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STATISTICS APPLIED to PSYCHOLOGY I – Code 800146 Academic Year 2020-21
STATISTICS APPLIED TO PSYCHOLOGY I – Code 800146 Academic Year 2020-21 COURSE INFORMATION Undergraduate Studies: 0812 – Bachelor’s degree in Psychology (Studies Plan 2009-10) Type: Basic (compulsory) ECTS: 6.0 Module: Basic training Area: Statistics Year: First Semester: 1 LECTURER INFORMATION Name: Rocío Alcalá Quintana, PhD Mail: [email protected] Office number: 2121-O Office hours (fall semester): Tuesdays and Thursdays, from 15.30 to 18.00. SYNOPSIS COMPETENCIES General competencies GC6: Know and understand research methods and data analysis techniques. Transversal competencies TC1: Analysis and synthesis. TC2: Preparation and defence of properly reasoned arguments. TC3: Problem solving and decision making in Psychology. TC5: Looking for information and data interpretation on social, scientific and ethical topics related to the field of Psychology. TC6: Team work and collaboration with other professionals TC7: Critical thinking and self- analysis. TC8: Learning how to learn, skills for life-long learning. Specific competencies SC17: Be able to measure and obtain relevant data for the evaluation of interventions. SC18: Know how to analyse and interpret results of evaluations. SC19: Know how to appropriately and accurately provide feedback to recipients. TEACHING ACTIVITIES TEACHING ACTIVITIES Hours % of total Attendance credits Theory classes 45 30 % 100% Practical sessions 15 10 % 100% Students' work 82.5 55 % 0% Assessment 7.5 5 % 100% BRIEF DESCRIPTION: Basic concepts of measurement and types of variables. Introduction to Statistics. Data summarization and visualization. Measures of central tendency, variability, and skewness. Measures of association. Introduction to probability theory. Probability distributions of some continuous and discrete random variables. Sampling. PRE-REQUISITES Basic proficiency in secondary school maths is required to follow the course adequately. -
Simple Mean Weighted Mean Or Harmonic Mean
MultiplyMultiply oror Divide?Divide? AA BestBest PracticePractice forfor FactorFactor AnalysisAnalysis 77 ––10 10 JuneJune 20112011 Dr.Dr. ShuShu-Ping-Ping HuHu AlfredAlfred SmithSmith CCEACCEA Los Angeles Washington, D.C. Boston Chantilly Huntsville Dayton Santa Barbara Albuquerque Colorado Springs Ft. Meade Ft. Monmouth Goddard Space Flight Center Ogden Patuxent River Silver Spring Washington Navy Yard Cleveland Dahlgren Denver Johnson Space Center Montgomery New Orleans Oklahoma City Tampa Tacoma Vandenberg AFB Warner Robins ALC Presented at the 2011 ISPA/SCEA Joint Annual Conference and Training Workshop - www.iceaaonline.com PRT-70, 01 Apr 2011 ObjectivesObjectives It is common to estimate hours as a simple factor of a technical parameter such as weight, aperture, power or source lines of code (SLOC), i.e., hours = a*TechParameter z “Software development hours = a * SLOC” is used as an example z Concept is applicable to any factor cost estimating relationship (CER) Our objective is to address how to best estimate “a” z Multiply SLOC by Hour/SLOC or Divide SLOC by SLOC/Hour? z Simple, weighted, or harmonic mean? z Role of regression analysis z Base uncertainty on the prediction interval rather than just the range Our goal is to provide analysts a better understanding of choices available and how to select the right approach Presented at the 2011 ISPA/SCEA Joint Annual Conference and Training Workshop - www.iceaaonline.com PR-70, 01 Apr 2011 Approved for Public Release 2 of 25 OutlineOutline Definitions -
Math 234: Course Syllabus – Spring 2013
Math 234: Course Syllabus – Spring 2013 Course logistics: Time and Location: Tuesday & Thursday 3:30pm – 4:45pm, 251 Mercer St., CIWW 517 Instructor: Michael O’Neil Email: [email protected] Phone: 212-992-5874 Office: Room 1105A, Warren Weaver Hall Office hours: Thursday 11:00am – 1:00pm Course website: See NYU Classes Recitation time: Friday 2:00pm – 3:15pm, 25 W. 4th St., C-8 Teaching assistant: Alex RoZinov Email: [email protected] Course information: Description: This course is intended as a thorough mathematical introduction to the theory of statistics, intended to be taken after sufficiency in probability is obtained at the level of Math 233: Theory of Probability. Topics covered in this class will include: sampling theory, hypothesis testing, point (parameter) estimation, regression and linear least squares, the bootstrap, tests of significance, likelihood methods, and an intro to Bayesian statistics. The course will mainly focus on analytical approaches, but will contain computational aspects of statistics such as simulation, least squares, tests of significance, etc. Text: Mandatory, homework problems will be assigned from it: Bulmer, Principles of Statistics Suggested references: Rice, Mathematical Statistics and Data Analysis Wasserman, All of Statistics Casella & Berger, Statistical Inference (graduate level) Schaum’s Outline of Statistics Grading: The grade for the course will be determined based on weekly homework assignments, a midterm, and a final exam. The following rubric will be used: Homework 30% Midterm 30% Final 40% The lowest homework grade will be dropped. Homework is due at the beginning of class on the due date; in the interest of fairness, absolutely no late assignments will be accepted. -
IN STATISTICS Under Choice Based Credit System (CBCS)
SYLLABUS FOR B.SC. (HONOURS) IN STATISTICS Under Choice Based Credit System (CBCS) Effective from 2017-2018 The University of Burdwan Burdwan-713104 West Bengal 1 Outlines of Course Structures The main components of this syllabus are as follows: 1. Core Course 2. Elective Course 3. Ability Enhancement Course 1. Core Course (CC) A course, that should compulsorily be studied by a candidate as a core requirement, is termed as a core course. 2. Elective Course 2.1 Discipline Specific Elective (DSE) Course: A course, which may be offered by the main discipline/subject of study, is referred to as Discipline Specific Elective. 2.2 Generic Elective (GE) Course: An elective course, chosen generally from an unrelated discipline/subject of study with intention to seek an exposure, is called a Generic Elective Course. 3. Ability Enhancement Course (AEC) The Ability Enhancement Course may be of two kinds: 3.1 Ability Enhancement Compulsory Course (AECC) 3.2 Skill Enhancement Course (SEC) Details of Courses of B.Sc. (Honours) under CBCS Course Credit Marks 1 Core Course (14 papers) Theory + Theory +Tutorial 14×75= 1050 Practical 14× (5+1)=84 14×(4+2)=84 2 Elective Course (8 Papers) A. DSE 4×(4+2)=24 4×(5+1)=24 4´75= 300 (4 Papers) B. GE 4×(4+2)=24 4×(5+1)=24 4´75= 300 (4 Papers) 3 Ability Enhancement Course A. AECC (2 Papers) AECC1 (ENVS) 4×1=4 4×1=4 100 AECC2 (English/MIL) 2×1=2 2×1=2 50 B. SEC (2 Papers) 2×2=4 2×2=4 2´50= 100 Total Credit : 142 142 Total Marks = 1900 2 Semester wise Course Structures Semester Course Course Name of the Course Pattern -
Hydraulics Manual Glossary G - 3
Glossary G - 1 GLOSSARY OF HIGHWAY-RELATED DRAINAGE TERMS (Reprinted from the 1999 edition of the American Association of State Highway and Transportation Officials Model Drainage Manual) G.1 Introduction This Glossary is divided into three parts: · Introduction, · Glossary, and · References. It is not intended that all the terms in this Glossary be rigorously accurate or complete. Realistically, this is impossible. Depending on the circumstance, a particular term may have several meanings; this can never change. The primary purpose of this Glossary is to define the terms found in the Highway Drainage Guidelines and Model Drainage Manual in a manner that makes them easier to interpret and understand. A lesser purpose is to provide a compendium of terms that will be useful for both the novice as well as the more experienced hydraulics engineer. This Glossary may also help those who are unfamiliar with highway drainage design to become more understanding and appreciative of this complex science as well as facilitate communication between the highway hydraulics engineer and others. Where readily available, the source of a definition has been referenced. For clarity or format purposes, cited definitions may have some additional verbiage contained in double brackets [ ]. Conversely, three “dots” (...) are used to indicate where some parts of a cited definition were eliminated. Also, as might be expected, different sources were found to use different hyphenation and terminology practices for the same words. Insignificant changes in this regard were made to some cited references and elsewhere to gain uniformity for the terms contained in this Glossary: as an example, “groundwater” vice “ground-water” or “ground water,” and “cross section area” vice “cross-sectional area.” Cited definitions were taken primarily from two sources: W.B. -
2010 Amazon 100 Bestsellers in Applied Mathematics November 12, 2010 10:30 AM
2010 Amazon 100 Bestsellers in Applied Mathematics November 12, 2010 10:30 AM. 1. Freakonomics Rev Ed: (and Other Riddles of Modern Life) Steven D. Levitt, Stephen J. Dubner HC? [Kindle] 2. The Calculus Diaries: How Math Can Help You Lose Weight, Win in Vegas, and Survive a Zombie Apocalypse by Jennifer Ouellette 3. How to Measure Anything: Finding the Value of Intangibles in Business by Douglas W. Hubbard 4. The Visual Display of Quantitative Information, 2nd edition by Edward R. Tufte 5. Secrets of Mental Math: The Mathemagician's Guide to Lightning Calculation and Amazing Math Tricks by Arthur Benjamin, Michael Shermer 6. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches by John W. Creswell 7. How to Lie with Statistics by Darrell Huff 8. Secrets of Mental Math: The Mathemagician's Guide to Lightning Calculation and Amazing Math Tricks Michael Shermer [Kindle] 9. Cartoon Guide to Statistics by Larry Gonick, Woollcott Smith 10. Statistics for Dummies by Deborah Rumsey 11. Discovering Statistics Using SPSS (Introducing Statistical Methods) by Andy Field 12. Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb 13. Freakonomics Rev Ed: (and Other Riddles of Modern Life) Steven D. Levitt, Stephen J. Dubner PB? [Kindle] 14. The Drunkard's Walk: How Randomness Rules Our Lives (Vintage) by Leonard Mlodinow 15. Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb [Kindle] 16. Envisioning Information by Edward R. Tufte 17. Now You See It: Simple Visualization Techniques for Quantitative Analysis by Stephen Few 18. -
Download Schaum's Outline of Statistics and Econometrics
NEW CUSTOMER? START HERE. Schaum's Outline of Statistics and Econometrics, Second Edition, Dominick Salvatore, Derrick Reagle, McGraw-Hill Education, 2011, 0071755470, 9780071755474, 336 pages. The ideal review for your statistics and econometrics course More than 40 million students have trusted Schaum’s Outlines for their expert knowledge and helpful solved problems. Written by renowned experts in their respective fields, Schaum’s Outlines cover everything from math to science, nursing to language. The main feature for all these books is the solved problems. Step-by-step, authors walk readers through coming up with solutions to exercises in their topic of choice. Clear, concise explanations of all statistics and econometrics concepts Appropriate for the following courses: Statistics and Econometrics, Statistical Methods in Economics, Quantitative Methods in Economics, Mathematical Economics, Micro-Economics, Macro-Economics, Math for Economists, Math for Social Sciences . DOWNLOAD PDF HERE International economics , Dominick Salvatore, Jan 15, 1998, , 766 pages. Posodobljena in vsebinsko razširjena šesta izdaja svetovne uspešnice s področja mednarodnega gospodarstva vključuje mnoga nova poglavja, študije primerov, najnovejše podatke in .... Microeconomics Theory and Applications, Dominick Salvatore, 2003, Business & Economics, 724 pages. "This fully-revised text provides robust coverage of intermediate microeconomics within a global context. Building on the success of previous editions, the text presents a .... Schaum's Outline of Structural Steel Design , Abraham Rokach, 1991, Technology & Engineering, 187 pages. A revision guide for students of structural engineering aiming to provide a succinct description of the key features of structural steel design using LRFD. Among topics .... Statistical estimation of simultaneous economic relationships , Anirudh Lal Nagar, 1959, Business & Economics, 96 pages. -
Notes on Calculating Computer Performance
Notes on Calculating Computer Performance Bruce Jacob and Trevor Mudge Advanced Computer Architecture Lab EECS Department, University of Michigan {blj,tnm}@umich.edu Abstract This report explains what it means to characterize the performance of a computer, and which methods are appro- priate and inappropriate for the task. The most widely used metric is the performance on the SPEC benchmark suite of programs; currently, the results of running the SPEC benchmark suite are compiled into a single number using the geometric mean. The primary reason for using the geometric mean is that it preserves values across normalization, but unfortunately, it does not preserve total run time, which is probably the figure of greatest interest when performances are being compared. Cycles per Instruction (CPI) is another widely used metric, but this method is invalid, even if comparing machines with identical clock speeds. Comparing CPI values to judge performance falls prey to the same prob- lems as averaging normalized values. In general, normalized values must not be averaged and instead of the geometric mean, either the harmonic or the arithmetic mean is the appropriate method for averaging a set running times. The arithmetic mean should be used to average times, and the harmonic mean should be used to average rates (1/time). A number of published SPECmarks are recomputed using these means to demonstrate the effect of choosing a favorable algorithm. 1.0 Performance and the Use of Means We want to summarize the performance of a computer; the easiest way uses a single number that can be compared against the numbers of other machines. -
STATISTICS APPLIED to PSYCHOLOGY I – Code 800146 Academic Year 2018-19
STATISTICS APPLIED TO PSYCHOLOGY I – Code 800146 Academic Year 2018-19 COURSE INFORMATION Undergraduate Studies: 0812 – Bachelor’s degree in Psychology (Studies Plan 2009-10) Type: Basic (compulsory) ECTS: 6.0 Module: Basic training Area: Statistics Year: First Semester: 1 LECTURER INFORMATION Name: Rocío Alcalá Quintana, PhD Mail: [email protected] Office number: 2121-O Office hours (fall semester): Tuesdays and Thursdays, from 15.30 to 18.00. SYNOPSIS COMPETENCIES General competencies GC6: Know and understand research methods and data analysis techniques. Transversal competencies TC1: Analysis and synthesis. TC2: Preparation and defence of properly reasoned arguments. TC3: Problem solving and decision making in Psychology. TC5: Looking for information and data interpretation on social, scientific and ethical topics related to the field of Psychology. TC6: Team work and collaboration with other professionals TC7: Critical thinking and self- analysis. TC8: Learning how to learn, skills for life-long learning. Specific competencies SC17: Be able to measure and obtain relevant data for the evaluation of interventions. SC18: Know how to analyse and interpret results of evaluations. SC19: Know how to appropriately and accurately provide feedback to recipients. TEACHING ACTIVITIES TEACHING ACTIVITIES Hours % of total Attendance credits Theory classes 45 30 % 100% Practical sessions 15 10 % 100% Students' work 82.5 55 % 0% Assessment 7.5 5 % 100% BRIEF DESCRIPTION: Basic concepts of measurement and types of variables. Introduction to Statistics. Data summarization and visualization. Measures of central tendency, variability, and skewness. Measures of association. Introduction to probability theory. Probability distributions of some continuous and discrete random variables. Sampling. PRE-REQUISITES Basic proficiency in secondary school maths is required to follow the course adequately. -
Cross-Sectional Skewness
Cross-sectional Skewness Sangmin Oh∗ Jessica A. Wachtery June 18, 2019 Abstract This paper evaluates skewness in the cross-section of stock returns in light of pre- dictions from a well-known class of models. Cross-sectional skewness in monthly returns far exceeds what the standard lognormal model of returns would predict. In spite of the fact that cross-sectional skewness is positive, aggregate market skewness is negative. We present a model that accounts for both of these facts. This model also exhibits long-horizon skewness through the mechanism of nonstationary firm shares. ∗Booth School of Business, The University of Chicago. Email: [email protected] yThe Wharton School, University of Pennsylvania. Email: [email protected]. We thank Hendrik Bessembinder, John Campbell, Marco Grotteria, Nishad Kapadia, Yapai Zhang, and seminar participants at the Wharton School for helpful comments. 1 Introduction Underlying the cross-section of stock returns is a universe of heterogeneous entities com- monly referred to as firms. What is the most useful approach to modeling these firms? For the aggregate market, there is a wide consensus concerning the form a model needs to take to be a plausible account of the data. While there are important differences, quantitatively successful models tend to feature a stochastic discount factor with station- ary growth rates and permanent shocks, combined with aggregate cash flows that, too, have stationary growth rates and permanent shocks.1 No such consensus exists for the cross-section. We start with a simple model for stock returns to illustrate the puzzle. The model is not meant to be the final word on the cross-section, but rather to show that the most straightforward way to extend the consensus for the aggregate to the cross-section runs quickly into difficulties both with regard to data and to theory. -
Math 140 Introductory Statistics
Notation Population Sample Sampling Math 140 Distribution Introductory Statistics µ µ Mean x x Standard Professor Bernardo Ábrego Deviation σ s σ x Lecture 16 Sections 7.1,7.2 Size N n Properties of The Sampling Example 1 Distribution of The Sample Mean The mean µ x of the sampling distribution of x equals the mean of the population µ: Problems usually involve a combination of the µx = µ three properties of the Sampling Distribution of the Sample Mean, together with what we The standard deviation σ x of the sampling distribution of x , also called the standard error of the mean, equals the standard learned about the normal distribution. deviation of the population σ divided by the square root of the sample size n: σ Example: Average Number of Children σ x = n What is the probability that a random sample The Shape of the sampling distribution will be approximately of 20 families in the United States will have normal if the population is approximately normal; for other populations, the sampling distribution becomes more normal as an average of 1.5 children or fewer? n increases. This property is called the Central Limit Theorem. 1 Example 1 Example 1 Example: Average Number Number of Children Proportion of families, P(x) of Children (per family), x µx = µ = 0.873 What is the probability that a 0 0.524 random sample of 20 1 0.201 σ 1.095 σx = = = 0.2448 families in the United States 2 0.179 n 20 will have an average of 1.5 3 0.070 children or fewer? 4 or more 0.026 Mean (of population) 0.6 µ = 0.873 0.5 0.4 Standard Deviation 0.3 σ =1.095 0.2 0.1 0.873 0 01234 Example 1 Example 2 µ = µ = 0.873 Find z-score of the value 1.5 Example: Reasonably Likely Averages x x − mean z = = What average numbers of children are σ 1.095 SD σ = = = 0.2448 reasonably likely in a random sample of 20 x x − µ 1.5 − 0.873 n 20 = x = families? σx 0.2448 ≈ 2.56 Recall that the values that are in the middle normalcdf(−99999,2.56) ≈ .9947 95% of a random distribution are called So in a random sample of 20 Reasonably Likely. -
4.1 What Is an Average? Example
STAT1010 – mean (or average) Chapter 4: Describing data ! 4.1 Averages and measures of center " Describing the center of a distribution ! 4.2 Shapes of distributions " Describing the shape ! 4.3 Quantifying variation " Describing the spread of a distribution 1 4.1 What is an average? ! In statistics, we generally use the term mean instead of average, and the mean has a specific formula… mean = sum of all values total number of values ! The term average could be interpreted in a variety of ways, thus, we’ll focus on the mean of a distribution or set of numbers. 2 Example: Eight grocery stores sell the PR energy bar for the following prices: $1.09 $1.29 $1.29 $1.35 $1.39 $1.49 $1.59 $1.79 Find the mean of these prices. Solution: The mean price is $1.41: $1.09 + $1.29 + $1.29 + $1.35 + $1.39 + $1.49 + $1.59 + $1.79 mean = 8 = $1.41 3 1 STAT1010 – mean (or average) Example: Octane Rating n = 40 87.4, 88.4, 88.7, 88.9, 89.3, 89.3, 89.6, 89.7 89.8, 89.8, 89.9, 90.0, 90.1, 90.3, 90.4, 90.4 90.4, 90.5, 90.6, 90.7, 91.0, 91.1, 91.1, 91.2 91.2, 91.6, 91.6, 91.8, 91.8, 92.2, 92.2, 92.2 92.3, 92.6, 92.7, 92.7, 93.0, 93.3, 93.7, 94.4 4 Example: Octane Rating Technical Note (short hand formula for the mean): Let x1, x2, …, xn represent n values.