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2019 | Book

Summary Statistics Read chapter Read first chapter

Quantitative Methods for Management

A Practical Approach

Authors: Prof. Miguel Ángel Canela, Prof. Inés Alegre, Prof. Alberto Ibarra

Publisher: Springer International Publishing

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

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About this book

This book focuses on the use of quantitative methods for both business and management, helping readers understand the most relevant quantitative methods for managerial decision-making. Pursuing a highly practical approach, the book reduces the theoretical information to a minimum, so as to give full prominence to the analysis of real business problems.

Each chapter includes a brief theoretical explanation, followed by a real-life managerial case that needs to be solved, which is accompanied by a corresponding Microsoft Excel® dataset. The practical cases and exercises are solved using Excel, and for each problem, the authors provide an Excel file with the complete solution and corresponding calculations, which can be downloaded easily from the book’s website. Further, in an appendix, readers can find solutions to the same problems, but using the R statistical language.

The book represents a valuable reference guide for postgraduate, MBA and executive education students, as it offers a hands-on, practical approach to learning quantitative methods in a managerial context. It will also be of interest to managers looking for a practical and straightforward way to learn about quantitative methods and improve their decision-making processes.

Table of Contents

Frontmatter

Basics

Frontmatter
Chapter 1. Summary Statistics
Abstract
This chapter deals with summary statistics. The main three summary statistics are the mean, the standard deviation, and the correlation. The first two are discussed in this chapter. The correlation is left for Chap. 3.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 2. Probability Distributions
Abstract
This chapter is a very elementary introduction to probability distributions. We first introduce the concept of probability in managerial terms and then explain how to operationalize the probability distribution of categorical and continuous variables.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra

Regression Analysis

Frontmatter
Chapter 3. The Regression Line
Abstract
The second part of this book is devoted to regression analysis. This chapter presents the main properties of the simplest regression model, the regression line. Chapters from 4 to 7 deal with regression analysis in general.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 4. Multiple Regression
Abstract
This chapter and Chaps. 5 to 7 cover multiple regression analysis. The discussion is oriented to practical business applications. Most technicalities have been skipped, and the mathematics have been simplified to the indispensable. So, if you have already followed a Statistics course, you may find that the attention paid to the different aspects of the regression analysis differs from what you found there.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 5. Testing Regression Coefficients
Abstract
This chapter continues the discussion of regression analysis engaged in Chaps. 3 and 4. We have seen that the regression coefficients are reported with extra information, which we have not discussed yet. How can we use this information? In particular, we explain here how to use the confidence limits and the p-values to decide about statistical significance. The example of this chapter, which deals with the analysis of the impact of the prices on market share, illustrates the interpretation of the p-values.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 6. Dummy Variables
Abstract
In this chapter, we explain how to introduce categorical variables in a regression analysis, coding the categories with dummy variables. This is needed in most of the applications of regression analysis, since the samples on which we collect our data are typically partitioned into groups. In the example, we use a dummy variable to code gender, which allows us to include the comparison between genders in the analysis in an easy way.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 7. Interaction
Abstract
This chapter adds sophistication to the regression equation through interaction terms. By including an extra term in the equation, we allow for the effect of an independent variable to change with the level of another independent variable. In the example, we use an interaction term to account for the effect of some variables on the efficiency of a truck to be different for different types of motor.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra

Classification

Frontmatter
Chapter 8. Classification Models
Abstract
In this chapter deals with classification models. As in regression analysis, the objective is to predict a dependent variable (Y) from a set of independent variables (X’s). The difference is that, in classification models, Y is a categorical variable. This book only covers binary classification, in which Y takes two values. The example of this chapter is a typical application, default prediction.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 9. Out-of-Sample Validation
Abstract
This chapter deals with the validation of classification models. The role of validation is to dismiss the concerns about overfitting, which happens when we develop a complex model in order to fit the current data but that model fails later to fit new data. The example deals with churn modeling, already mentioned in Chap. 8.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra

Time Series Data

Frontmatter
Chapter 10. Trend and Seasonality
Abstract
In the last part of this book, we present some elementary methods to deal with time series data. This type of data has already appeared in Chaps. 1 and 2. The example uses monthly sales data of cold weather gear. The analysis is mostly graphical, based on a linear trend with multiplicative seasonals. This provides a simple model for describing the past behavior of the data and for forecasting the values for the next year.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 11. Nonlinear Trends
Abstract
In many applications of time series analysis, a linear trend is not adequate for the data. In this chapter we discuss some alternatives, within the scope of Excel. In the example, we use a quadratic trend and additive seasonals for modeling the variation of monthly sales of a beer brand. We also perform an exercise of out-of-sample validation based on the last year’s data.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 12. Moving Average Trends
Abstract
This chapter deals with time series trends based on moving average formulas. A special case, in which the trend values are calculated with the exponential smoothing formula, is discussed with more detail.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Chapter 13. Holt-Winters Forecasting
Abstract
The last chapter of this book presents the Holt-Winters forecasting method, in two versions, with additive and with multiplicative seasonals. The example is an application of Holt-Winters with multiplicative seasonals to data on monthly sales of a meat product at an Ecuadorian supermarket chain.
Miguel Ángel Canela, Inés Alegre, Alberto Ibarra
Backmatter
Metadata
Title
Quantitative Methods for Management
Authors
Prof. Miguel Ángel Canela
Prof. Inés Alegre
Prof. Alberto Ibarra
Copyright Year
2019
Electronic ISBN
978-3-030-17554-2
Print ISBN
978-3-030-17553-5
DOI
https://doi.org/10.1007/978-3-030-17554-2