...Write a brief paper on Statistics and Operations Research in the context of Analytics. Analytics is defined as the scientific process of transforming data into insight for making better decisions. It is the combination of skills, technologies, applications and processes used by data scientist to gain insight in to their business based on data and statistics to drive business planning. It typically use’s data, statistical and quantitative analysis to measure the performance of the subject (Organization/ website etc.) on which a study is to be conducted. Analytics can be used in various fields such as market research, for studying user web pattern behavior & in many other applications to derive some meaningful information out of the complex world around us. But we will first touch upon the area of operations research & how Analytics with the help of various statistical tools can help to solve the operation related problem in an organization. Operations research overlaps with other disciplines, such as industrial engineering and operations management. It is often concerned with determining an optimal solution out of a business problem. It may either to maximize your profit, performance, or yield or minimize your losses, risk, or cost. In the fields of production, logistics, or sales where managers are facing a problems so as how to allocate resources, develop production schedules, manage the supply chain, and set prices. For example, it many help to decide how to organize...
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...EDITORIAL Analyzing and Interpreting Data From Likert-Type Scales L ikert-type scales are frequently used in medical education and medical education research. Common uses include end-of-rotation trainee feedback, faculty evaluations of trainees, and assessment of performance after an educational intervention. A sizable percentage of the educational research manuscripts submitted to the Journal of Graduate Medical Education employ a Likert scale for part or all of the outcome assessments. Thus, understanding the interpretation and analysis of data derived from Likert scales is imperative for those working in medical education and education research. The goal of this article is to provide readers who do not have extensive statistics background with the basics needed to understand these concepts. Developed in 1932 by Rensis Likert1 to measure attitudes, the typical Likert scale is a 5- or 7-point ordinal scale used by respondents to rate the degree to which they agree or disagree with a statement (T A B L E). In an ordinal scale, responses can be rated or ranked, but the distance between responses is not measurable. Thus, the differences between ‘‘always,’’ ‘‘often,’’ and ‘‘sometimes’’ on a frequency response Likert scale are not necessarily equal. In other words, one cannot assume that the difference between responses is equidistant even though the numbers assigned to those responses are. This is in contrast to interval data, in which the difference...
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...Assignment 8a PSYC340 Research Methods I 1. ______ statistics are used in the process of hypothesis testing. a. Descriptive b. Null c. Alternative d. inferential 2. Inferential statistics allow us to: a. infer something about the sample based on the population. b. infer something about the population based on the sample c. infer that the sample is representative d. do all the above 3. No effect is to _____ hypothesis as effect is to _____ hypothesis. a. null; alternative b. alternative; null c. one-tailed; two-tailed d. two-tailed; one tailed 4. Ho is to Ha as ______ hypothesis is to ______ hypothesis. a. null; alternative b. alternative; null c. one-tailed; two-tailed d. two-tailed; one tailed 5. One and two-tailed hypotheses are both types of ______ hypotheses. a. null b. alternative c. directional d. non-directional 6. When using a ____ hypothesis, the researcher predicts the direction of the expected difference between the groups. a. null b. non-directional c. one-tailed d. two-tailed 7. A false alarm is to ____ as a miss is to _____. a. Type I error; Type II error b. Type II error; Type I error c. null hypothesis; alternative hypothesis d. alternative hypothesis; null hypothesis 8. Failing to reject Ho when we should have rejected it is a ____ error. a. Type I b. Type II c. null d. one-tailed 9. If researchers report that the results from their study were significant, p < .05, this...
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...Overview: This module is taught in lectures and practical classes. This includes sessions on quantitative methods and an appropriate statistical package. Module aims: This module aims to give students a broad knowledge of a range of methodological and analytical skills, which they can apply in a variety of research contexts. As well as giving students a grounding in the basic principles of quantitative research methodology, the module will (i) look at how data can be described, (ii) introduce a range of statistical tests commonly used, and (iii) explore what the results mean in terms of the research question posed. The module comprises lectures, and the delivery will involve hands-on lab work using an appropriate statistical package. Module Objective Learning Outcomes: On completion of this module, students should be able to demonstrate: • An ability to apply a variety of different statistical methods to their research project. • An ability to collect, store, manage, test and interpret data. • An understanding of the epistemological and methodological difficulties associated with different techniques. • An ability to use computer software associated with the analysis of quantitative data. • An awareness of the ethical issues surrounding data collection and analysis. Skills Taught: On completion of this module, students should be able to; • Conduct research in a manner that is consistent with professional practice • Collect quantitative data • Enter quantitative...
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................................................................................................... 5 2.1. 2.2. 2.3. Mergers and acquisitions activity in recent years .................................................. 5 Overview of efficient market hypothesis .............................................................. 7 Abnormal operating performance ......................................................................... 9 3. Event study approach ............................................................................................... 12 3.1. 3.2. Event study technique ........................................................................................ 12 Test statistics ..................................................................................................... 14 Parametric tests...
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...A Stochastic Approach to Indian Banking Sector : Technical Analysis of Private Sector Banks Dr. Rahul Rajan Abhilasha Srivastava Abstract The objective of this paper is to present a method for estimating the cost efficiency of Indian banks in order to study the degree of technical and cost performance of the Indian banking sector and to analyze how the banking sector has been affected by technical efficiency and cost efficiency. Initially, the evolution in the technical front in the banks between 2005 to 2012 is measured. For this analysis purpose a sample of 101 Indian banks including 28 public ,29 private and 44 foreign banks operating in India is taken for the period 2005-2012. For analysis purpose both the parametric method of productive efficiency frontier (Stochastic Frontier Analysis ) and nonparametric method (Data Envelopment Analysis) are taken. Introduction The financial system is the lifeline of the economy. The changes in the economy get mirrored in the performance of the financial system, more so of the banking industry. The banking system in India is significantly different from that of other Asian nations because of the country’s unique geographic, social, and economic characteristics. India has a large population and land size, a diverse culture, and extreme disparities in income, which are marked among its regions. There are high levels of illiteracy among a large percentage of its population but, at the same time, the country has a large reservoir...
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...Data Science 2(2004), 329-346 Estimating Optimal Transformations for Multiple Regression Using the ACE Algorithm Duolao Wang1 and Michael Murphy2 School of Hygiene and Tropical Medicine and 2 London School of Economics 1 London Abstract: This paper introduces the alternating conditional expectation (ACE) algorithm of Breiman and Friedman (1985) for estimating the transformations of a response and a set of predictor variables in multiple regression that produce the maximum linear effect between the (transformed) independent variables and the (transformed) response variable. These transformations can give the data analyst insight into the relationships between these variables so that relationship between them can be best described and non-linear relationships can be uncovered. The power and usefulness of ACE guided transformation in multivariate analysis are illustrated using a simulated data set as well as a real data set. The results from these examples clearly demonstrate that ACE is able to identify the correct functional forms, to reveal more accurate relationships, and to improve the model fit considerably compared to the conventional linear model. Key words: Alternating conditional expectation (ACE) algorithm, nonparametric regression, transformation. 1. Introduction In regression analysis, we try to explain the effect of one or more independent variables (predictors or covariates) on a dependent variable (response). The initial stages of data analysis often involve...
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...Bootstrapping (statistics) From Wikipedia, the free encyclopedia In statistics, bootstrapping is a method for assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates.[1][2] This technique allows estimation of the sampling distribution of almost any statistic using only very simple methods.[3][4] Generally, it falls in the broader class of resampling methods. Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples of the observed dataset (and of equal size to the observed dataset), each of which is obtained by random sampling with replacement from the original dataset. It may also be used for constructing hypothesis tests. It is often used as an alternative to inference based on parametric assumptions when those assumptions are in doubt, or where parametric inference is impossible or requires very complicated formulas for the calculation of standard errors. Contents * 1 History * 2 Approach * 3 Situations where bootstrapping is useful * 4 Discussion * 4.1 Advantages...
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...RESEARCH METHODOLOGY & INFORMATION – STUDY NOTES Definitions: 1. BRAINSTORMING 2. CASESTUDY 3. CLUSTER SAMPLING 4. CONVENIENCE SAMPLING 5. DESCRIPTIVE RESEARCH 6. DESCRIPTIVE DATA 7. EXPLORATORY RESEARCH 8. FOLLOW-UP 9. LONGITUDINAL STUDY 10. NON PARAMETRIC STATISTIC 11. NON-PROBABILITY SAMPLING 12. PARAMETRIC STATISTIC 13. POPULATION 14. PILOT TEST 15. PURPOSIVE SAMPLING 16. PROBABILITY SAMPLING 17. QUOTA SAMPLING 18. RESEARCH ETHICS 19. RESEARCH OBJECTIVES 20. SAMPLING FRAME 21. SEMANTIC DIFFERENTIAL RATING SCALE 22. SIGNIFICANCE TESTING 23. SIMPLE RANDOM SAMPLING & SNOWBALL SAMPLING 24. STRATIFIED RANDOM SAMPLING 25. SYSTEMATIC SAMPLING 26. CRITICAL VALUES: The value of the standard statistic (z or t) beyond which we reject the null hypothesis. It is the boundary between the acceptance & rejection regions. Eg. For a 2 tailed hypothesis test being tested at α = 5% the critical values are ± 1.96. 27. TYPE I ERROR (OR) LEVEL OF SIGNIFICANCE (OR) PRODUCER’S RISK (α): A value indicating the % of sample values that are outside certain limits, assuming that the null hypothesis is true. It is the probability of rejecting the null hypothesis when it is true. Eg: α=5% indicates that 5 times out of 100 the null hypothesis gets rejected when it is true. 28. TYPE II ERROR (OR) CONSUMER’S RISK (β): It is the probability...
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...Apulensis Series Oeconomica, 15(2), 2013, 594-604 ANALYSIS OF THE ROMANIAN BANKS’ PERFORMANCE THROUGH ROA, ROE AND NON-PERFORMING LOANS MODELS Adela Socol1 Adina Elena Dănuleţiu2 ABSTRACT: General assumption of this study is that the banks performance represents their ability to generate sustainable profitability and that banks’ profitability is one important issue of contemporary banking field, grace to its role in emphasizing of the financial soundness of banks, abreast to others indicators regarding to the capital adequacy or assets quality. The paper examines how is affected banking profitability (expressed through traditional measures of performance ROA - Return on Assets and ROE - Return on Equity) by the CRR Credit risk ratio in Romanian banking system during March 2008 - June 2013. We developed two regression models in order to study the dependence between mentioned variables. We found that ROA and ROE vary each of them depending on the CRR Credit risk ratio, which is expressed as the ratio of gross value of exposure to loans and related interest under “doubtful” and “loss” to total classified loans and related interest pertaining to non-bank loans, off-balance sheet items excluded. Keywords: ROA Return on Assets, ROE - Return on Equity, Credit risk ratio, Regression model JEL Codes: D12, C58, G21 Introduction Significant level of non-performing loans and faster cross-border deleveraging are considered the major weaknesses of the Romanian banking sector...
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...Statistics Darrell Washington BUS308: Statistics for Managers Instructor: Gary Withers November 23, 2015 Statistics Introduction Data analysis is done to convert the raw data into meaningful information. Statistics is all about this data analysis. Before actually working on data analysis in real world, it is important that the basic knowledge is possessed by everybody involved in this process. The following paper seeks to provide basic knowledge in some specific areas of statistical data analysis. Descriptive statistics As the name suggests, descriptive statistics means analysis of data to describe or summarize data in a meaningful manner. The analysis helps in determination of patterns that exist in the data. Descriptive statistics simply describes the data but not present an analysis beyond the data. One cannot make any conclusions about any hypothesis using this. The data, when presented to users, is difficult to comprehend in its raw form. Sometimes the sheer size and volume of data may render it impossible to understand what it represents. Descriptive statistics helps in proper presentation of data such that it can be understood and analyzed by the user. For example, if we had the data about the returns from stocks of 100 companies, then that data would make no sense as it would be difficult to comprehend. But if that data is arranges, classified industry-wise, an average is calculated for each industry and for all the stocks, and then the interpretation would...
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...Module 1 The Research Process * Is a scholarly activity aimed at finding new truths about a specific discipline basically designed to find solution to a problem. * Done in various fields of studies. * May be done in natural sciences like Biology, Chemistry and Physics. And in the field of social sciences such as Sociology, Psychology, Education, Anthropology, Industry and the like. Definitions of Research * a systematic patient study and investigation in some field of knowledge undertaken to discover or establish facts or principles (Webster’s New World Dictionary, 1994) * a systematic investigation in order to establish facts and new conclusions (Oxford, 1996) Definitions of Research * a systematic and objective analysis and recording of controlled observations that may lead to the development of generalizations, principles or theories resulting in prediction and possibly ultimate control of events (Best & Kahn, 1998) * purposive, systematic and scientific process of gathering, analyzing, classifying, organizing, presenting and interpreting data for the solution of a problem, for prediction, for invention, for the discovery of truth, or for the expansion or verification of existing knowledge, all for the preservation and improvement of the quality of life (Calderon, 2000) * a process of systematically examining and explaining the observables. It seeks to generate answers to questions but it also generates further questions for study....
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...and the living of the people as well. Moreover, these type of financial institutions are providing world class banking services to the people of that region. Influenced by the success of these financial institutions several other local institutions are furnishing themselves for providing better services to the clients and getting market share from their counterparts (Kumbhakar, Lozano-Vivas, Lovell, & Hasan, 1999). It is worth denying to say that the standards that are being followed by the foreign financial institutions are setting example to the local companies as well. Inspired to this several local companies are now flourished in this region (e.g. HSBC, Commercial Bank of Ceylon of Srilanka, ICBC of China etc.). Objectives The research if going to analyze the performance of the financial institutions of the Asia pacific region. The main objectives...
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...Stochastic frontier analysis of the efficiency of Nigerian banks Abstract Using the Stochastic Frontier Analysis (SFA) the efficiency of Nigerian banks was analysed. The result of the study proved that there is inefficiency in the Nigerian banking system and that the level of inefficiency ranged from 0 to 19 per cent of total cost. The study was able to derive the individual bank's level of inefficiency. Put differently, the study was able to derive the individual bank's level of efficiency. I. INTRODUCTION In the last three decades, as bank regulators open their financial Industries for competition and liberalisation, many banks operated at a level that is less efficient and profitable leading to unsoundness or distress in the industry; thus generating concerns and worries among the bank stakeholders. There are a large number of studies which employ models to explain inter-bank differences in earnings, bank efficiency and continuous existence (failure) in the United States of America and other developed countries of the world. Similar studies have not been carried out using data from emerging markets like Nigeria especially when viewed against the background of the statement of Barltrop and McNaughton (1992) that financial analysis should be done within the context of the particular country and economic environment as each country has a different economic environment, different regulatory and legal environment, different commercial practices, different accounting...
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...Statistical Methods This paper talks about statistical methods. Statistical data indicates that the agency 's approach is characterized by its population by inference Presented from a representative sample of the population views. As scientists rarely observed throughout Crowd, sampling and statistical inference is essential. This paper discusses some of the general principles Visualization of planning experiments and data. Then, a strong focus on the appropriate choice Standard statistical models and statistical inference methods. First of all, the Standard Model described. These models, in order to apply interval estimation and hypothesis testing parameters Also described, including the next two sample cases, when the purpose of comparing two or more of the population For their means and variances. Secondly, non-parametric inference tests are also described in the case where the data Sample distribution is not compatible with standard parameter distribution. Thirdly, using multiple resampling methods Computer -generated random sample finally introduced the characteristics of the distribution and estimate Statistical inference. The method of multivariate data processing of the following sections involved. method Clinical trials also briefly review process. Finally, the last section of statistical computer software discussion And through the collection of citations to adapt to different levels of expertise, and to guide readers theme. In this article, there are some methods such...
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