...Significance of Regression Analysis In statistics, regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances...
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...INTRODUCTION Unprecedented globalizations have witnessed double digit economic growth resulting in fierce competition and accelerated pace of innovation. As a result inflow of Foreign Direct investments has become a striking measure of economic development in both developed and developing countries. FDI and FII thus have become instruments of international economic integration and stimulation. Fast growing economies like Singapore, China, Korea etc have registered incredible growth at onset of FDI. Though US captures most of the FDI inflows, developing countries still account for significant growth of FDI and rise in FII. FDI not only gives access to foreign capital but also provides domestic counties with cutting edge technology, desired skill sets, tools of innovation and other complementary skills. Apart from helping in creating additional economic activity and generating employment, foreign investment also facilitates flow of sophisticated technology into the country and helps the industry to march into advanced technology. A favorable business environment fostered Indian economy after 1991, when the government of India opened the door for foreign capital in the way of direct investment and through foreign institutional investors. The policies drafted to stimulate the flow of foreign capital in to India provided much needed impetus for India to emerge as an attractive destination for foreign investors. Consequently, the international capital inflows have been...
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...Project Title: A STATISTICAL ANAYLYSIS OF NBA PLAYER SALARIES USING A MULTIPLE REGRESSION. ABSTRACT Basketball is one of the most popular sports in the world and National Basketball Association (NBA) is the most popular basketball league in the world. The NBA league is based on the United States of America and it consists of 30 teams. The NBA is so popular that the NBA finals are the 2nd most watched televised event in the U.S. after the NFL (National Football League) Super Bowl. Sometimes when we think about NBA players and the enormous amount of money they are making, we become a little jealous. It is well known about how some star players make so much money or are over-paid and yet can hardly form a sentence. The greatest challenge for the board of NBA has been how to harmonize the salaries. Due to this various people have tried to come up with different solutions .Some argue that height ,weight and physical strength play a big role in team winning but this is not the case as some players who are short help their teams win in several occasions. To solve this problem a multiple regression analysis will be utilized to analyze the salary data. A relationship will be established between the salary and performance variables. The other challenge will be choosing the model parameters that will be significant in order to be included in the model that will be developed. This can be solved by arranging the factors affecting an NBA player salary in a decreasing order of importance...
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... Understand the concepts of Binary Logistic Regression Logistic regression is essential in predicting a categorical variable present in a set of variables that are predictors. During Logistic regression, categorical dependent variable which is the discriminant function is used when all the predictors that are present are continuous and distributed in a nice way. The binary logic regression has become the most preferred data analysis method that describes the relationship between response variable and an explanatory variable that and it is usually used where a variable follows binomial distribution. Assumptions of binary logistic regression Among the assumptions applied in the application of binary logistic regression is that logistic regression usually does not assume a relationship that is linear between the dependable and undependable variables. It is a must for the dependable variable to be a dichotomy i.e. must have two categories. It is also not required that the independent variables to be an interval, distributed in a normal way, linearly related, or even have linear variance within specific groups. Another assumption is that large samples are most important in this regression because it has a maximum likelihood of coefficients of large sample estimates. It is usually recommended that one should have a linear regression of 50 cases and above per predictor. Requirements for Binary Logistic Regression When carrying out binary logistics it is required...
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...classification. Nominal data Nominal data has no ranking/ordering associated with it. For example: city of birth; gender; etc. Interval data Interval data is like ordinal except we say the intervals between each values are equally split. For example, in temperature, the difference between 20 and 30 degrees is the same magnitude as 78 and 79. https://answers.yahoo.com/question/index?qid=20081203215352AAg56gC A clear picture of this importance in SPSS is depicted as follow: Figure 1 Types of data in SPSS The next important item to consider prior to entering the data into the SPSS is the objectives of data analysis. This according to Sekaran (2003) are to get a ‘feel of data’, test the ‘goodness of data’ and hypothesis testing as depicted in Figure 2 below: Figure 2 Flow diagram of Data Analysis Process Getting data ready for analysis Editing Data The first task needed to be done is to edit the data. This means we have to decipher and code the data prior to its entering into the computer. For example, open end question which could be basically responded by “yes” or “no” may not be done clearly with follow up notes. This often is unstructured questionnaires require considerable amount of time to organise and manage the data. This task has to be done with...
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...Question 1 For 4 Dow Jones stocks for a 15 year period, compute quarterly realized betas from daily data. Find firm specific and macroeconomic variables that help explain quarterly beta. Answer 1.1 Factors Introduction Factor model survey the sensitivity of a stock return as a function of one or more factors. There are single-factor and multi-factor models. In factors model, based on the type of factors used, it can be classified to economic and fundamental factor models. Economic factor models use macroeconomic and financial markets variables as factors, while fundamental factor models use firm-specific microeconomic variables, such as financial indicators. In recent research shows that the change in macroeconomic factors could be reflected in the change of systematic risk which impacts a stock’s expected return (Humpe & Macmillan 2007). Macroeconomic factors included industry production index, CPI, GDP, unemployment rate, inflation rate, risk premium, default premium, business cycle index and so on. From Chen (1986) notable study which uses variables include industrial production, inflation, risk premium, term structure, market index, consumption and oil prices to found out that industrial production, unanticipated change in the risk premium, unanticipated inflation, and, a slightly weaker, the unanticipated change in term structure, are the most important factors affecting expected stock returns. The 15 macroeconomic variables used as factors in our model are...
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...1. Module Name: Introductory Econometrics Code: P12205 Credits: 10 Semester: Spring 2011/12 Delivery: 16 one-hour lectures + 4 one-hour workshops Aims: The main aims of this module are: to introduce students to the principles, uses and interpretation of regression analysis most commonly employed in applied economics; to provide participants with sufficient knowledge of regression methods to critically evaluate and interpret empirical research. On completion of this module students should be able to: demonstrate understanding of the assumptions and properties underlying regression analysis and the principle of ‘least squares’; interpret and manipulate the coefficients of multiple regression and performance criteria; conduct diagnostic checking of the validity of regression equations coefficients; appreciate the problems of misspecification, multicollinearity, heteroscedasticity and autocorrelation. Content: 1. Simple Regression Analysis 2. Multiple Regression Analysis 3. Dummy Variables 4. Heteroscedasticity 5. Autocorrelation Main Textbook: Dougherty, C. (2011). Introduction to Econometrics, 4th edition, Oxford. 2. Module Name: Computational Finance Code: P12614 Credits: 10 Semester: Spring 2011/12 Programme classes: 12 1-2 hour lectures/workshops Aims: The module aims to describe and analyse the general finance topics and introduces students to implement basic computational approaches to financial problems using Microsoft Excel. It stresses...
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...EXECUTIVE SUMMARY INTRODUCTION/BACKGROUND The objective of the thesis is to predict and optimize the mechanical properties of Aircraft fuselage aluminium (AA5083). Firstly, data-driven modelling techniques such as Artificial Neural – Fuzzy networks and regressive analysis are used and by making the effective use of experimental data, FIS membership function parameters are trained. At the core, mathematical model that functionally relates tool rotational speed and forward movement per revolution to that of Yield strength, Ultimate strength and Weld quality are obtained. Also, simulations are performed, and the actual values are compared with the predicted values. Finally, multi-objective optimization of mechanical properties fuselage aluminium was undertaken using Genetic Algorithm to improve the performance of the tools industrially. AIMS AND OBJECTIVES Objectives of the dissertation include Understanding the basic principles of operation of Friction Stir Welding (FSW). Gaining experience in modelling and regressive analysis. Gaining expertise in MATLAB programming. Identifying the best strategy to achieve the yield strength, Ultimate Tensile strength and Weld quality of Friction Stir Welding. Performing optimization of mechanical properties of FSW using Genetic Algorithm. I To draw conclusions on prediction of mechanical properties of FSW optimization of aircraft fuselage aluminium. ACHIEVEMENTS The basic principles of friction welding of the welding...
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...A linear regression is worth a maximum of 5 points; a multiple regression is worth a maximum of 10 points and both are worth 13 points. You must analyze the regression(s) that you do. The better job that you do, for instance, checking the residuals and for multicollinearity, the more points you get. If you choose to do a linear regression then you must compare the list price to the square footage. You are to project out the listing price of a home that has 2750 square feet. If you choose to do the multiple regression then you must compare the list price to all the varables in the data file, square footage, number of bedroom, number of bathrooms, number of cars in the garage, and the age. You must project out the cost of a house with 2650 square feet; that is 10 years old, with 3 bed and 3 bathrooms with a garage that sleeps 3 cars. Linear Regression Scatter Plot StatTools Report | | | | | Analysis: | Scatterplot | | | | | Performed By: | | | | | | Date: | Tuesday, January 24, 2012 | | | | Updating: | Live | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |...
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...Forecasting Methods for Managers Multi-Variate Modelling including Lagged variables and Dummy Variables 2 Topics for Today • Multi-variate relationships • Correlation matrices • Doing a multiple regression in Excel • Multi-collinearity • Lagged variables • Dummy variables ▫ For modelling qualitative data ▫ For modelling seasonality 3 Multi-Variate Relationships • So far we have only looked at Time Series. These are where: . . . . one dependent variable, eg: sales, temperature . . . . varies with time • We have identified no underlying drivers of the relationship • We just made forecasts one or more periods ahead • These are commonly used business models . . . . but the business world is not that simple: ▫ The variables we need to forecast do not just depend on time ▫ Multi-variate models are required ▫ We can then identify the ‘levers’ to pull to ‘drive’ our variable 4 An Example In previous years this was a double module • Attendance at tutorials varied as the year progressed • Time is one factor but other factors could be: ▫ ▫ ▫ ▫ ▫ ▫ Students’ perception that the tutorial will help them pass Weather conditions: eg temperature on morning of tutorial Time of day for the tutorial (9am tutorials are not popular) Students dropping out of the module or the university Volume of background reading in the recommended texts Assignment marks achieved – low marks produce attendance 5 An Example How suitable are these...
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...Multiple Regression Model | Case # 28 House Prices | | A group of statistic student’s objective is to provide a business solution using statistical calculations and tools on a sample data. | | Upaiwan Porndumrongkit Ana Sanchez George Satiah Kritchapon Sopawatjirarich | 10/16/2010 | Executive Summary Summary: Home owners want to determine a reasonable asking price of a house based on a collection of home descriptions and its characteristics. However, home owners can get confused very easily as they see close variation in price based on different descriptive characteristics. Home owners hesitate to get professional advice from real estate agents due to service price. On the other hand, our Multi Regression Model can help real estate agents provide fast predictions and advice to home owners at a minimum service price. Our solution would give real estate agents a competitive advantage in the real estate market. Problem statement: How to determine a reasonable asking price on n number of houses with its descriptive characteristics. Solution: Multi Regression Model is one way to assist real estate agents use data to provide an estimate. Introduction The objective of this project is to provide the detailed data analysis and resolution to the problem statement mentioned above. As a group...
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...developing it. He or she must be must familiar with the critical success factors involved in selecting site locations, the process by which an international corporate strategy is formed, and the changes in the organization that will need to take place in order to implement it. Without a grasp of these concepts the operations manager will not be able to use the tools of operations management to help his or her firm establish a presence in new markets. How OM Techniques can help International Consultants to become better Managers The operations aspect of international consulting should start with a SWOT analysis, an examination of Strengths, Weaknesses, Opportunities, and Threats. The analysis should be done on the firm that is seeking to move operations abroad, the countries that the firm is considering expanding to, and firms competing for the same market around the world. An analysis of the target firm will reveal its core competencies, areas of vulnerability where breakdowns could occur, opportunities for expansion, and threats...
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...Introduction to Regression Model 3 1.1 Regression Model 3 1.2 Simple Linear Regression Model 3 1.3 Multiple Linear Regression Model 4 1.4 Multicollinearity 4 1.5 Conclusion for Multiple Linear Regression Modelling 5 2. Literature Review 5 2.1 Introduction of NBA 5 2.2 Reasons for High Average Salary in NBA 5 2.3 Salaries based on long term contract 6 2.4 Reasons for Choosing On-court Performance Data 7 3. Preparations for Running the Multiple Regression Model 8 3.1 Objectives of this paper 8 3.2 Introduction of the variables 8 3.2.1 Dependent Variable 8 3.2.2 Independent Variables 8 3.3 Data Source 9 3.4 Scattered Plots 9 3.4.1 Scattered Plots of Salary and Independent Variables 9 3.4.2 Residual Scattered Plots 10 4. Multiple linear regression modelling 11 4.1 The Adjusted R² 11 4.2 The Histogram 12 4.3 Model Generated by Analysis: 12 5. Ethical Problems 13 5.1 Sample Size 13 5.2 Data for Kobe Bryant 14 5.3 Excluded Related Independent Variables 14 5.4 Multi-collinearity 14 6. Conclusion 15 Abstract This paper examines the correlation between NBA players’ salaries and their on-court performance indicators. Before getting into the relationship, I would introduce the essence of what is regression model and how to interpret it, then we would move on to the introduction of NBA and how this model have been wildly used in testing the relationship between NBA players salaries and indicators. In the third part of this paper, regression models tell...
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...EMBA Advanced Market Analysis and Strategy - MAR 6087 SPSS Analysis PulteGroup, Inc. – South Florida Division By: Scott Mairn and Irina Prell November 30, 2012 Table of Contents I. Executive Summary II. Company Overview III. Research Objectives/Approach IV. Sampling Description V. Data Analysis and Interpretation VI. Implications VII. Conclusion and Future Direction Executive Summary This report will take a look the results from the Servqual survey research conducted for the PulteGroup, Inc. South Florida Division to analyze their service in September of 2012. The objective of this research is to assess how the 5 factors of the Servqual survey affect the homeowners’ attitude towards service in PulteGroup communities. Each homeowner was asked questions consistent with the intentional model of Servqual instruments which are designed to measure reliability, responsiveness, assurance, apathy, and tangibles of a service. SPSS software by IBM was used to analyze the results. The following methodologies were utilized: • Attitudinal Scores • Heavy Half Theory • T-test • Factor Analysis • Multiple Regression Analysis The third party management companies employed by PulteGroup in two communities, VillageWalk of Bonita Springs and VeronaWalk of Naples, delivered the survey to homeowners. We received 34 fully completed surveys, which were fully analyzed. The results showed a wide range in attitude with scores ranging from 125 to 725. The...
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...(2012) 67–77 Contents lists available at SciVerse ScienceDirect Knowledge-Based Systems journal homepage: www.elsevier.com/locate/knosys Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios Javier de Andrés ⇑, Manuel Landajo, Pedro Lorca University of Oviedo, Spain a r t i c l e i n f o a b s t r a c t In this paper we address the bankruptcy prediction problem and outline a procedure to improve the performance of standard classifiers. Our proposal replaces traditional indicators (accounting ratios) with the output of a so-called multinorm analysis. The deviations of each firm from a battery of industry norms (computed by nonparametric quantile regression) are used as input variables for the classifiers. The approach is applied to predict bankruptcy of firms, and tested on a representative data set of Spanish firms. Results indicate that the approach may provide significant improvements in predictive accuracy, both in linear and nonlinear classifiers. Ó 2011 Elsevier B.V. All rights reserved. Article history: Received 9 February 2011 Received in revised form 2 October 2011 Accepted 3 November 2011 Available online 30 December 2011 Keywords: Bankruptcy prediction Classification techniques Nonparametric methods Quantile regression Accounting ratios 1. Introduction Under the current economic conditions, bankruptcy early warning systems have become tools of key importance in order to guarantee the stability of the economy...
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