The Use of Dummy Variables in Regression Analysis By Smita Skrivanek, Principal Statistician, MoreSteam.com LLC What is a Dummy variable? A Dummy variable or Indicator Variable is an artificial variable created to represent an attribute with two or more distinct categories/levels. Why is it used? Regression analysis treats all independent (X) variables in the analysis as numerical. Numerical variables are interval or ratio scale variables whose values are directly comparable, e.g. ‘10 is twice
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5. A consumer organization wants to develop a regression model to predict mileage (as measured by miles per gallon) based on the horsepower of the car’s engine and the weight of the car (in pounds). Data were collected from a sample of 50 recent car models, and the results are organized and stored in the attached spreadsheet Auto. | |Coefficients |Standard Error |t Stat |P-value | |Intercept |58.15708245 |2.658248208
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understand and solve problems.. It is a study of the application of differential calculus, integral calculus and matrix algebra, measures of central tendencies, measures of averages, correlation and regression etc. It also includes the application of the techniques of management science such as Linear programming, Game theory, CPM and PERT analyses to business problems. The relevance and usefulness of Quantitative Techniques in seven functional areas of Management are discussed in this paper. Introduction:
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© Orangetree Business Solutions Private Limited, 2012 No part of this book should be referenced or copied without the prior permission of the company. A FEW WORDS TO THE STUDENTS Analytics is becoming a popular tool for managerial decision making. It‘s still not so widespread in countries like India, but in the west it has become a standard practice. Previously studying analytics involved an in depth knowledge of statistics and programming languages. But widespread availability of statistical
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pool. 2. a. Regression Analysis: AppraisedValue versus House Size(sq ft) The regression equation is AppraisedValue = 49.10 + 0.2074 House Size(sq ft) S = 121.230 R-Sq = 58.7% R-Sq(adj) = 58.3% Analysis of Variance Source DF SS MS F P Regression 1 1841760 1841760 125.32 0.000 Error 88 1293302 14697 Total 89 3135062 Fitted Line: AppraisedValue versus House Size(sq ft) 2b. The regression equation is AppraisedValue
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ABSTRACT The research question of this study is whether the weighted moving average as a technical method and simple trading plan as a fundamental approach can help Indonesian trader and/or investor in gaining consistent profit. The finding of this study will be useful to all the traders because it will show whether the usage of both fundamental and technical analysis will give better result than just using one of them as an analysis method. Upon answering the research question, researchers
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Structural equation models (SEMs) include path analysis and factor analysis. SEMs include the econometrician's Simultaneous Equations Models. However, SEMs may include unobserved ("latent") variables (constructs) representing unobserved concepts. Multiple regression analysis often raises more questions than it answers. It examines data via correlations without establishing causation. Path analysis seeks
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Understanding the Factors Affecting The Unemployment Rate Through Regression Analysis An Individual Report Presented to The Faculty of Economics Department In Partial Fulfillment To The Requirements for ECONMET C31 Submitted to: Dr. Cesar Rufino Submitted by: Aaron John Dee 10933557 April 8, 2011 1 TABLE OF CONTENTS I. INTRODUCTION 4 A. Background of the Study 4 B. Statement of the Problem 5 C. Objective 5 II. THEORETICAL FRAMEWORK AND RELATED
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sum of squares | MLR | 1989.69 | ANN | 1716.93 | GA | 496.5 | Results: * As can be seen from the above table, Multiple linear regression technique (MLR) provides the model with highest “Total Sum of Squares” followed by the Artificial Neural Network (ANN) anby Genetic Algorithm (GA). * The three models give different results because because MLR assumes a linear relationship between the dependent and independent variables while both ANN and GA don’t have any such assumptions and they
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balance with a larger household size. 2. The equation of best fit, also known as the regression equation, is Credit Balance($) = 2591 + 403 Size. This means that for each 1 increase in household size that that credit balance increase by $403. 3. The coefficient of correlation is the square root of .566 which is .7523. This value being closer to one than to zero and being positive implies strong linear relationship between credit balance and size. It confirms that as credit balance increases
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