The III connects research about inequality from across the LSE. Here you can find published research exploring inequality from leading academics across the school. Search by keyword by using your browser's 'find in. Handbook of Crystal Growth, 2nd Edition. General Preface; Preface to Volume II; List of Contributors; Part A. Crystal Growth in Geology: Patterns on the Rocks. Geological Scenarios. These characteristics of human capital prompted Harlan Cleveland, former President of the World Academy of Art and Science, to observe that “the only limits. Arsham's Statistics Site. Statistical Thinking for Managerial Decisions. Para mis visitantes del mundo de habla hispana, este sitio se encuentra disponible en espa. It contains various useful concepts and topics at many levels of learning statistics for decision making under uncertainties. The cardinal objective for this Web site is to increase the extent to which statistical thinking is merged with managerial thinking for good decision making under uncertainty. Enter a word or phrase in the dialogue box, e. If the first appearance of the word/phrase is not what you are looking for, try Find Next. Towards Statistical Thinking for Decision Making. Introduction. The Birth of Probability and Statistics. Statistical Modeling for Decision- Making under Uncertainties. Statistical Decision- Making Process. What is Business Statistics? Common Statistical Terminology with Applications. Descriptive Sampling Data Analysis. Greek Letters Commonly Used in Statistics. Type of Data and Levels of Measurement. Why Statistical Sampling? Sampling Methods. Representative of a Sample: Measures of Central Tendency. Selecting Among the Mean, Median, and Mode. Specialized Averages: The Geometric & Harmonic Means. Histogramming: Checking for Homogeneity of Population. How to Construct a Box. Common Statistical Terminology with Applications Like all profession, also statisticians have their own keywords and phrases to ease a precise communication. However, one must interpret the results of any decision making in a. Back to top Program aims. The Bachelor of Education program aims to graduate students who meet the academic requirements for registration as a teacher in Queensland and who demonstrate: the knowledge, skills. 900S08001 E-Discovery: Region 4 Resources and Materials. 906K99003 E-hazards: They're Out There. 735B08001 E-submission XML Guidance Document Version 1.1. 420N10002 E-update April 2010 Earth Day 2010: EPA SmartWay. Pro Team High Quality Cycling Apparel at Low Prices. Free USA Shipping on Orders over $90. Official Team Cycling Jerseys & Bibs Made In Italy. A free financial dictionary with thousands of terms and buzz words in all areas of business, investing, and finance. Plot. Measuring the Quality of a Sample. Selecting Among the Measures of Dispersion. Shape of a Distribution Function: The Skewness- Kurtosis Chart. A Numerical Example & Discussions. The Two Statistical Representations of a Population. Empirical (i. e., observed) Cumulative Distribution Function. Probability as a Confidence Measuring Tool for Statistical Inference. Introduction. Probability, Chance, Likelihood, and Odds. How to Assign Probabilities. General Computational Probability Rules. Combinatorial Math: How to Count Without Counting. Joint Probability and Statistics. Mutually Exclusive versus Independent Events. What Is so Important About the Normal Distributions? Footnotes ^ Not all specialisations within the Bachelor of Education are made available every year in on-campus mode at all three campuses. Comprehensive and meticulously documented facts about energy. Learn about the science of energy, the pros and cons of different energy technologies, public policies, and more. What Is a Sampling Distribution? What Is The Central Limit Theorem (CLT)? An Illustration of CLTWhat Is. Distribution- free Tests. Hypotheses Testing for Means and Proportions. Introduction. Single Population t- Test. Two Independent Populations. Non- parametric Multiple Comparison Procedures. The Before- and- After Test. ANOVA for Normal but Condensed Data Sets. ANOVA for Dependent Populations. Tests for Statistical Equality of Two or More Populations. Introduction. Equality of Two Normal Populations. Testing a Shift in Normal Populations. Analysis of Variance (ANOVA)Equality of Proportions in Several Populations. Distribution- free Equality of Two Populations. Comparison of Two Random Variables. Applications of the Chi- square Statistic. Introduction. Test for Crosstable Relationship. Crosstable Analysis. Identical Populations Test for Crosstable Data. Test for Equality of Several Population Proportions. Test for Equality of Several Population Medians. Goodness- of- Fit Test for Probability Mass Functions. Compatibility of Multi- Counts. Necessary Conditions in Applying the Above Tests. Testing the Variance: Is the Quality that Good? Testing the Equality of Multi- Variances. Correlation Coefficients Testing. Regression Modeling and Analysis. Simple Linear Regression: Computational Aspects. Regression Modeling and Analysis. Regression Modeling Selection Process. Covariance and Correlation. Pearson, Spearman, and Point- biserial Correlations. Correlation, and Level of Significance. Independence vs. Correlated. How to Compare Two Correlation Coefficients. Conditions and the Check- list for Linear Models. Analysis of Covariance: Comparing the Slopes. Residential Properties Appraisal Application. Unified Views of Statistical Decision Technologies. Introduction. Hypothesis Testing with Confidence. Regression Analysis, ANOVA, and Chi- square Test. Regression Analysis, ANOVA, T- test, and Coefficient of Determination. Relationships among Popular Distibutions. Index Numbers and Ratios with Applications. Introduction. Consumer Price Index. Ratio Indexes. Composite Index Numbers Variation Index as a Quality Indicator. Labor Force Unemployment Index. Seasonal Index and Deseasonalizing Data. Human Ideal Weight: The Body Mass Index. Statistical Technique and Index Numbers. Introduction to Statistical Thinking for Decision Making. This site builds up the basic ideas of business statistics systematically and correctly. It is a combination of lectures and computer- based practice, joining theory firmly with practice. It introduces techniques for summarizing and presenting data, estimation, confidence intervals and hypothesis testing. The presentation focuses more on understanding of key concepts and statistical thinking, and less on formulas and calculations, which can now be done on small computers through user- friendly Statistical Java. Script A, etc. In all aspects of our lives, and importantly in the business context, an amazing diversity of data is available for inspection and analytical insight. Statistical concepts and statistical thinking enable them to. Materials in this Web site are tailored to help you make better decisions and to get you thinking statistically. A cardinal objective for this Web site is to embed statistical thinking into managers, who must often decide with little information. They must facilitate a process of never- ending improvement at all stages of manufacturing and service. This is a strategy that employs statistical methods, particularly statistically designed experiments, and produces processes that provide high yield and products that seldom fail. Moreover, it facilitates development of robust products that are insensitive to changes in the environment and internal component variation. Carefully planned statistical studies remove hindrances to high quality and productivity at every stage of production. This saves time and money. It is well recognized that quality must be engineered into products as early as possible in the design process. One must know how to use carefully. Business Statistics is a science assisting you to make business decisions under uncertainties based on some numerical and measurable scales. Decision making processes must be based on data, not on personal opinion nor on belief. The Devil is in the Deviations: Variation is inevitable in life! Every process, every measurement, every sample has variation. Managers need to understand variation for two key reasons. First, so that they can lead others to apply statistical thinking in day- to- day activities and secondly, to apply the concept for the purpose of continuous improvement. This course will provide you with hands- on experience to promote the use of statistical thinking and techniques to apply them to make educated decisions, whenever you encounter variation in business data. You will learn techniques to intelligently assess and manage the risks inherent in decision- making. Therefore, remember that: Just like weather, if you cannot control something, you should learn how to measure and analyze it, in order to predict it. Their deficiencies lead students to develop phobias for the sweet science of statistics. In this respect, Professor Herman Chernoff (1. This means that these people are extremely busy. For my teaching philosophy statements, you may like to visit the Web site On Learning & Teaching. One speaks in statistical jargon; the other understands the monetary or utilitarian benefit of using the statistician's recommendations. Plugging numbers into the formulas and crunching them have no value by themselves. You should continue to put effort into the concepts and concentrate on interpreting the results. For example, in computing the variance, consider its formula. Why do we square the deviations from the mean. Because, if we add up all deviations, we get always zero value. So, to deal with this problem, we square the deviations. Why not raise to the power of four (three will not work)? Squaring does the trick; why should we make life more complicated than it is? Notice also that squaring also magnifies the deviations; therefore it works to our advantage to measure the quality of the data. Why is there a summation notation in the formula. To add up the squared deviation of each data point to compute the total sum of squared deviations. Why do we divide the sum of squares by n- 1. The amount of deviation should reflect also how large the sample is; so we must bring in the sample size. That is, in general, larger sample sizes have larger sum of square deviation from the mean. The reason for n- 1 is that when you divide by n- 1, the sample's variance provides an estimated variance much closer to the population variance, than when you divide by n. You note that for large sample size n (say over 3. The results are almost the same, and they are acceptable. The factor n- 1 is what we consider as the. In fact, when you try to understand the formulas, you do not need to remember them, they are part of your brain connectivity. Clear thinking is always more important than the ability to do arithmetic. As you used to do experiments in physics labs to learn physics, computer- assisted learning enables you to use any online interactive tool available on the Internet to perform experiments. The purpose is the same; i. The appearance of computer software, Java. Script, Statistical Demonstration Applets, and Online Computation are the most important events in the process of teaching and learning concepts in model- based, statistical decision making courses. These e- lab Technologies allow you to construct numerical examples to understand the concepts, and to find their significance for yourself. The way the instructors attempt to help their students acquire skills and knowledge has absolutely nothing to do with the way students actually learn. Many instructors rely on lectures and tests, and memorization. All too often, they rely on. Certainly, we learn by doing, failing, and practicing until we do it right. The computer assisted learning serves this purpose. Professionals with strong quantitative skills are in demand. Three theories of banking and the conclusive evidence. Highlights? The financial crisis has heightened awareness that these questions have been unduly neglected by many researchers. During the past century, three different theories of banking were dominant at different times: (1) The currently prevalent financial intermediation theory of banking says that banks collect deposits and then lend these out, just like other non- bank financial intermediaries. The theories differ in their accounting treatment of bank lending as well as in their policy implications. Since according to the dominant financial intermediation theory banks are virtually identical with other non- bank financial intermediaries, they are not usually included in the economic models used in economics or by central bankers. Moreover, the theory of banks as intermediaries provides the rationale for capital adequacy- based bank regulation. Should this theory not be correct, currently prevailing economics modelling and policy- making would be without empirical foundation. Despite the importance of this question, so far only one empirical test of the three theories has been reported in learned journals. This paper presents a second empirical test, using an alternative methodology, which allows control for all other factors. The financial intermediation and the fractional reserve theories of banking are rejected by the evidence. This finding throws doubt on the rationale for regulating bank capital adequacy to avoid banking crises, as the case study of Credit Suisse during the crisis illustrates. The finding indicates that advice to encourage developing countries to borrow from abroad is misguided. The question is considered why the economics profession has failed over most of the past century to make any progress concerning knowledge of the monetary system, and why it instead moved ever further away from the truth as already recognised by the credit creation theory well over a century ago. The role of conflicts of interest and interested parties in shaping the current bank- free academic consensus is discussed. A number of avenues for needed further research are indicated. JEL classification. Keywords. Bank accounting; Bank credit; Credit creation; Economics; Financial intermediation; Foreign borrowing; Fractional reserve banking; Money creation. Introduction. The failure by leading economists to incorporate banking in their economic theories has been identified as a significant and costly weakness (Werner, Richard A., 1. Werner, Richard A., 2. Kohn, Donald, 2. 00. Likewise, it has been pointed out that the macro- economic feedback of banking activity had been neglected in finance research (Werner, 2. Recognition of these shortcomings has led to the emergence of . The present paper contributes to this growing literature by addressing a long- standing central dispute about the role and function of banks, which has major implications for monetary and macroeconomics, finance and banking, as well as government policy: it is the question whether a bank lends existing money or newly creates the money it lends. As Werner (2. 01. The oldest, the credit creation theory of banking, maintains that each bank can individually create money . The fractional reserve theory states that only the banking system as a whole can collectively create money, while each individual bank is a mere financial intermediary, gathering deposits and lending these out. The financial intermediation theory considers banks as financial intermediaries both individually and collectively, rendering them indistinguishable from other non- bank financial institutions in their behaviour, especially concerning the deposit and lending businesses, being unable to create money individually or collectively. Although various economists support each of the three theories, and despite the pivotal significance for research and policy, the question which of the three theories is accurate has until recently not been empirically examined. The first empirical test published in a learned journal on this issue was Werner (2. It was found that only the credit creation theory was consistent with the observed empirical evidence. As a result, during the observation interval of one day, other transactions took place in addition to the test transaction. While the final results of the test were unambiguous, a number of aggregated uncontrolled factors had to be jointly evaluated. Therefore as a robustness check it would be desirable to test the three theories of banking using a different methodology, in a fully controlled environment, without the potential interference from other transactions. The main contribution of the present paper is to provide such an alternative empirical test, allowing complete control of all other factors. For this purpose, use is made of the fact that modern banking and its constituent accounting operations take place entirely within the IT systems of banks. In this paper a controlled test design is proposed that uses the relevant banking software to simulate a bank loan transaction and booking it as if it was a real transaction. While humans may change their behaviour in such simulation situations when they become aware of the nature of the test, such potential bias does not apply to software code. The test of booking a bank loan in banking software yields the finding that the credit creation theory of banking alone conforms to the empirical facts, providing a separate and different corroboration of the findings in Werner (2. The results from the test on bank lending are used to throw new light on capital adequacy- based bank regulation (such as the Basel III/CRR approach) and its alleged ability to prevent banking crises, illustrated through the case of the capital raising by Swiss bank Credit Suisse in 2. It is found that capital adequacy- based bank regulation cannot prevent banking crises. Instead, it is noted that central bank guidance of bank credit and banking systems dominated by small banks have a superior track record in generating stable growth without crises. Furthermore, the question is asked why the economics profession has singularly failed over most of the past century to make any progress in terms of knowledge of the monetary system, and instead moved ever further away from the truth as already recognised by the credit creation theory well over a century ago. The role of conflicts of interest is discussed and a number of avenues for needed further research are indicated. The paper is structured as follows: The second section will briefly survey the literature on the three theories of banking and their differing accounting implications. Section 3 presents the new empirical test. Section 4 analyses and interprets the results. Section 5 applies the insights to examining capital adequacy- based bank regulation, considering the case of Credit Suisse. Section 6 discusses the implications for development policies, and specifically, the advice for developing countries to borrow from abroad in order to stimulate economic growth. Section 7 considers the failure by academic and central bank economists to make progress for a century concerning the role of banks. Closing words are recorded in Section 8. A brief overview of the three main theories of banking and their accounting. Like Werner (2. 01. With a few exceptions, the citations differ from those in Werner (2. Several authors of the . The financial intermediation theory of banking. The presently dominant financial intermediation theory holds that banks are merely financial intermediaries, not different from other non- bank financial institutions: they gather deposits and lend these out ( Fig. In the words of recent authors, “Banks create liquidity by borrowing short and lending long” (Dewatripont, Rochet, & Tirole, 2.
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