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Category : Higher Education
1. Introduction and Data Collection Basic Concepts of Statistics. The Growth of Statistics and Information Technology. How This Text Is Organized. The Importance Tables and Charts for Categorical Data. Organizing Numerical Data. Tables and Charts for Numerical Data. Cross Tabulations. 3. Numerical Descriptive Measures Measures of Central Tendency, Variation, and Shape. Descriptive Numerical Measures for a Population. Computing Descriptive 4. Basic Probability 5. Some Important Discrete Probability Distributions The Probability Distribution for a Discrete Random Variable. Covariance and Its Application in Finance. Binomial Distribution. 6. The Normal Distribution and Other Continuous Distributions Continuous Probability Distributions. The Normal Distribution. Evaluating Normality. The Uniform Distribution. 8. Confidence Interval Estimation Confidence Interval Estimation for the Mean (Known). Confidence Interval Estimation for the Mean (Unknown). 9. Fundamentals of Hypothesis Testing Hypothesis-Testing Methodology. Z Test of Hypothesis for the Mean (Known). One-Tail Tests. t Test of Hypothesis for the Mean Comparing the Means of Two Independent Populations. Comparing the Means of Two Related Populations. 11. Analysis of Variance The Completely Randomized Design: One-Way Analysis of Variance. The Randomized Block Design. 12. Chi-Square Tests and Nonparametric Tests Chi-Square Test for Differences between Two Proportions (Independent Samples). Chi-Square Test for Differences 13. Simple Linear Regression Types of Regression Models. Determining the Simple Linear Regression Equation. Measures of Variation. 14. Introduction to Multiple Regression Developing the Multiple Regression Model. R2, Adjusted R2, and the Overall F test 000. Residual Analysis for the Multiple 15. Multiple Regression Model Building The Quadratic Regression Model. Using Transformations in Regression Models. Influence Analysis. Collinearity. Model Building. Payoff Tables and Decision Trees. Criteria for Decision Making. Decision Making with Sample Information. Utility. 18. Statistical Applications in Quality and Productivity Management Total Quality Management. Six Sigma® Management. The Theory of Control Charts. Control Chart for the Proportion of Answers to Self-Test Problems Answers to Even-Numbered Problems Appendices Index CD-ROM Topics Table of Contents
Using Statistics: Good Tunes
of Collecting Data. Types of Data.
2. Presenting Data in Tables and Charts
Using Statistics: Comparing the Performance of Mutual Funds
Scatter Diagrams and Time Series Plots. Misusing Graphs and Ethical Issues.
Using Statistics: Comparing the Performance of Mutual Funds
Numerical Measures from a Frequency Distribution. Exploratory Data Analysis. The Covariance and the Coefficient of Correlation.
Pitfalls in Numerical Descriptive Measures and Ethical Issues.
Using Statistics: The Consumer Electronics Company
Basic Probability Concepts. Conditional Probability. Bayes’ Theorem. Counting Rules. Ethical Issues and Probability
Using Statistics: The Accounting Information System of the Saxon Plumbing Company
Poisson Distribution. The Hypergeometric Distribution. CD ROM Topic Using the Poisson Distribution to Approximate the
Binomial Distribution.
Using Statistics: Download Time for a Web Site Home Page
The Exponential Distribution. The Normal Approximation to the Binomial Distribution.
7. Sampling Distributions
Using Statistics: The Oxford Cereal Company Packaging Process
Sampling Distributions. Sampling Distribution of the Mean. Sampling Distribution of the Proportion.
Types of Survey Sampling Methods. Evaluating Survey Worthiness. CD ROM Topic Sampling from Finite Populations.
Using Statistics: Auditing Invoices at the Saxon Home Improvement Company
Confidence Interval Estimation for the Proportion. Determining Sample Size. Applications of Confidence Interval Estimation in Auditing.
Confidence Interval Estimation and Ethical Issues. CD ROM Topic Estimation and Sample Size Determination for Finite Populations.
Using Statistics: The Oxford Cereal Company Packaging Process
(Unknown). Z Test of Hypothesis for the Proportion. The Power of a Test. Potential Hypothesis-Testing Pitfalls and Ethical Issues.
10. Two-Sample Tests
Comparing Two Population Proportions. F Test for the Difference between Two Variances.
Using Statistics: The Perfect Parachute Company
The Factorial Design: Two-Way Analysis of Variance.
Using Statistics: Guest Satisfaction at T. C. Resort Properties
among More than Two Proportions. Chi-Square Test of Independence. McNemar Test for the Difference between
Two Proportions (Related Samples). Chi-Square Test for a Variance or Standard Deviation. Chi-Square Goodness
of Fit Tests. Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations. Wilcoxon Signed Ranks
Test: Nonparametric Analysis for Two Related Populations. Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way ANOVA
Friedman Rank Test: Nonparametric Analysis for the Randomized Block Design.
Using Statistics: Forecasting Sales at the Sunflowers Clothing Stores
Assumptions. Residual Analysis. Measuring Autocorrelation: The Durbin-Watson Statistic. Inferences about
the Slope and Correlation Coefficient. Estimation of Predicted Values. Pitfalls in Regression and Ethical Issues.
Using Statistics: Predicting OmniPower Sales
Regression Model. Inferences Concerning the Population Regression Coefficients. Testing Portions of the Multiple Regression Model.
Using Dummy-Variables and Interaction Terms in Regression Models. Logistic Regression.
Using Statistics: Predicting Standby Hours for Unionized Artists
Pitfalls in Multiple Regression and Ethical Issues.
16. Time-Series Forecasting and Index Numbers
Using Statistics: Forecasting Revenues for Three Companies
The Importance of Business Forecasting. Component Factors of the Classical Multiplicative Time-Series Model.
Smoothing the Annual Time Series. Least-Squares Trend Fitting and Forecasting. The Holt-Winters Method for Trend-Fitting
and Forecasting. Autoregressive Modeling for Trend Fitting and Forecasting. Choosing an Appropriate Forecasting Model.
Time-Series Forecasting of Monthly or Quarterly Data. Index Numbers. Pitfalls Concerning Time-Series Analysis.
Index Numbers. Pitfalls Concerning Time-Series Analysis.
17. Decision Making
Using Statistics: Selecting Stocks
Nonconforming Items—The p Chart. The Red Bead Experiment: Understanding Process Variability. Control Chart for
an Area of Opportunity— the c Chart. Control Charts for the Range and the Mean. Process Capability.
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