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KEY MESSAGE: TheEleventh Editionof this highly-regarded introductory text emphasizes inference and sound decision-making through its extensive coverage of data collection and analysis. McClave develops statistical thinking and teaches readers to properly assess the credibility of inferences-from the vantage point of both the consumer and the producer. This edition incorporates more exercises and more visual features, such as redesigned end-of-chapter summaries and an increased use of applets. This text assumes a mathematical background of basic algebra. KEY TOPICS: Statistics, Data, and Statistical Thinking; Methods for Describing Sets of Data; Probability; Discrete Random Variables; Continuous Random Variables; Sampling Distributions; Inferences Based on a Single Sample: Estimation with Confidence Intervals; Inferences Based on a Single Sample: Tests of Hypothesis; Inferences Based on a Two Samples: Confidence Intervals and Tests of Hypotheses; Analysis of Variance: Comparing More Than Two Means; Simple Linear Regression; Multiple Regression and Model Building; Categorical Data Analysis; Nonparametric Statistics MARKET: For all readers interested in statistics.
Dr. Jim McClave is currently President and CEO of Info Tech, Inc., a statistical consulting and software development firm with an international clientele. He is also currently an Adjunct Professor of Statistics at the University of Florida, where he was a full-time member of the faculty for twenty years.
Terry Sincich obtained his PhD in Statistics from the University of Florida in 1980. He is an Associate Professor in the Information Systems & Decision Sciences Department at the University of South Florida in Tampa. Dr. Sincich is responsible for teaching basic statistics to all undergraduates, as well as advanced statistics to all doctoral candidates, in the College of Business Administration. He has published articles in such journals as the Journal of the American Statistical Association, International Journal of Forecasting, Academy of Management Journal, and the Auditing: A Journal of Practice & Theory. Dr. Sincich is a co-author of the texts Statistics, Statistics for Business & Economics, Statistics for Engineering & the Sciences, and A Second Course in Statistics: Regression Analysis.
Table of Contents
Statistics, Data, and Statistical Thinking
The Science of Statistics
Types of Statistical Applications
Fundamental Elements of Statistics
Types of Data
The Role of Statistics in Critical Thinking
Statistics in Action: USA Weekend Teen Surveys - Are Boys Really from Mars and Girls from Venus?
Using Technology: Creating and Listing Data in Minitab
Methods for Describing Sets of Data
Describing Qualitative Data
Graphical Methods for Describing Quantitative Data
Numerical Measures of Central Tendency
Numerical Measures of Variability
Interpreting the Standard Deviation
Numerical Measures of Relative Standing
Methods for Detecting Outliers (Optional)
Graphing Bivariate Relationships (Optional)
Distorting the Truth with Descriptive Techniques
Statistics In Action: The "Eye Cue"
Test: Does Experience Improve Performance?
Using Technology: Describing Data in Minitab
Probability (from McClave 11endash;Chap 3)
Events, Sample Spaces, and Probability
Unions and Intersections
The Additive Rule and Mutually Exclusive Events
The Multiplicative Rule and Independent Events
Some Counting Rules (Optional)
Statistics In Action: Lotto Buster! - Can You Improve Your Chances of Winning the Lottery?
Using Technology: Generating a Random Sample in Minitab
Random Variables and Probability Distributions
Two Types of Random Variables
Probability Distributions for Discrete Random Variables
The Binomial Distribution
Probability Distributions for Continuous Random Variables
The Normal Distribution
Descriptive Methods for Assessing Normality
Approximating a Binomial Distribution with a Normal Distribution (Optional)
The Central Limit Theorem
Statistics in Action: Super Weapons Development - Is the Hit Ratio Optimized?
Using Technology: Binomial Probabilities, Normal Probabilities, and Normal Probability Plots in Minitab
Inferences Based on a Single Sample: Estimation with Confidence Intervals
Identifying the Target Parameter
Large-Sample Confidence Interval for a Population Mean
Small-Sample Confidence Interval for a Population Mean
Large-Sample Confidence Interval for a Populati
Table of Contents provided by Publisher. All Rights Reserved.