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Krishnan Correlation CUSB.Pdf
SELF INSTRUCTIONAL STUDY MATERIAL PROGRAMME: M.A. DEVELOPMENT STUDIES COURSE: STATISTICAL METHODS FOR DEVELOPMENT RESEARCH (MADVS2005C04) UNIT III-PART A CORRELATION PROF.(Dr). KRISHNAN CHALIL 25, APRIL 2020 1 U N I T – 3 :C O R R E L A T I O N After reading this material, the learners are expected to : . Understand the meaning of correlation and regression . Learn the different types of correlation . Understand various measures of correlation., and, . Explain the regression line and equation 3.1. Introduction By now we have a clear idea about the behavior of single variables using different measures of Central tendency and dispersion. Here the data concerned with one variable is called ‗univariate data‘ and this type of analysis is called ‗univariate analysis‘. But, in nature, some variables are related. For example, there exists some relationships between height of father and height of son, price of a commodity and amount demanded, the yield of a plant and manure added, cost of living and wages etc. This is a a case of ‗bivariate data‘ and such analysis is called as ‗bivariate data analysis. Correlation is one type of bivariate statistics. Correlation is the relationship between two variables in which the changes in the values of one variable are followed by changes in the values of the other variable. 3.2. Some Definitions 1. ―When the relationship is of a quantitative nature, the approximate statistical tool for discovering and measuring the relationship and expressing it in a brief formula is known as correlation‖—(Craxton and Cowden) 2. ‗correlation is an analysis of the co-variation between two or more variables‖—(A.M Tuttle) 3. -
BACHELOR of COMMERCE (Hons)
GURU GOBIND SINGH INDRAPRASTHA UNIVERSITY , D ELHI BACHELOR OF COMMERCE (Hons) BCOM 110- Business Statistics L-5 T/P-0 Credits-5 Objectives: The objective of this course is to familiarize students with the basic statistical tools used to summarize and analyze quantitative information for decision making. COURSE CONTENTS Unit I Lectures: 20 Statistical Data and Descriptive Statistics: Measures of Central Tendency: Mathematical averages including arithmetic mean, geometric mean and harmonic mean, properties and applications, positional averages, mode, median (and other partition values including quartiles, deciles, and percentile; Unit II Lectures: 15 Measures of variation: absolute and relative, range, quartile deviation, mean deviation, standard deviation, and their co-efficients, properties of standard deviation/variance; Moments: calculation and significance; Skewness, Kurtosis and Moments. Unit III Lectures: 15 Simple Correlation and Regression Analysis: Correlation Analysis, meaning of correlation simple, multiple and partial; linear and non-linear, Causation and correlation, Scatter diagram, Pearson co-efficient of correlation; calculation and properties, probable and standard errors, rank correlation; Simple Regression Analysis: Regression equations and estimation. Unit IV Lectures: 20 Index Numbers: Meaning and uses of index numbers, construction of index numbers, univariate and composite, aggregative and average of relatives – simple and weighted, tests of adequacy of index numbers, Base shifting, problems in the construction of index numbers. Text Books : 1. Levin, Richard and David S. Rubin. (2011), Statistics for Management. 7th Edition. PHI. 2. Gupta, S.P., and Gupta, Archana, (2009), Statistical Methods. Sultan Chand and Sons, New Delhi. Reference Books : 1. Berenson and Levine, (2008), Basic Business Statistics: Concepts and Applications. Prentice Hall. BUSINESS STATISTICS:PAPER CODE 110 Dr. -
The Interpretation of the Probable Error and the Coefficient of Correlation
THE UNIVERSITY OF ILLINOIS LIBRARY 370 IL6 . No. 26-34 sssftrsss^"" Illinois Library University of L161—H41 Digitized by the Internet Archive in 2011 with funding from University of Illinois Urbana-Champaign http://www.archive.org/details/interpretationof32odel BULLETIN NO. 32 BUREAU OF EDUCATIONAL RESEARCH COLLEGE OF EDUCATION THE INTERPRETATION OF THE PROBABLE ERROR AND THE COEFFICIENT OF CORRELATION By Charles W. Odell Assistant Director, Bureau of Educational Research ( (Hi THE UBMM Of MAR 14 1927 HKivERsrrv w Illinois PRICE 50 CENTS PUBLISHED BY THE UNIVERSITY OF ILLINOIS. URBANA 1926 370 lit TABLE OF CONTENTS PAGE Preface 5 i Chapter I. Introduction 7 Chapter II. The Probable Error 9 .Chapter III. The Coefficient of Correlation 33 PREFACE Graduate students and other persons contemplating ed- ucational research frequently ask concerning the need for training in statistical procedures. They usually have in mind training in the technique of making tabulations and calcula- tions. This, as Doctor Odell points out, is only one phase, and probably not the most important phase, of needed train- ing in statistical methods. The interpretation of the results of calculation has not received sufficient attention by the authors of texts in this field. The following discussion of two derived measures, the probable error and the coefficient of correlation, is offered as a contribution to the technique of educational research. It deals with the problems of the reader of reports of research, as well as those of original investigators. The tabulating of objective data and the mak- ing of calculations from the tabulations may be and fre- quently is a tedious task, but it is primarily one of routine. -
Outing the Outliers – Tails of the Unexpected
Presented at the 2016 International Training Symposium: www.iceaaonline.com/bristol2016 Outing the Outliers – Tails of the Unexpected Outing the Outliers – Tails of the Unexpected Cynics would say that we can prove anything we want with statistics as it is all down to … a word (or two) from the Wise? interpretation and misinterpretation. To some extent this is true, but in truth it is all down to “The great tragedy of Science: the slaying making a judgement call based on the balance of of a beautiful hypothesis by an ugly fact” probabilities. Thomas Henry Huxley (1825-1895) British Biologist The problem with using random samples in estimating is that for a small sample size, the values could be on the “extreme” side, relatively speaking – like throwing double six or double one with a pair of dice, and nothing else for three of four turns. The more random samples we have the less likely (statistically speaking) we are to have all extreme values. So, more is better if we can get it, but sometimes it is a question of “We would if we could, but we can’t so we don’t!” Now we could argue that Estimators don’t use random values (because it sounds like we’re just guessing); we base our estimates on the “actuals” we have collected for similar tasks or activities. However, in the context of estimating, any “actual” data is in effect random because the circumstances that created those “actuals” were all influenced by a myriad of random factors. Anyone who has ever looked at the “actuals” for a repetitive task will know that there are variations in those values. -
STATISTICS of OBSERVATIONS & SAMPLING THEORY Parent
ASTR 511/O’Connell Lec 6 1 STATISTICS OF OBSERVATIONS & SAMPLING THEORY References: Bevington “Data Reduction & Error Analysis for the Physical Sciences” LLM: Appendix B Warning: the introductory literature on statistics of measurement is remarkably uneven, and nomenclature is not consistent. Is error analysis important? Yes! See next page. Parent Distributions Measurement of any physical quantity is always affected by uncontrollable random (“stochastic”) processes. These produce a statistical scatter in the values measured. The parent distribution for a given measurement gives the probability of obtaining a particular result from a single measure. It is fully defined and represents the idealized outcome of an infinite number of measures, where the random effects on the measuring process are assumed to be always the same (“stationary”). ASTR 511/O’Connell Lec 6 2 ASTR 511/O’Connell Lec 6 3 Precision vs. Accuracy • The parent distribution only describes the stochastic scatter in the measuring process. It does not characterize how close the measurements are to the true value of the quantity of interest. Measures can be affected by systematic errors as well as by random errors. In general, the effects of systematic errors are not manifested as stochastic variations during an experiment. In the lab, for instance, a voltmeter may be improperly calibrated, leading to a bias in all the measurements made. Examples of potential systematic effects in astronomical photometry include a wavelength mismatch in CCD flat-field calibrations, large differential refraction in Earth’s atmosphere, or secular changes in thin clouds. • Distinction between precision and accuracy: o A measurement with a large ratio of value to statistical uncertainty is said to be “precise.” o An “accurate” measurement is one which is close to the true value of the parameter being measured. -
Chapter 3 the Problem of Scale
Chapter 3 The problem of scale Exploratory data analysis is detective work–numerical detective work–or counting detective work–or graphical detective work. A detective investigating a crime needs both tools and under- standing. If he has no fingerprint powder, he will fail to find fingerprints on most surfaces. If he does not understand where the criminal is likely to have put his fingers, he will will not look in the right places. Equally, the analyst of data needs both tools and understanding (p 1: Tukey (1977)) As discussed in Chapter 1 the challenge of psychometrics is assign numbers to observations in a way that best summarizes the underlying constructs. The ways to collect observations are multiple and can be based upon comparisons of order or of proximity (Chapter 2). But given a set of observations, how best to describe them? This is a problem not just for observational but also for experimental psychologists for both approaches are attempting to make inferences about latent variables in terms of statistics based upon observed variables (Figure 3.1). For the experimentalist, the problem becomes interpreting the effect of an experimental manipulation upon some outcome variable (path B in Figure 3.1 in terms of the effect of manipulation on the latent outcome variable (path b) and the relationship between the latent and observed outcome variables (path s). For the observationalist, the observed correlation between the observed Person Variable and Outcome variable (path A) is interpreted as a function of the relationship between the latent person trait variable and the observed trait variable (path r), the latent outcome variable and the observed outcome variable (path s), and most importantly for inference, the relationship between the two latent variables (path a). -
Notes on the Use of Propagation of Error Formulas
~.------~------------ JOURNAL OF RESEARCH of the National Bureau of Standards - C. Engineering and Instrumentation Vol. 70C, No.4, October- December 1966 Notes on the Use of Propagation of Error Formulas H. H. Ku Institute for Basic Standards, National Bureau of Standards, Washington, D.C. 20234 (May 27, 1966) The " la w of propagation o[ error" is a tool th at physical scientists have convenie ntly and frequently used in th eir work for many years, yet an adequate refere nce is diffi c ult to find. In this paper an exposi tory rev ie w of this topi c is presented, partic ularly in the li ght of c urre nt practi ces a nd interpretations. Examples on the accuracy of the approximations are given. The reporting of the uncertainties of final results is discussed. Key Words : Approximation, e rror, formula, imprecision, law of error, prod uc ts, propagati on of error, random, ratio, syste mati c, sum. Introduction The " law of propagation of error" is a tool that physical scientists have conveniently and frequently In the December 1939, issue of the American used in their work for many years. No claim is Physics Teacher, Raymond T. Birge wrote an ex made here that it is the only tool or even a suitable pository paper on "The Propagation of Errors." tool for all occasions. "Data analysis" is an ever In the introductory paragraph of his paper, Birge expanding field and other methods, existing or new, re marked: are probably available for the analysis and inter "The questi on of what constitutes the most reliable valu e to pretation fo r each particular set of data. -
Measures of Central Tendency
1 UNIT-1 FREQUENCY DISTRIBUTION Structure: 1.0 Introduction 1.1 Objectives 1.2 Measures of Central Tendency 1.2.1 Arithmetic mean 1.2.2 Median 1.2.3 Mode 1.2.4 Empirical relation among mode, median and mode 1.2.5 Geometric mean 1.2.6 Harmonic mean 1.3 Partition values 1.3.1 Quartiles 1.3.2 Deciles 1.3.3 Percentiles 1.4 Measures of dispersion 1.4.1 Range 1.4.2 Semi-interquartile range 1.4.3 Mean deviation 1.4.4 Standard deviation 1.2.5 Geometric mean 1.5 Absolute and relative measure of dispersion 1.6 Moments 1.7 Karl Pearson’s β and γ coefficients 1.8 Skewness 1.9 Kurtosis 1.10 Let us sum up 1.11 Check your progress : The key. 2 1.0 INTRODUCTION According to Simpson and Kafka a measure of central tendency is typical value around which other figures aggregate‘. According to Croxton and Cowden ‗An average is a single value within the range of the data that is used to represent all the values in the series. Since an average is somewhere within the range of data, it is sometimes called a measure of central value‘. 1.1 OBJECTIVES The main aim of this unit is to study the frequency distribution. After going through this unit you should be able to : describe measures of central tendency ; calculate mean, mode, median, G.M., H.M. ; find out partition values like quartiles, deciles, percentiles etc; know about measures of dispersion like range, semi-inter-quartile range, mean deviation, standard deviation; calculate moments, Karls Pearsion’s β and γ coefficients, skewness, kurtosis.