Statistics for Management and Economics, Abridged (Custom Solutions) (12版)

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【簡介】 Discover how important statistical methods are for today's managers and economists as you learn how to apply these tools to real business problems with Keller's best-selling STATISTICS FOR MANAGEMENT AND ECONOMICS. This edition emphasizes applications over calculations. A unique three-step "ICI" approach to problem solving teaches you how to "identify" the correct statistical technique by focusing on the problem objective and data type; "compute" the statistics by hand or using Excel®; and, finally, "interpret" the results within the context of the problem. Data-driven examples and cases demonstrate how marketing managers, financial analysts, accountants and economists rely on statistical applications. In addition, thousands of exercises and datasets use real data from individuals, sports teams or the stock market to let you practice what you've learned. 【目錄】 1 What is Statistics? 2 Graphical Descriptive Techniques 3 Numerical Descriptive Techniques 4 Probability 5 Random Variables and Discrete Probability Distributions 6 Continuous Probability Distributions 7 Sampling Distributions 8 Introduction to Estimation 9 Introduction to Hypothesis Testing 10 Inference About a Population 11 Inference about Comparing Two Populations 12 Analysis of Variance 13 Chi-Squared Tests 14 Simple Linear Regression and Correlation Appendix A Data File Sample Statistics Appendix B Tables Appendix C A Guide to Statistical Techniques Appendix D Index of Excel Output and Instructions Appendix E Management and Economic Applications Appendix F Answers to Selected Even-Numbered Exercises

Big Data Management and Analytics

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【簡介】 With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge. Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape. Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system. Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance. 【目錄】 Contents: Introduction to Big Data Big Data Management and Modeling Big Data Processing Big Data Analytics and Machine Learning Big Data Analytics Through Visualization Taming Big Data with Spark 2.0 Managing Big Data in Cloud Storage Big Data in Healthcare Big Data in Finance Enabling Tools and Technologies for Big Data Analytics References Index