Business Statistics: For Contemporary Decision Making, International Adaptation, 11/e (Paperback)

Ken Black

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商品描述

本書序言

●New sections on Negative Binomial Distribution, Bernoulli Distribution, and Geometric Distribution
●Updated Decision Dilemma and Decision Dilemma Solved features for currency and relevancy
●Updated Problems and Demonstration Problems with the latest data and current companies, industries, and countries
●New Chapter Cases, including those on Malaysia’s Rubber Output, Electric Vehicle Car Production in Germany, Nestlé in Argentina, Taiwan’s Dominance of the Semiconductor Industry, and FTSE 100 Versus Gold Price for Portfolio Investment
●Extended all time-series data to incorporate current data
●Updated all Minitab outputs to the new version
●Updated Database Mining and Big Data Case features for relevancy

本書特色

●Decision Dilemmas: Each course section is introduced with a real-world business vignette that presents a dilemma and related managerial or statistical questions. Solutions to these questions require the use of techniques presented in the section. A Decision Dilemma Solved feature concludes each section, giving students the opportunity to answer and discuss each question presented at the beginning of the section. 
●Thinking Critically About Statistics in Business Today Exercises: Each course section features one or several of these exercises that give real-life examples of how the statistics presented in the section apply in the business world today. 
●Databases: Twenty databases representing several industries including banking, consumer spending, energy, environmental, finance, manufacturing, healthcare, market research, retailing, stocks, and more provide additional opportunities for students to apply the statistics presented in each chapter. 
●Big Data Cases engage students with variables, samples, and data. With data from the American Hospital Association, these cases enable students to solve and perform a number of data tasks that are practical for the course and future careers.
●Ethical Considerations: This feature in each course section integrates the topic of ethics with applications of business statistics. 
●Demonstration Problems: Virtually every section of every chapter contains demonstration problems. A demonstration problem contains both an example problem and its solution and is used as an additional pedagogical tool to supplement explanations and examples.
●900+ Practice Problems: A treasury of practice problems are available in this course.

作者簡介

Ken Black is currently professor of quantitative management in the College of Business at the University of Houston–Clear Lake. Born in Cambridge, Massachusetts, and raised in Missouri, he earned a bachelor’s degree in mathematics from Graceland University, a master’s degree in math education from the University of Texas at El Paso, a Ph.D. in business administration (management science), and a Ph.D. in educational research from the University of North Texas. Since joining the faculty of UHCL in 1979, Professor Black has taught all levels of statistics courses, business analytics, forecasting, management science, market research, and production/operations management. He received the 2014 Outstanding Professor Alumni Award from UHCL. In 2005, he was awarded the President’s Distinguished Teaching Award for the university. He has published over 25 journal articles and 30 professional papers, as well as two textbooks: Business Statistics: An Introductory Course and Business Statistics for Contemporary Decision-Making. 

目錄大綱

1 Introduction to Statistics and Business Analytics
2 Visualizing Data with Charts and Graphs
3 Descriptive Statistics
4 Probability
5 Discrete Random Variables and Their Probability Distributions
6 Continuous Random Variables and Normal Distributions
7 Sampling and Sampling Distributions
8 Statistical Inference: Estimation for Single Populations
9 Statistical Inference: Hypothesis Testing for Single Populations
10 Statistical Inference About Two Populations
11 Analysis of Variance and Design of Experiments
12 Simple Regression Analysis and Correlation
13 Multiple Regression Analysis
14 Building Multiple Regression Models
15 Time-Series Forecasting and Index Numbers
16 Analysis of Categorical Data
17 Nonparametric Statistics
18 Statistical Quality Control
19 Decision Analysis