Distribution | p. 194 |
| The Poisson Distribution | p. 198 |
| Continuous Probability Distributions | |
| Statistics in Action: Examining Unethical Behavior: More Fun Than Decent People Think Should Be Legal | p. 215 |
| Continuous Random Variables | p. 216 |
| Normal Random Variables | p. 217 |
| Determining a Probability for a Normal Random Variable | p. 220 |
| Finding Areas Under the Standard Normal Curve | p. 221 |
| Areas Under Any Normal Curve | p. 226 |
| Another Look at the Empirical Rule | p. 233 |
| Using Excel to Determine Areas and Values Having a Specified Area for Normal Populations | p. 233 |
| Normal Approximation to the Binomial | p. 238 |
| Other Continuous Distributions | p. 242 |
| Statistical Inference and Sampling | |
| Statistics in Action: Traffic Congestion Takes Its Toll | p. 259 |
| Random Sampling and the Distribution of the Sample Mean | p. 261 |
| The Central Limit Theorem | p. 267 |
| Confidence Intervals for the Mean of a Normal Population ([sigma] Known) | p. 277 |
| Confidence Intervals for the Mean of a Normal Population ([sigma] Unknown) | p. 286 |
| Selecting the Necessary Sample Size | p. 291 |
| Bootstrap Confidence Intervals | |
| Other Sampling Procedures | |
| Hypothesis Testing for the Mean and Variance of a Population | |
| Statistics in Action: Hillerich & Bradsby Company: Hitting the Quality Mark | p. 303 |
| Hypothesis Testing on the Mean of a Population: Large Sample | p. 305 |
| One-Tailed Test for the Mean of a Population: Large Sample | p. 318 |
| Reporting Testing Results Using a p-Value | p. 325 |
| Hypothesis Testing on the Mean of a Normal Population: Small Sample | p. 332 |
| Inference for the Variance and Standard Deviation of a Normal Population | p. 338 |
| Inference Procedures for Two Populations | |
| Statistics in Action: Rollovers with Sport-Utility Vehicles: Assessing the Impact | p. 355 |
| Independent versus Dependent Samples | p. 356 |
| Comparing Two Means Using Two Large, Independent Samples | p. 358 |
| Comparing Two Normal Population Means Using Two Small, Independent Samples | p. 370 |
| Comparing the Variances of Two Normal Populations Using Independent Samples | p. 382 |
| Comparing the Means of Two Normal Populations Using Paired Samples | p. 391 |
| Estimation and Testing for Population Proportions | |
| Statistics in Action: Streaming Meemies: The Most Valuable Consumer Group on the Internet | p. 411 |
| Estimation and Confidence Intervals for a Population Proportion | p. 413 |
| Hypothesis Testing for a Population Proportion | p. 420 |
| Comparing Two Population Proportions (Large, Independent Samples) | p. 426 |
| Analysis of Variance | |
| Statistics in Action: MBA Programs: Making an Executive Decision to Master Your Career | p. 443 |
| Comparing Two Means: Another Look | p. 445 |
| One-Factor ANOVA Comparing More than Two Means | p. 453 |
| Designing an Experiment | p. 472 |
| Randomized Block Design | p. 475 |
| The Two-Way Factorial Design | p. 490 |
| Quality Improvement | |
| Statistics in Action: Business Excellence: Banking on Six Sigma to Keep Quality Job One | p. 519 |
| Quality Improvement: The Past, Present, and Future | p. 521 |
| The Malcolm Baldrige National Quality Award and ISO 9000 Registration | p. 525 |
| Quality-Improvement Tools | p. 530 |
| Statistical Process Control, Process Variation, and Control Charts | p. 536 |
| Control Charts for Variables Data: The X and R Charts | p. 540 |
| Control Charts for Attribute Data: The p and c Charts | p. 552 |
| Process Capability | p. 560 |
| Applications of the Chi-Square Statistic | |
| Statistics in Action: Data Mining: The Art and Science of Customer Relationship Management | p. 579 |
| Chi-Square Goodness-of-Fit Tests | p. 580 |
| Chi-Square Tests of Independence | p. 597 |
| Correlation and Simple Linear Regression | |
| Statistics in Action: Alpha Beta Soup for the Investor's Soul: Recipe Includes Regression Analysis | p. 621 |
| Bivariate Data and Correlation | p. 622 |
| The Simple Linear Regression Model | p. 634 |
| Inference on the Slope, [beta subscript 1] | p. 640 |
| Measuring the Strength of the Model | p. 649 |
| Estimation and Prediction Using the Simple Linear Model | p. 654 |
| Examining the Residuals | p. 659 |
| Multiple Linear Regression | |
| Statistics in Action: Getting Framed Right Before Your Eyes: Evaluating the Price You Will Pay | p. 687 |
| The Multiple Linear Regression Model | p. 689 |
| Hypothesis Testing and Confidence Intervals for the [beta] Parameters | p. 695 |
| Determining the Predictive Ability of Certain Independent Variables | p. 706 |
| The Problem of Multicollinearity | p. 717 |
| Dummy Variables and Additional Topics in Multiple Linear Regression | p. 724 |
| Model Building | p. 738 |
| Time Series Analysis and Index Numbers | |
| Statistics in Action: Forecasting Underlying Economic Trends: Oh CPI, Why Did You Lie? | p. 759 |
| Components of a Time Series | p. 761 |
| Measuring Trend: No Seasonality | p. 768 |
| Measuring Cyclical Activity: No Seasonality | p. 776 |
| Types of Seasonal Variation | p. 780 |
| Measuring Seasonality | p. 785 |
| A Time Series Containing Seasonality, Trend, and Cycles | p. 793 |
| Index Numbers | p. 804 |
| Quantitative Business Forecasting | |
| Statistics in Action: Going Postal with Forecasting Techniques: Determining Which Methods Receive the Stamp of Approval | p. 819 |
| Methods of Forecasting | p. 821 |
| The Naive Forecast | p. 823 |
| Projecting the Least Squares Trend Equation | p. 826 |
| Simple Exponential Smoothing | p. 830 |
| Exponential Smoothing for a Time Series Containing Trend | p. 836 |
| Exponential Smoothing Method for Trend and Seasonality | p. 840 |
| Choosing the Appropriate Forecasting Procedure | p. 847 |
| Autoregressive Forecasting Techniques | |
| The Other Side of Forecasting: Linear Regression Using Time Series Data | |
| The Problem of Autocorrelation: The Durbin-Watson Statistic | |
| Nonparametric Statistics | |
| Statistics in Action: Putting Service Back into Customer Service: Assessing Survey Data to Get Blue Oval Certified | p. 869 |
| A Test for Randomness: The Runs Test | p. 871 |
| Nonparametric Tests of Central Tendency: Two Populations | p. 880 |
| Comparing More than Two Populations: The Kruskal-Wallis Test and the Friedman Test | p. 897 |
| A Measure of Association: Spearman Rank Correlation | |
| An Alternative to the Least Squares Regression Line: A Nonparametric Approach | |
| Decision Making Under Uncertainty (on CD) | |
| Statistics in Action: Stealth Manufacturers: Using Decision Strategies to Make Products That Tech Giants Put Their Names On | |
| Defining the Decision Problem | |
| Decision Strategies | |
| The Concept of Utility | |
| Decision Trees and Bayes' Rule | |
| Appendixes | |
| Tables | p. 1 |
| Answers to Odd-Numbered Exercises | p. 40 |
| Introduction to Excel | |
| Introduction to SPSS | |
| Introduction to MINITAB | |
| Database Using Household Financial Variables | |
| Database Using Financial Variables of Companies | |
| Derivation of Minimal Total Sample Size | |
| Index | p. 1 |
| Table of Contents provided by Syndetics. All Rights Reserved. |
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