Exploring Decision Rules for Election Results by Classification Trees

This study explores the most important socio-economic variables determining the voting decisions of the provinces in Municipality Elections by using classification trees. We collected data on many potential variables that may affect voting decisions in favor  of a political party. Each province’s economic, geographic and demographic data is taken into consideration as independent variables. The dependent variable is the winner party in 2014 Municipality Elections. Data set consists of 81 provinces’ data on 69 variables. The aim of the study is to find which variables affect voting decision the most and try to find a pattern that may lead political campaigns. Amongst many classification algorithms, we used C5.0 algorithm coded in R. It helps us explore the structure of a set of data, while developing easy to visualize decision rules for predicting a categorical (classification tree) or continuous (regression tree) outcome. The C5.0 algorithm determines the separation criterion with the greatest information gain in each decision node and performs optimal separation. Since our data size is small, we used k=1000 trials (estimations) and then summarized them to provide more robust results. By choosing C5.0 algorithm’s sub-trial size as 5, 5000 trees are formed and the mean of all importance scores of all trees formed are calculated and interpreted. The most important independent variables discriminating the voting decision are found to be the result of the previous elections, mean household population, proportion of population between ages 15 and 19, electricity consumption per person, and proportion of population between ages 55 and 64. Keywords: classification trees, voting decision, C5.0 algorithm, decision tree

JMASM 49: A Compilation of Some Popular Goodness of Fit Tests for Normal Distribution: Their Algorithms and MATLAB Codes (MATLAB)

The main purpose of this study is to review calculation algorithms for some of the most common non-parametric and omnibus tests for normality, and to provide them as a compiled MATLAB function. All tests are coded to provide p-values for those normality tests, and the proposed function gives the results as an output table

VERİ ZARFLAMA ANALİZİNDEKİ AĞIRLIK KISITLAMALARININ BELİRLENMESİNDE ANALİTİK HİYERARŞİ SÜRECİNİN KULLANIMI

Veri Zarflama Analizi - VZA (Data Envelopment Analysis - DEA) son yıllarda yöneylem araştırması ve yönetim bilimlerinde çok yaygın olarak kullanılan bir metottur. VZA matematiksel programlama tekniklerini kullanarak çok sayıda girdi ve çok sayıda çıktıyı değerlendirir ve benzer Karar Birimleri'nin (Decision Making Unit - DMU) etkinlik (efficiency) analizini yapar. VZA'nin en önemli avantajı, klasik etkinlik yaklaşımlarından farklı olarak girdi ve çıktıların ağırlıklarının analizci tarafından belirlenmesidir. Bu çalışmada, VZA'daki ağırlıkların kısıtlanması için oluşturulacak kısıt koşullarının belirlenmesinde, uzman görüşünü dikkate alan Analitik Hiyerarşi Sureci (AHS) kullanılmıştır. Oluşturulan ağırlık kısıtlamalı VZA modeli deneysel bir veri seti üzerinde uygulanmış ve sonuçlar ağırlık kısıtlamasız modelin sonuçlarıyla karşılaştırılmıştır.ABSTRACT Data Envelopment Analysis (DEA) has become one of the most widely used methods in operations research and management science. DEA uses mathematical programming techniques to evaluate multi input-multi output data and finds the relative efficiency scores of similar Decision Making Units (DMUs). On the contrary to classical efficiency approaches, the most important feature of DEA is that the determination of weights for inputs and outputs by the analyzer is not required. In this study, Analytic Hierarchy Process is used to determine the constraints for weight restrictions. The weight-restricted model is then applied to a data set, and the results are compared to that of the unrestricted model

REAL OPTIONS IN INFORMATION TECHNOLOGY PROJECTS

Many sources such as the immaturity, complexity and unpredictable evolution of the technologies themselves, and the difficulty of predicting the market demand generally cause uncertainty in the Information Technology (IT) projects. Managerial flexibility has value in the context of uncertain IT projects, as management can continuously gather information about uncertain project and market characteristics and, based on this information, change its course of action. As traditional capital budgeting (or project appraisal) approaches fail to consider embedded managerial flexibility in projects, the new investment evaluation approach called "real options" has come on the scene recently. "Options thinking," an emerging investment management philosophy based on the theory of financial options can provide a promising foundation for hedging the risks under uncertainty. This paper aims at analyzing a real IT investment having growth opportunities by using real options approach. Specifically, the value of investing in a software project as well as embedded flexibility in this opportunity is modeled as an option contract. The expected costs required for completing the project and thereafter the resulting asset value are considered as the base-case parameters of this contract. Option values derived from Binomial method are used for evaluating the project and the optimal investment policy is determined based on these values. As a result, it is shown that the value of an IT project having flexibility can differ from one obtained with traditional capital budgeting methods and a rejected project which was seen as unprofitable before can turn a profitable one through embedded flexibilities. Çoğu zaman teknolojilerin olgunlaşmamış olması, karmaşıklığı, bu teknolojilerin kendilerinin tahmin edilemez gelişimi ve pazar talebini önceden tahmin etmenin zorluğu gibi nedenler Bilişim Teknolojisi (BT) projelerinde belirsizliğe neden olmaktadır. Belirsiz proje ve pazar karakteristikleri hakkında sürekli bilgi toplayabilen ve bu bilgiye dayalı olarak kararlarını ve aksiyonlarını revize edebilen bir yönetim söz konusu ise, yönetsel esneklik, belirsiz BT projeleri bağlamında bir değere sahiptir. Geleneksel sermaye bütçeleme (ya da proje değerleme) yöntemleri, projelerde gizli olan bu yönetsel esnekliği doğru değerleyemediğinden, son zamanlarda "reel opsiyonlar" olarak adlandırılan yeni bir değerleme yaklaşımı ortaya çıkmıştır. Finansal opsiyon teorisine dayanan bir yatırım yönetimi yaklaşımı olarak "opsiyonlarla düşünme", projelerdeki belirsizlik durumlarında risklerden korunmayı sağlayabilir. Bu çalışmanın amacı, büyüme esnekliğine sahip gerçek bir BT yatırımını reel opsiyonlar yaklaşımı ile analiz etmektir. Spesifik olarak, bir yazılım projesine yatırım yapmanın ve bu fırsatın barındırdığı esnekliklerin değeri, bir opsiyon sözleşmesi olarak modellenmiştir. Bu sözleşmede, temel alınan durum değişkenleri olarak, projenin tamamlanması ile elde edilen varlığın değeri ve projeyi tamamlamanın beklenen maliyeti söz konusudur. Binom yaklaşımına dayalı olarak elde edilen opsiyon değerleri, projenin değerlendirilmesinde dikkate alınmış ve bu değerlere bağlı olarak optimal yatırım politikası belirlenmiştir. Sonuç olarak, bir BT projesinin barındırdığı esnekliklere bağlı olarak değerinin, geleneksel sermaye bütçeleme yöntemleri ile elde edilen değerden daha farklı olabileceği ve kazançsız görülüp reddedilen bir projenin bu esnekliklerle daha karlı bir projeye dönüşebileceği gösterilmiştir

Comparative study on retail sales forecasting between single and combination methods

In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company’s current system