Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top

2024 | Book

Analytics for Description Read chapter Read first chapter

Business Statistics for Competitive Advantage with Excel and JMP

Basics, Model Building, Simulation, and Cases

Author: Cynthia Fraser

Publisher: Springer Nature Switzerland

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

Login to get access
insite
SEARCH

About this book

This book is the latest title of the popular Excel textbook; redesigned, while including interactive, user-friendly JMP to encourage business students to develop competitive advantages for use in their future careers. How can performance outcome drivers be identified? How can performance outcomes be forecast? Use of regression, conjoint analysis, Monte Carlo simulation provide answers and solutions for specific scenarios. Students learn to build models, produce statistics, and translate results into implications for decision makers.

The text features new and updated examples and assignments, and each chapter discusses a focal case from the business world which can be analyzed using the statistical strategies and software provided in the text. Paralleling recent interest in climate change and sustainability, new case studies concentrate on issues such as the impact of drought on business, automobile emissions, and sustainable package goods.

The book continues its coverage of inference, Monte Carlo simulation, contingency analysis, and linear and nonlinear regression. A new chapter is dedicated to conjoint analysis design and analysis, including complementary use of regression and JMP.

Table of Contents

Frontmatter
Chapter 1. Analytics for Description
Abstract
All analytics begin with data. The type of analytics depends on the type of data. In this chapter, we look at types of data, ways to display data graphically, and means to describe data.
Cynthia Fraser
Chapter 2. Analytics to Infer Population Characteristics and Differences
Abstract
Analytics to infer population characteristics and segment differences rely on samples. Samples are collected and analyzed to efficiently estimate population characteristics. Inference from samples enables tests of hypotheses about what may be true in the population. Those hypotheses are tested and population parameters are estimated with confidence intervals. Included in this chapter are tests of hypotheses and confidence intervals forInference relies on the properties of Normally distributed sample means. Those properties of Normal distributions are explored first, below.
Cynthia Fraser
Chapter 3. Association Between Categorical Variables: Contingency Analysis with Chi Square
Abstract
Categorical variables, including nominal (where numbers are simply labels) and ordinal, rank order variables, are described by tabulating their frequencies or probability. If two categorical variables are associated, the frequencies of values of one will depend on the frequencies of values of the other. Chi square tests the hypothesized association between two categorical variables and contingency analysis quantifies their association.
Cynthia Fraser
Chapter 4. Analytics with Simple Regression to Identify Drivers and Forecast
Abstract
Analytics from regression can easily create a long range forecast based on trend. Regression and correlation, upon which regression is based, reflect linear association between two variables. Regression quantifies the influence of a continuous, independent driver x on a continuous dependent, performance variable y. In the case of a trend focused forecast, the driving variable x is time period. In later chapters, focus will be expanded to both explain how an independent decision variable x drives a dependent performance variable y and also in predicting performance y to compare the impact of alternate decision variable x values. X is also called a predictor, since from x we can predict y. Here, focus is on prediction of performance or response y from knowledge of the driver, time period, x.
Cynthia Fraser
Chapter 5. Finance Application: Portfolio Analysis with a Market Index as a Leading Indicator in Simple Linear Regression
Abstract
Simple linear regression of stock rates of return with a Market index provides an estimate of beta, a measure of Market specific risk, which is central to finance investment theory.
Cynthia Fraser
Chapter 6. Analytics to Account for Segment or Scenario Differences
Abstract
In this chapter, 0–1 indicator or “dummy” variables are used to incorporate segment differences, shocks, or structural shifts into regression models. With cross sectional data, indicators can be used to incorporate the unique responses of particular groups or segments. With time series data, indicators can be used to account for external shocks or structural shifts. Indicators also offer one option to account for seasonality or cyclicality in time series.
Cynthia Fraser
Chapter 7. Analytics to Model Nonlinear Response
Abstract
In many cases, trends (and other responses) are not constant over time periods. To accommodate nonlinear response, variables can be rescaled to produce valid models with superior fit. An example will be offered in the context of trend models built for forecasting.
Cynthia Fraser
Chapter 8. Building Multiple Regression Models
Abstract
Explanatory multiple regression models are used to accomplish two complementary goals: identification of drivers of performance and prediction of performance under alternative scenarios. Multiple regression offers a major advantage over simple regression. Multiple regression accounts for the joint impact of multiple drivers, which provides a truer estimate of the impact of each one individually. Looking at just one driver, as in simple regression, its estimated impact will be much greater than it actually is. A single driver takes the credit for the joint influence of multiple drivers. For this reason, multiple regression provides a clearer picture of influence.
Cynthia Fraser
Chapter 9. Conjoint Analysis and Experimental Data
Abstract
This chapter introduces ANOVA, (Analysis of Variance) to analyze data from conjoint analysis experiments. Conjoint analysis is used in experiments to quantify customer preferences for better design of new products and services.
Cynthia Fraser
Chapter 10. Explanatory Time Series Models
Abstract
An explanatory model from time series data allows us to identify performance drivers and forecast performance given specific driver values, just as regression models from cross sectional data do. When decision makers want to forecast future performance in the shorter term, a time series of past performance is used to identify drivers and fit a model. A time series model can be used to identify drivers whose variation over time is associated with later variation in performance over time.
Cynthia Fraser
Chapter 11. Analytics to Simulate Likely Outcomes
Abstract
Decision makers deal with uncertainty when considering future scenarios. Future performance levels depend on multiple influences with uncertain future values. To estimate future performance, managers make assumptions about likely future scenarios and uncertain future values of performance drivers. Monte Carlo simulation can be used to simulate random samples using decision makers’ assumptions about performance driver values, and those random samples can then be combined to produce a distribution of likely future outcomes. Inferences from a simulated distribution of outcomes, given assumptions, can then be made to inform decision making and to adjust assumptions.
Cynthia Fraser
Backmatter
Metadata
Title
Business Statistics for Competitive Advantage with Excel and JMP
Author
Cynthia Fraser
Copyright Year
2024
Electronic ISBN
978-3-031-42555-4
Print ISBN
978-3-031-42554-7
DOI
https://doi.org/10.1007/978-3-031-42555-4