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2018 | Buch

Basics of Regression Models Kapitel lesen Erstes Kapitel lesen

Applied Regression Analysis for Business

Tools, Traps and Applications

verfasst von: Dr. Jacek Welc, Prof. Pedro J. Rodriguez Esquerdo

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric procedures, which are useful in building regression models for economic forecasting and supporting business decisions. A significant part of the book is devoted to traps and pitfalls in implementing regression analysis in real-world scenarios. The book consists of nine chapters, the final two of which are fully devoted to case studies.

Today's business environment is characterised by a huge amount of economic data. Making successful business decisions under such data-abundant conditions requires objective analytical tools, which can help to identify and quantify multiple relationships between dozens of economic variables. Single-equation regression analysis, which is discussed in this book, is one such tool. The book offers a valuable guide and is relevant in various areas of economic and business analysis, including marketing, financial and operational management.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Basics of Regression Models
Abstract
A regression model, also called an econometric model, is a quantitative analytical tool in which the behavior of some variables is explained by other variables. A single-equation regression model has the form of an equation (a mathematical function) which quantifies a relationship between a dependent variable (which is explained by the model) and one or more explanatory variables, which are also called regressors (and which have statistical or causal relationships with the dependent variable).
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 2. Relevance of Outlying and Influential Observations for Regression Analysis
Abstract
As the name suggests, the outlying observations (commonly called “outliers”) are the ones which “lie out” from the remaining observations in the sample. In regression models, the outliers are observations with large residuals (Makridakis et al. 1998). First, we will discuss the nature of the outliers occurring in the case of the single variables, which we will call the univariate outliers. Then we will proceed to discussing the more subtle type of outliers, which occur in the case of multivariate analyses.
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 3. Basic Procedure for Multiple Regression Model Building
Abstract
In this chapter, we will illustrate the step-by-step procedure for the estimation of an econometric model. Starting from the raw set of hypothetical data (concerning a fictitious company), we will guide the reader through the following phases of the model building process:
  • The preliminary specification of the model.
  • The detection of potential outliers in the data set.
  • The selection of explanatory variables from the set of candidate variables (by means of two alternative procedures, that is, “general to specific modeling” and “stepwise regression”).
  • The tentative interpretation of the obtained regression structural parameters.
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 4. Verification of the Multiple Regression Model
Abstract
In the last section of the previous chapter, we provided the tentative interpretation of structural parameters of our model. However, before the model is fully interpreted and applied in practice for forecasting or making simulations, it must be verified for statistical correctness.
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 5. Common Adjustments to Multiple Regressions
Abstract
Many variables which constitute causal factors for other variables are of qualitative nature. Their qualitative nature means that they cannot be directly measured by numbers. However, if they influence the behavior of other variables, they must be dealt somehow in process of regression model building. We might imagine hundreds of such qualitative variables; however, examples of the most commonly used in economic analyses are:
  • Variables used in marketing research (mostly in cross-sectional models), relating to peoples’ profiles and impacting peoples’ behavior as consumers:
    • Sex (male vs. female).
    • Marital status (married vs. non-married).
    • Having children vs. not having children.
    • Occupation (“white-collar” vs. “blue-collar” vs. other).
    • Education (higher vs. secondary level vs. primary level).
    • Place of living (big city vs. small town vs. rural).
  • Variables used in modeling economic and financial results of companies (both in time-series as well as in cross-sectional models):
    • Company’s industry according to its SIC (Standard Industrial Classification) code (belonging to a given industry vs. belonging to other industries).
    • Company’s stock market listing status (listed on stock exchange vs. belonging to private shareholders).
    • Company’s shareholding status (state-owned vs. owned by private shareholders).
    • Occurrence or not of one-off events such as employees’ strike in a period for which economic results of a company are reported.
  • Variables used in modeling macroeconomic processes (mostly in time-series models):
    • Occurrence or not of a recession in a period.
    • Occurrence or not of a one-off event impacting inflation rate, such as flood or drought.
    • Seasonal factors (first quarter of a year vs. second quarter vs. third quarter vs. fourth quarter).
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 6. Common Pitfalls in Regression Analysis
Abstract
Much too often the analytical tools offered by statistics and econometrics can be heavily abused. The incorrect application of the regression analysis and unskilled interpretation of its results might result in making disastrous economic decisions. In these circumstances, quantitative analysis can produce highly misleading results. In the following sections of this chapter, we will illustrate some common pitfalls of the reckless regression analysis.
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 7. Regression Analysis of Discrete Dependent Variables
Abstract
Many social and economic variables are of a qualitative nature. They are examples of discrete variables. These variables relate mainly to events or processes that result in a small number of possibilities, where many times the key issue is whether the particular event happens or not.
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 8. Real-Life Case Study: The Quarterly Sales Revenues of Nokia Corporation
Abstract
In this chapter of the book, we offer an example of a regression model which may be useful in predicting company’s quarterly sales revenues. The presented example constitutes a real-life case study, based on time-series data reported by Nokia Corporation in its quarterly financial reports for the individual quarters of 2002–2011 period.
Jacek Welc, Pedro J. Rodriguez Esquerdo
Chapter 9. Real-Life Case Study: Identifying Overvalued and Undervalued Airlines
Abstract
A preceding chapter offered a real-life example which illustrated a construction and application of a time-series econometric model. In contrast, in this chapter, we present an example of the cross-sectional model, based on the observed stock prices and financial results of selected airline companies, listed on global stock exchanges. The purpose of the model discussed in this chapter is to capture the statistical relationships between relative market values of selected airlines (as measured by their price-to-sales ratios) on one side and their selected accounting ratios, computed on the basis of their reported financial statements. Finally, the obtained model will be used in identifying potentially undervalued and overvalued stocks.
Jacek Welc, Pedro J. Rodriguez Esquerdo
Backmatter
Metadaten
Titel
Applied Regression Analysis for Business
verfasst von
Dr. Jacek Welc
Prof. Pedro J. Rodriguez Esquerdo
Copyright-Jahr
2018
Electronic ISBN
978-3-319-71156-0
Print ISBN
978-3-319-71155-3
DOI
https://doi.org/10.1007/978-3-319-71156-0