Business Statistics with MML/MSL Student Access Code Card (2nd Edition)
Author Information
Norean D. Sharpe, Richard D. De Veaux, Paul VellemanProduct Details
ISBN: 9780321771933Publish Date: 01/06/2011
Publisher: Addison Wesley
Number of pages: 1008
Description
Business Statistics, Second Edition, helps students learn the statistical tools and develop the understanding they’ll need to go successfully from the classroom to the boardroom. The dynamic approach conquers the modern challenges of teaching business statistics by making it relevant, emphasizing analysis and understanding over simple computation, preparing students to make better business decisions through statistics, and effectively communicating results.
This text features a wealth of real data applications, with coverage of current issues including ethics and data mining. It draws readers in using a conversational writing style and delivers content with a fresh, exciting approach that reflects the authors’ blend of teaching, consulting, and entrepreneurial experiences. Learning tools such as the Plan/Do/Report guided examples prepare students to tackle any business problem they will encounter as a future business leader.
EXPLORING AND COLLECTING DATA: Statistics and Variation, Data, Surveys and Sampling, Displaying and Describing Categorical Data, Displaying and Describing Quantitative Data, Correlation and Linear Regression; MODELING WITH PROBABLITY: Randomness and Probability, Random Variables and Probability Models, The Normal Distribution, Sampling Distributions; INFERENCE FOR DECISION MAKING: Confidence Intervals for Proportions, Confidence Intervals for Means, Testing Hypotheses, Comparing Two Groups, Inference for Counts: Chi-Square Tests; MODELS FOR DECISION MAKING: Inference for Regression, Understanding Residuals, Multiple Regression, Building Multiple Regression Models, Time Series Analysis; SELECTED TOPICS IN DECISION MAKING: Design and Analysis of Experiments and Observational Studies, Quality Control, Nonparametric Methods, Decision Making and Risk, Introduction to Data Mining
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