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Business Statistics For Competitive Advantage With Excel 2016

Author: Cynthia Fraser
Publisher: Springer
ISBN: 3319321854
Size: 19.60 MB
Format: PDF, ePub, Mobi
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The revised Fourth Edition of this popular textbook is redesigned with Excel 2016 to encourage business students to develop competitive advantages for use in their future careers as decision makers. Students learn to build models using logic and experience, produce statistics using Excel 2016 with shortcuts, and translate results into implications for decision makers. The textbook features new examples and assignments on global markets, including cases featuring Chipotle and Costco. A number of examples focus on business in emerging global markets with particular emphasis on emerging markets in Latin America, China, and India. Results are linked to implications for decision making with sensitivity analyses to illustrate how alternate scenarios can be compared. The author emphasises communicating results effectively in plain English and with screenshots and compelling graphics in the form of memos and PowerPoints. Chapters include screenshots to make it easy to conduct analyses in Excel 2016. PivotTables and PivotCharts, used frequently in business, are introduced from the start. The Fourth Edition features Monte Carlo simulation in four chapters, as a tool to illustrate the range of possible outcomes from decision makers’ assumptions and underlying uncertainties. Model building with regression is presented as a process, adding levels of sophistication, with chapters on multicollinearity and remedies, forecasting and model validation, auto-correlation and remedies, indicator variables to represent segment differences, and seasonality, structural shifts or shocks in time series models. Special applications in market segmentation and portfolio analysis are offered, and an introduction to conjoint analysis is included. Nonlinear models are motivated with arguments of diminishing or increasing marginal response.

Microsoft Excel Data Analysis And Business Modeling

Author: Wayne Winston
Publisher: Microsoft Press
ISBN: 1509304223
Size: 14.20 MB
Format: PDF, Mobi
View: 2660
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This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Master business modeling and analysis techniques with Microsoft Excel 2016, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands on, scenario-focused guide helps you use Excel’s newest tools to ask the right questions and get accurate, actionable answers. This edition adds 150+ new problems with solutions, plus a chapter of basic spreadsheet models to make sure you’re fully up to speed. Solve real business problems with Excel–and build your competitive advantage Quickly transition from Excel basics to sophisticated analytics Summarize data by using PivotTables and Descriptive Statistics Use Excel trend curves, multiple regression, and exponential smoothing Master advanced functions such as OFFSET and INDIRECT Delve into key financial, statistical, and time functions Leverage the new charts in Excel 2016 (including box and whisker and waterfall charts) Make charts more effective by using Power View Tame complex optimizations by using Excel Solver Run Monte Carlo simulations on stock prices and bidding models Work with the AGGREGATE function and table slicers Create PivotTables from data in different worksheets or workbooks Learn about basic probability and Bayes’ Theorem Automate repetitive tasks by using macros

Business Statistics For Competitive Advantage With Excel 2010

Author: Cynthia Fraser
Publisher: Springer Science & Business Media
ISBN: 1441998578
Size: 57.35 MB
Format: PDF, ePub, Mobi
View: 6063
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Exceptional managers know that they can create competitive advantages by basing decisions on performance response under alternative scenarios. To create these advantages, managers need to understand how to use statistics to provide information on performance response under alternative scenarios. This updated edition of the popular text helps business students develop competitive advantages for use in their future careers as decision makers. Students learn to build models using logic and experience, produce statistics using Excel 2010 with shortcuts, and translate results into implications for decision makers. The author emphasizes communicating results effectively in plain English and with compelling graphics in the form of memos and PowerPoints. Statistics, from basics to sophisticated models, are illustrated with examples using real data such as students will encounter in their roles as managers. A number of examples focus on business in emerging global markets with particular emphasis on China and India. Results are linked to implications for decision making with sensitivity analyses to illustrate how alternate scenarios can be compared. Chapters include screenshots to make it easy to conduct analyses in Excel 2010 with time-saving shortcuts expected in the business world. PivotTables and PivotCharts, used frequently in businesses, are introduced from the start. Monte Carlo simulation is introduced early, as a tool to illustrate the range of possible outcomes from decision makers’ assumptions and underlying uncertainties. Model building with regression is presented as a process, adding levels of sophistication, with chapters on multicollinearity and remedies, forecasting and model validation, autocorrelation and remedies, indicator variables to represent segment differences, and seasonality, structural shifts or shocks in time series models. Special applications in market segmentation and portfolio analysis are offered, and an introduction to conjoint analysis is included. Nonlinear models are motivated with arguments of diminishing or increasing marginal response, and a chapter on logit regression models introduces models of market share or proportions. The Second Edition includes more explanation of hypothesis tests and confidence intervals, how t, F, and chi square distributions behave. The Data Files, Solution Files, and Chapter PowerPoints: The data files for text examples, cases, lab problems and assignments are stored on Blackboard and may be accessed using this link: https://blackboard.comm.virginia.edu/webapps/portal/frameset.jsp Instructors can gain access to the files, as well as solution files and chapter PowerPoints by registering on the Springer site: http://www.springer.com/statistics/business%2C+economics+%26+finance/book/978-1-4419-9856-9?changeHeader Business people can gain access to the files by emailing the author [email protected] https://blackboard.comm.virginia.edu/webapps/portal/frameset.jsp Instructors can gain access to the files, as well as solution files and chapter PowerPoints by registering on the Springer site: http://www.springer.com/statistics/business%2C+economics+%26+finance/book/978-1-4419-9856-9?changeHeader Business people can gain access to the files by emailing the author [email protected]

Introductory Econometrics

Author: Humberto Barreto
Publisher: Cambridge University Press
ISBN: 9780521843195
Size: 39.63 MB
Format: PDF, Kindle
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This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.

An Introduction To Statistical Learning

Author: Gareth James
Publisher: Springer Science & Business Media
ISBN: 1461471389
Size: 42.55 MB
Format: PDF, Docs
View: 7297
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Excel Data Analysis

Author: Hector Guerrero
Publisher: Springer Science & Business Media
ISBN: 9783642108358
Size: 40.17 MB
Format: PDF, Docs
View: 137
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Why does the World Need—Excel Data Analysis, Modeling, and Simulation? When spreadsheets ?rst became widely available in the early 1980s, it spawned a revolution in teaching. What previously could only be done with arcane software and large scale computing was now available to the common-man, on a desktop. Also, before spreadsheets, most substantial analytical work was done outside the classroom where the tools were; spreadsheets and personal computers moved the work into the classroom. Not only did it change how the analysis curriculum was taught, but it also empowered students to venture out on their own to explore new ways to use the tools. I can’t tell you how many phone calls, of?ce visits, and/or emails I have received in my teaching career from ecstatic students crowing about what they have just done with a spreadsheet model. I have been teaching courses related to spreadsheet based analysis and modeling for about 25 years and I have watched and participated in the spreadsheet revolution.

Python For Finance

Author: Yves Hilpisch
Publisher: "O'Reilly Media, Inc."
ISBN: 1491945389
Size: 50.63 MB
Format: PDF, ePub
View: 2993
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The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

Applied Corporate Finance

Author: Aswath Damodaran
Publisher: John Wiley & Sons
ISBN: 0470384646
Size: 67.39 MB
Format: PDF, Mobi
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Readable and usable in style and valuable in approach, this text provides the practical and succinct advice that students and practitioners need, rather than a sole concentration on debate theory, assumptions, or models. Like no other text of its kind, the author applies corporate finance to real companies. The new Third Edition has four real-world core companies to study and follow. Perfected suited for MBA programs’ corporate finance and equity valuation courses, all business decisions are classified into three groups: the investment, financing, and dividend decisions.

Harness Oil And Gas Big Data With Analytics

Author: Keith Holdaway
Publisher: John Wiley & Sons
ISBN: 1118910893
Size: 59.11 MB
Format: PDF, ePub, Mobi
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Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.