... 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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...Linear Regression and Correlation Chapter 13 McGraw-Hill/Irwin ©The McGraw-Hill Companies, Inc. 2008 GOALS Understand and interpret the terms dependent and independent variable. Calculate and interpret the coefficient of correlation, the coefficient of determination, and the standard error of estimate. Conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero. Calculate the least squares regression line. Construct and interpret confidence and prediction intervals for the dependent variable. 2 Regression Analysis - Introduction Recall in Chapter 4 the idea of showing the relationship between two variables with a scatter diagram was introduced. In that case we showed that, as the age of the buyer increased, the amount spent for the vehicle also increased. In this chapter we carry this idea further. Numerical measures to express the strength of relationship between two variables are developed. In addition, an equation is used to express the relationship. between variables, allowing us to estimate one variable on the basis of another. 3 Regression Analysis - Uses Some examples. Is there a relationship between the amount Healthtex spends per month on advertising and its sales in the month? Can we base an estimate of the cost to heat a home in January on the number of square feet in the home? Is there a relationship between the miles per gallon achieved by large...
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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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...Name_____________________________ 1. Volume-based plant-wide rates produce inaccurate product cost when: A) a large share of factory overhead cost is not volume-based. B) firms produce a diverse mix of product. C) different products consume different amounts of batch-level and product level costs. D) all of the above are correct. E) None of the answers above is correct. 2. As with any costing system, a firm should use activity-based costing (ABC) when the: A) currently used system doesn't seem to be working. B) benefits of such a system exceed the cost of implementation. C) firm wants more detailed information. D) firm's production growth exceeds 20 percent per year. E) current system has been in use for a long time. 3. One limitation of activity-based costing (ABC) is that: A) it is expensive even though it saves significant time. B) it is very time-consuming, even though it is equivalent in cost to other costing systems. C) it is both expensive and time-consuming. D) the system cannot be used in service industries. E) ABC has no significant limitations. 4. Elimination of low-value-added activities in a firm should: A) be discouraged because of potential harmful effects to customer value. B) reduce costs, while not significantly affecting the quality of the...
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...3 Ways to Improve Your Targeted Marketing with Analytics Introduction Targeted marketing is a simple concept, but a key element in a marketing strategy. The goal is to identify the potential customers who are most likely to buy what you are selling. Once you have identified this target group of people, you can focus your marketing efforts in their direction. This process could substantially decrease marketing costs. In this tutorial, you will use data from a Portuguese banking institution whose goal is to cut down on their telemarketing costs1. They want to call only the clients most likely to subscribe to a long-term deposit. By using modern data mining approaches (TreeNet, MARS, and RandomForests), you will build a model to successfully identify which customers to target and explore the characteristics of this market. Tutorial 1) Open SPM®: 1 [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014 2) Click the folder shortcut to open a data file: 3) Locate bank.csv (or your chosen file name) and click Open. The Activity Window, pictured below, will appear: This file contains 41,188 records of telemarketing calls at the Portuguese bank. On the left side of the Activity Window, you can see the variables in the data file. These include attributes of the prospect (i.e. age, marital status, education), economic indicators (i...
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...Business Statistics Topic: Correlation and Regression Recommended Readings: Lind D.A., Marchal W.G., and Wathen S.A. (2012), Statistical Techniques in Business and Economics, 15th International Ed., McGraw Hill [Chapter 13] Earlier edts are also suitable. Waters, D., (2008) Quantitative Methods for Business,4th Ed., Financial Times, Prentice Hall [Chapter 9] When we look at interval or ratio scale variables there is often a relationship, eg: price and quantity demanded; time spent studying and exam results obtained; gardai (police) on duty and number of crimes as well as alcohol consumed and sensibility! Regression and correlation analysis is useful because it allows us predict the value of one variable from the knowledge of another. The said relationship can be positive or negative. One first step in establishing if any of these relationships exist is to draw a scatter graph. A Scatter plot or diagram is a chart that portrays the relationship between the two variables. It is the usual first step in correlation analysis * The Dependent variable is the variable being predicted or estimated. * The Independent variable provides the basis for estimation. It is the predictor variable. Correlation Analysis From a scatter plot we have a first picture of the data. The next step is to calculate a measure which can assess the strength of that relationship. The correlation coefficient r which represents correlation in a sample is calculated as: r =...
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...6 Part 1.1 a) Advanced Analytics in Professional Standards 6 Part 1.1 b) Academic Research on Advanced Analytical 6 Part 1.3 Simple Trend-line Regression 7 Part 2.1 Specific Risk of Material Misstatement 11 Part 2.2 An Appropriate Audit Program 12 Appendix 15 References 16 List of Key Audlish terms 17 Partner Summary In order to better understand the audit reports, we have documented academic research and existing audit standards relevant to planning stage APRs. This background information will provide a summary of professional standards and guidance directly related to APRs. First and foremost, every auditor must follow the standards called the Generally Accepted Auditing Standards (GAAS) which are set by the Public Company Accounting Oversight Board (PCAOB). Important sections within the standards required to know include: Independence, Consideration of Fraud in a Financial Statement Audit, and Communications about Control Deficiencies in Financial Statements, which includes nine rules that deal with identifying and reporting deficiencies found in financial statements. In the second part of our report, we prepared basic ARP’s and identified some key red flags for the Chevron Company. To access client viability, we used vertical and horizontal analysis, where we found information to create concern about their financial strength. We also looked at other factors such as forecast cash flow for next year, ratios...
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... Date: November 4, 2012 Ye Olde FoodKing Company From: Re: Big Suzy’s Snack Cakes Regression Analysis Introduction The Regional Food Manager for Ye Olde FoodKing Company has retained Mark Craig of Blue Steel Consulting to perform a regression analysis to forecast demand of your product. The four characteristics readily available included price, competitors’ price, average income, and market population. The results of each regression analysis are presented at the end of this memo. The remainder of this memo describes the regression analysis used and limitations to the data available. Running a regression provides a statistical procedure to estimate the liner dependency of one or more independent variables on dependent variables. Confidence that the true (population) relation between X and Y lies between “Lower” and “Upper” Methodology * Form a theoretical model of the relationship under consideration. * This model suggests what explanatory variables to include in the analysis. * Collect data on independent and dependent variables. * Estimate regression line. * Examine results. This includes test of statistical significance and analysis of residuals. Regression Analysis Regression analysis is a statistical technique that examines the relation of a dependent variable to specified independent variables. The easiest form of regression analysis involves finding and understanding the straight-line relationship to illustrate the variation...
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...on that variable - and 2. the number of errors we would make if we knew the joint distribution overall and were told for each case the attribute o one variable each time we were asked to guess the attribute of the other. These measures include lambda, which is appropriate for the analysis of two nominal variables; gamma, which is appropriate for the analysis of two ordinal variables; and Pearson's product-moment correlation, which is appropriate for the analysis of two interval or ratio variables. • Regression analysis represents the relationships between variables in the form of equations, which can be used to predict the values of a dependent variable on the basis of values of one or more independent variables • Regression equations are computed on the basis of a regression line: the geometric line representing, with the least amount of discrepancy, the actual location of points in a scattergram. • Types of regression analysis include linear regression analysis, multiple regression analysis, partial regression analysis, and curvilinear regression analysis. Inferential Statistics • Inferential statistics are used to estimate the generalizability of findings arrived at through the analysis of a sampling to the larger...
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...measurements on earnings management analysis Contents Abstract 1. Introduction 2. Background and hypothesis 3. Literature review 4、Methodology 5. Sample selection and description 6. The test on the earnings management universality 7. The Impact of the Fair Value Application on Earnings Management 7.1 Correlation Analysis of the Main Variables 7.2 The Empirical Test of Linear Regression Equation 8. Limitation 9. Summary References Appendix I . Appendix II . Abstract The impact of fair value standard application towards enterprise is far-reaching and important, mainly in measurement methods of the changes on assets and liabilities way. This shift of fair value measurement method helps business executives for some sort of motive or target to conduct earnings management, thereby artificially adjusted profits. The authors focus on the study of earnings management in the listed Corporation under the fair value model during the financial crisis. We investigate the correlation between the application of fair value accounting and earnings management for providing the policy basis to improve fair value accounting and standardizing corporate earnings management behavior. We collect the sample data by selecting the Chinese A-share listed companies, and use the modified Jones model as a tool to prove the listed companies in the existence of widespread earnings management behavior. Descriptive analysis and regression analysis also could be to verify the fair...
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...a p p e n d i x C DEMAND FORECASTING IN MARKETING * When you finish this appendix you should • Understand the principles of forecasting. • Know the differences between Time Series and Regression Analyses. • Understand how expert opinion, sales forces and consumer opinions can all contribute to better forecasts. * by Beverley Thompson, The University of Western Sydney, Nepean Demand Forecasting in Mar keting w 689 An important part of the marketing planning process is the setting of goals that are realistic and achievable, given a particular marketing environment and level of marketing commitment. In marketing, such goals are usually based on market share objectives and sales targets, both of which require accurate forecasts of total market size, market size of target segments and likely market share within a targeted segment. W H AT A R E W E F O R E C A S T I N G ? Accurate forecasting requires a clear definition of the market in question. Markets may be differentiated on the basis of the following variables. GEOGRAPHY A market may be defined at world, country, state, region, sales territory, town, store or customer level. When formulating a forecast or other marketing plans, the geographical dimension must be clearly indicated. Planning Coca Cola consumption for the year 2000 Sydney Olympics for example, will necessitate the forecasting of increased consumption for the Sydney sales region, but not necessarily for Brisbane...
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...Introduction 3 2. Executive Summary 5 3. Data Preprocessing 6 4. Analysis 8 4.1 Data Partition 8 4.2 Stat Explore 10 4.3 Clustering & Segmentation: 12 4.4 Decision Tree 24 4.5 Interactive Decision Tree 28 4.6 Gradient Boosting 33 4.7 Linear Regression 35 4.8 Neural Network 38 4.9 Compare Models 40 4.10 Score New Data 42 4.11 Logistic Regression 44 5. Conclusion 49 1. Introduction Given the complexity and the large extent of the interdependencies between airports, aircraft, passengers, airlines, control centers, etc. of the national aviation system, flight delays occur frequently. Recently, the OAG Flight Status database reported that over 4.6 million flights arrived more than 15 minutes late at their destination; a conservative average of 80 passengers per flight equates to about 368 million passengers being inconvenienced. Inspired by this, our term paper predicts the delay in flights of some of the players in the U.S. Airlines Industry and the impact of the flight delays to improve their performance (July 2014 to June 2015). DATA SOURCE The secondary data source we will use is available under Airline On-time Statistics on the TranStats website of the Bureau of Transportation Statistics as our source of data. We extracted information for specific carriers from all major airports during a particular time period. For the purpose of our analysis, we restricted the scope of our study to focus on the arrivals in time period...
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...Quantitative Methods II ©2006 Prentice Hall Lecture 12 14-12-2014 Chapter 13 Linear Correlation and Regression ©2006 Prentice Hall Intended Learning Outcomes (ILOs) • By the end of this lecture, the student should be able to: Understand and explain the terms dependent and independent variable Calculate and interpret the correlation coefficient , the coefficient of determination, and the standard error of estimate Calculate the least squares regression line Construct and interpret confidence and prediction intervals for the dependent variable ©2006 Prentice Hall • In this chapter, we will develop numerical measures to express the relationship between two variables. Is the relationship strong or weak, is it direct or inverse? In addition, we will develop an equation to express the relationship between variables. Then, we will estimate one variable on the basis of another. • - Examples: Is there a relationship between the number of hours that student studies for an exam and the score earned? - Is there a relationship between years of employee experience and the quantity of production? - Is there a relationship between the product price and the purchasing amount of that product? - Is there a relationship between the amount of money spend per month on advertising and the monthly sales? - Is there a relationship between age and blood pressure ? Can we estimate, based on the amount of money spent on advertising in January,...
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...Abstract: Purpose – To determine the factors that explain customer satisfaction in the full service restaurant industry. Design/methodology/approach – Secondary research and qualitative interviews were used to build the model of customer satisfaction. A structured questionnaire was employed to gather data and test the model. Sampling involved a random selection of addresses from the telephone book and was supplemented by respondents selected on the basis of judgment sampling. Factor analysis and multiple regression were used to test the model. Findings – The regression model suggested that customer satisfaction was influenced most by responsiveness of the frontline employees, followed by price and food quality (in that order). Physical design and appearance of the restaurant did not have a significant effect. Research limitations/implications – To explain customer satisfaction better, it may be important to look at additional factors or seek better measures of the constructs. For example, the measures of food quality may not have captured the complexity and variety of this construct. It may also be important to address the issue of why customers visit restaurants. Instead of the meal, business transactions or enjoying the cherished company of others may be more important. Under the circumstances, customer satisfaction factors may be different. The results are also not generalizable as the sampled area may have different requirements from restaurants. Practical implications...
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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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