Multiple Choice: 2 points each

Size: px
Start display at page:

Download "Multiple Choice: 2 points each"

Transcription

1 MID TERM MSF 503 Modeling 1 Name: Answers go here! NEATNESS COUNTS!!! Multiple Choice: 2 points each 1. In Excel, the VLOOKUP function does what? Searches the first row of a range of cells, and then returns a value from any cell on the same column of the range. Searches the last column of a range of cells, and then returns a value from any row on the same column of the range. Searches the first column of a range of cells, and then returns a value from any cell on the same row of the range. Searches the last column of a range of cells, and then returns a value from any column on the same row of the range. 2. When the number of Bernoulli trials, n, is large n p will be normally distributed. The normal distribution determines the value of each random x. We no longer need to generate u s. The probability of success p is approaches the standard normal. 3. Which of the following generates a z in Excel? = 3 + RAND() * 4 = 5 * NORMSINV() - 2 = 4 + NORMSINV( RAND() ) - 3 = 6 + NORMSINV( RAND() ) * 7 4. If we want to look at how changing the value of an input affects the value output over a range of possible inputs, we can use Goal Seek Data Table VLOOKUP Cell Auditing

2 5. A model is Reality. A representation. A forecast. The truth. 6. The inverse transform method for generating random numbers works because The LCG can be programmed in VBA. Integration is possible. f(x) is continuous. F(x) is uniformly distributed. 7. The Beta parameter in the exponential distribution Determines the probable number of arrivals per period. Is the variance of interarrival times. Is the variance of the Poisson process. Tells you how many arrivals will occur. 8. Generating bivariate normal random numbers means calculating Rho ρ. Conditional means and variances. The standard deviations of x 1 and x 2. Covariances. 9. In VBA, Option Explicit Fixes the lower bound of all arrays to 0. Requires variable declaration before definition. Disallows implicit type conversion. Declares the default comparison method for strings.

3 10. What does Goal Seek do? Finds the optimal model parameter(s). Shows how the outcomes will vary over a range of inputs. Shows the maximum model output and its parameters. Finds the model inputs that lead to a target output. 11. Linear interpolation is a... Method for calculating unknown regression coefficients. Method for examining known empirical distributions. Method for forecasting unknown results in the future. Method for finding unknown points between known ones. 12. Any Excel or VBA function that returns an array requires... Ctrl + Alt + Enter. An input parameter of type variant. The Range().Offset() syntax. Absolute cell referencing. 13. According to the CAPM, the market risk premium is: The population standard deviation of log returns of the market. The expected return of the market minus the risk free rate. The sample standard deviation of log returns of the market. The expected return of the market minus the expected return on a stock. 14. From the CAPM, the Beta of a stock (or a portfolio of stocks)... Is the correlation of the stock with the market. Is the covariance of the stock with the market. Is the slope of the stock with the market. Is the intercept of the stock with the market.

4 15. Cross-sectional normalizing, whereas time series normalizing. (Pick 2 answers.) Compares data from many stocks at a point in time. Compares data from a single stock across time. Compares data from the market to a single stock. Compares data from the market to a portfolio of stocks. 16. Excel forces definition of: 1. Calculations 2. Input and output data 3. GUI requirements 1, 2, and , and 3. 1, and Prototyping should always focus on The hardest parts first. Object oriented design considerations. The final design up front. The parts that will be the easiest to solve first. 18. What is a prototype spreadsheet? Something to be destroyed. A test version. An implementation tool. The optimal architecture. 19. The oracle problem refers to which? How do you know your spreadsheet is right? Methods of software testing. Code inspections and walkthroughs. Categories of spreadsheet errors.

5 20. Spreadsheet testing should consume how much development time? 4-5 hours per spreadsheet % of the total % of the total programming time. 1-2 hours per spreadsheet. Problems: 5 points each 21. Write a VBA function that will put normal (i.e. N( μ, σ 2 )) random numbers into a column of n number of cells. Assume you have a function rand_norms() that returns a z s.

6 22. Use the inverse transform method (i.e. no VBA or Excel code) to generate a - random number between 4 and 5 from the following density using u s =.512: 3 f ( x) =.0064x 0 x 5

7 23. Write a VBA function to generate a random x given the following empirical - data: i x i n x(i) Next, generate a random number from this function using u s =.734.

8 24. Write a VBA function that uses the Linear Congruential Generator to - pick a random numbers from the array of cells shown (with equal probabilities for all). A B C

9 25. Given the following time series data: - A 1 Raw Data and difference bins of 0-.10, ,.51-, calculate the ranked data scores. 26. If the probability of failure is.4, and the probability of success is.3, then how many successes and failures does the following set of u s s generate? u s = {.189,.832,.561,.432,.671,.483,.022,.705,.913 } - Write a VBA function that generates u s s returns the number of successes and failures in n such trials.

10 27. Given the following information: µ 1 = 2, µ 2 = 5, σ 1 = 1, σ 2 = 3, and ρ 1,2 =.5. Generate z 1 and z 2 given z s(1) =.485 and z s(2) = Essay: 15 points 28. What is meant by the terms spreadsheet errors, spreadsheet best practices and spreadsheet testing? Use specific examples from the readings to demonstrate your knowledge of these concepts.

11

12

Chapter 3 RANDOM VARIATE GENERATION

Chapter 3 RANDOM VARIATE GENERATION Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.

More information

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application

More information

Introduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman

Introduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman Introduction to Statistical Computing in Microsoft Excel By Hector D. Flores; hflores@rice.edu, and Dr. J.A. Dobelman Statistics lab will be mainly focused on applying what you have learned in class with

More information

Normality Testing in Excel

Normality Testing in Excel Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com

More information

Simple Regression Theory II 2010 Samuel L. Baker

Simple Regression Theory II 2010 Samuel L. Baker SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the

More information

Java Modules for Time Series Analysis

Java Modules for Time Series Analysis Java Modules for Time Series Analysis Agenda Clustering Non-normal distributions Multifactor modeling Implied ratings Time series prediction 1. Clustering + Cluster 1 Synthetic Clustering + Time series

More information

seven Statistical Analysis with Excel chapter OVERVIEW CHAPTER

seven Statistical Analysis with Excel chapter OVERVIEW CHAPTER seven Statistical Analysis with Excel CHAPTER chapter OVERVIEW 7.1 Introduction 7.2 Understanding Data 7.3 Relationships in Data 7.4 Distributions 7.5 Summary 7.6 Exercises 147 148 CHAPTER 7 Statistical

More information

Department of Mathematics, Indian Institute of Technology, Kharagpur Assignment 2-3, Probability and Statistics, March 2015. Due:-March 25, 2015.

Department of Mathematics, Indian Institute of Technology, Kharagpur Assignment 2-3, Probability and Statistics, March 2015. Due:-March 25, 2015. Department of Mathematics, Indian Institute of Technology, Kharagpur Assignment -3, Probability and Statistics, March 05. Due:-March 5, 05.. Show that the function 0 for x < x+ F (x) = 4 for x < for x

More information

Using Duration Times Spread to Forecast Credit Risk

Using Duration Times Spread to Forecast Credit Risk Using Duration Times Spread to Forecast Credit Risk European Bond Commission / VBA Patrick Houweling, PhD Head of Quantitative Credits Research Robeco Asset Management Quantitative Strategies Forecasting

More information

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics

Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in

More information

Calculator Notes for the TI-Nspire and TI-Nspire CAS

Calculator Notes for the TI-Nspire and TI-Nspire CAS CHAPTER 11 Calculator Notes for the Note 11A: Entering e In any application, press u to display the value e. Press. after you press u to display the value of e without an exponent. Note 11B: Normal Graphs

More information

Statistical Functions in Excel

Statistical Functions in Excel Statistical Functions in Excel There are many statistical functions in Excel. Moreover, there are other functions that are not specified as statistical functions that are helpful in some statistical analyses.

More information

Below is a very brief tutorial on the basic capabilities of Excel. Refer to the Excel help files for more information.

Below is a very brief tutorial on the basic capabilities of Excel. Refer to the Excel help files for more information. Excel Tutorial Below is a very brief tutorial on the basic capabilities of Excel. Refer to the Excel help files for more information. Working with Data Entering and Formatting Data Before entering data

More information

Pearson's Correlation Tests

Pearson's Correlation Tests Chapter 800 Pearson's Correlation Tests Introduction The correlation coefficient, ρ (rho), is a popular statistic for describing the strength of the relationship between two variables. The correlation

More information

Choosing Probability Distributions in Simulation

Choosing Probability Distributions in Simulation MBA elective - Models for Strategic Planning - Session 14 Choosing Probability Distributions in Simulation Probability Distributions may be selected on the basis of Data Theory Judgment a mix of the above

More information

Portfolio Performance Measures

Portfolio Performance Measures Portfolio Performance Measures Objective: Evaluation of active portfolio management. A performance measure is useful, for example, in ranking the performance of mutual funds. Active portfolio managers

More information

Review Jeopardy. Blue vs. Orange. Review Jeopardy

Review Jeopardy. Blue vs. Orange. Review Jeopardy Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 0-3 Jeopardy Round $200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?

More information

Illustration (and the use of HLM)

Illustration (and the use of HLM) Illustration (and the use of HLM) Chapter 4 1 Measurement Incorporated HLM Workshop The Illustration Data Now we cover the example. In doing so we does the use of the software HLM. In addition, we will

More information

Point Biserial Correlation Tests

Point Biserial Correlation Tests Chapter 807 Point Biserial Correlation Tests Introduction The point biserial correlation coefficient (ρ in this chapter) is the product-moment correlation calculated between a continuous random variable

More information

Probability Calculator

Probability Calculator Chapter 95 Introduction Most statisticians have a set of probability tables that they refer to in doing their statistical wor. This procedure provides you with a set of electronic statistical tables that

More information

Models of Risk and Return

Models of Risk and Return Models of Risk and Return Aswath Damodaran Aswath Damodaran 1 First Principles Invest in projects that yield a return greater than the minimum acceptable hurdle rate. The hurdle rate should be higher for

More information

FTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver

FTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver FTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver Question: How do you create a diversified stock portfolio? Advice given by most financial advisors

More information

Microsoft Excel. Qi Wei

Microsoft Excel. Qi Wei Microsoft Excel Qi Wei Excel (Microsoft Office Excel) is a spreadsheet application written and distributed by Microsoft for Microsoft Windows and Mac OS X. It features calculation, graphing tools, pivot

More information

Chapter 5 Risk and Return ANSWERS TO SELECTED END-OF-CHAPTER QUESTIONS

Chapter 5 Risk and Return ANSWERS TO SELECTED END-OF-CHAPTER QUESTIONS Chapter 5 Risk and Return ANSWERS TO SELECTED END-OF-CHAPTER QUESTIONS 5-1 a. Stand-alone risk is only a part of total risk and pertains to the risk an investor takes by holding only one asset. Risk is

More information

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm

More information

Using Microsoft Excel for Probability and Statistics

Using Microsoft Excel for Probability and Statistics Introduction Using Microsoft Excel for Probability and Despite having been set up with the business user in mind, Microsoft Excel is rather poor at handling precisely those aspects of statistics which

More information

SUMAN DUVVURU STAT 567 PROJECT REPORT

SUMAN DUVVURU STAT 567 PROJECT REPORT SUMAN DUVVURU STAT 567 PROJECT REPORT SURVIVAL ANALYSIS OF HEROIN ADDICTS Background and introduction: Current illicit drug use among teens is continuing to increase in many countries around the world.

More information

YASAIw.xla A modified version of an open source add in for Excel to provide additional functions for Monte Carlo simulation.

YASAIw.xla A modified version of an open source add in for Excel to provide additional functions for Monte Carlo simulation. YASAIw.xla A modified version of an open source add in for Excel to provide additional functions for Monte Carlo simulation. By Greg Pelletier, Department of Ecology, P.O. Box 47710, Olympia, WA 98504

More information

Evaluating Trading Systems By John Ehlers and Ric Way

Evaluating Trading Systems By John Ehlers and Ric Way Evaluating Trading Systems By John Ehlers and Ric Way INTRODUCTION What is the best way to evaluate the performance of a trading system? Conventional wisdom holds that the best way is to examine the system

More information

Data Analysis Tools. Tools for Summarizing Data

Data Analysis Tools. Tools for Summarizing Data Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool

More information

Analyzing Intervention Effects: Multilevel & Other Approaches. Simplest Intervention Design. Better Design: Have Pretest

Analyzing Intervention Effects: Multilevel & Other Approaches. Simplest Intervention Design. Better Design: Have Pretest Analyzing Intervention Effects: Multilevel & Other Approaches Joop Hox Methodology & Statistics, Utrecht Simplest Intervention Design R X Y E Random assignment Experimental + Control group Analysis: t

More information

Econometrics Simple Linear Regression

Econometrics Simple Linear Regression Econometrics Simple Linear Regression Burcu Eke UC3M Linear equations with one variable Recall what a linear equation is: y = b 0 + b 1 x is a linear equation with one variable, or equivalently, a straight

More information

CHAPTER 6: Continuous Uniform Distribution: 6.1. Definition: The density function of the continuous random variable X on the interval [A, B] is.

CHAPTER 6: Continuous Uniform Distribution: 6.1. Definition: The density function of the continuous random variable X on the interval [A, B] is. Some Continuous Probability Distributions CHAPTER 6: Continuous Uniform Distribution: 6. Definition: The density function of the continuous random variable X on the interval [A, B] is B A A x B f(x; A,

More information

business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar

business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel

More information

2WB05 Simulation Lecture 8: Generating random variables

2WB05 Simulation Lecture 8: Generating random variables 2WB05 Simulation Lecture 8: Generating random variables Marko Boon http://www.win.tue.nl/courses/2wb05 January 7, 2013 Outline 2/36 1. How do we generate random variables? 2. Fitting distributions Generating

More information

Bachelor's Degree in Business Administration and Master's Degree course description

Bachelor's Degree in Business Administration and Master's Degree course description Bachelor's Degree in Business Administration and Master's Degree course description Bachelor's Degree in Business Administration Department s Compulsory Requirements Course Description (402102) Principles

More information

Chapter 9 Monté Carlo Simulation

Chapter 9 Monté Carlo Simulation MGS 3100 Business Analysis Chapter 9 Monté Carlo What Is? A model/process used to duplicate or mimic the real system Types of Models Physical simulation Computer simulation When to Use (Computer) Models?

More information

CHAPTER 6. Topics in Chapter. What are investment returns? Risk, Return, and the Capital Asset Pricing Model

CHAPTER 6. Topics in Chapter. What are investment returns? Risk, Return, and the Capital Asset Pricing Model CHAPTER 6 Risk, Return, and the Capital Asset Pricing Model 1 Topics in Chapter Basic return concepts Basic risk concepts Stand-alone risk Portfolio (market) risk Risk and return: CAPM/SML 2 What are investment

More information

Statistical Data analysis With Excel For HSMG.632 students

Statistical Data analysis With Excel For HSMG.632 students 1 Statistical Data analysis With Excel For HSMG.632 students Dialog Boxes Descriptive Statistics with Excel To find a single descriptive value of a data set such as mean, median, mode or the standard deviation,

More information

Session 10. Laboratory Works

Session 10. Laboratory Works Session 10 Laboratory Works Adding-in the data analysis tool pack to excel Statistical analysis such as descriptive statistics and regression requires the Excel Data Analysis add-in. The default configuration

More information

This chapter will demonstrate how to perform multiple linear regression with IBM SPSS

This chapter will demonstrate how to perform multiple linear regression with IBM SPSS CHAPTER 7B Multiple Regression: Statistical Methods Using IBM SPSS This chapter will demonstrate how to perform multiple linear regression with IBM SPSS first using the standard method and then using the

More information

Chapter 7 Risk, Return, and the Capital Asset Pricing Model

Chapter 7 Risk, Return, and the Capital Asset Pricing Model Chapter 7 Risk, Return, and the Capital Asset Pricing Model MULTIPLE CHOICE 1. Suppose Sarah can borrow and lend at the risk free-rate of 3%. Which of the following four risky portfolios should she hold

More information

Simple Linear Regression, Scatterplots, and Bivariate Correlation

Simple Linear Regression, Scatterplots, and Bivariate Correlation 1 Simple Linear Regression, Scatterplots, and Bivariate Correlation This section covers procedures for testing the association between two continuous variables using the SPSS Regression and Correlate analyses.

More information

Monte Carlo Simulation. SMG ITS Advanced Excel Workshop

Monte Carlo Simulation. SMG ITS Advanced Excel Workshop Advanced Excel Workshop Monte Carlo Simulation Page 1 Contents Monte Carlo Simulation Tutorial... 2 Example 1: New Marketing Campaign... 2 VLOOKUP... 5 Example 2: Revenue Forecast... 6 Pivot Table... 8

More information

Outline. Topic 4 - Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares

Outline. Topic 4 - Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares Topic 4 - Analysis of Variance Approach to Regression Outline Partitioning sums of squares Degrees of freedom Expected mean squares General linear test - Fall 2013 R 2 and the coefficient of correlation

More information

A comparison between different volatility models. Daniel Amsköld

A comparison between different volatility models. Daniel Amsköld A comparison between different volatility models Daniel Amsköld 211 6 14 I II Abstract The main purpose of this master thesis is to evaluate and compare different volatility models. The evaluation is based

More information

Engineering Problem Solving and Excel. EGN 1006 Introduction to Engineering

Engineering Problem Solving and Excel. EGN 1006 Introduction to Engineering Engineering Problem Solving and Excel EGN 1006 Introduction to Engineering Mathematical Solution Procedures Commonly Used in Engineering Analysis Data Analysis Techniques (Statistics) Curve Fitting techniques

More information

WEB APPENDIX. Calculating Beta Coefficients. b Beta Rise Run Y 7.1 1 8.92 X 10.0 0.0 16.0 10.0 1.6

WEB APPENDIX. Calculating Beta Coefficients. b Beta Rise Run Y 7.1 1 8.92 X 10.0 0.0 16.0 10.0 1.6 WEB APPENDIX 8A Calculating Beta Coefficients The CAPM is an ex ante model, which means that all of the variables represent before-thefact, expected values. In particular, the beta coefficient used in

More information

Chapter 11. Topics Covered. Chapter 11 Objectives. Risk, Return, and Capital Budgeting

Chapter 11. Topics Covered. Chapter 11 Objectives. Risk, Return, and Capital Budgeting Chapter 11 Risk, Return, and Capital Budgeting Topics Covered Measuring Market Risk Portfolio Betas Risk and Return CAPM and Expected Return Security Market Line CAPM and Stock Valuation Chapter 11 Objectives

More information

HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION

HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate

More information

ESTIMATING THE DISTRIBUTION OF DEMAND USING BOUNDED SALES DATA

ESTIMATING THE DISTRIBUTION OF DEMAND USING BOUNDED SALES DATA ESTIMATING THE DISTRIBUTION OF DEMAND USING BOUNDED SALES DATA Michael R. Middleton, McLaren School of Business, University of San Francisco 0 Fulton Street, San Francisco, CA -00 -- middleton@usfca.edu

More information

CHAPTER 7: OPTIMAL RISKY PORTFOLIOS

CHAPTER 7: OPTIMAL RISKY PORTFOLIOS CHAPTER 7: OPTIMAL RIKY PORTFOLIO PROLEM ET 1. (a) and (e).. (a) and (c). After real estate is added to the portfolio, there are four asset classes in the portfolio: stocks, bonds, cash and real estate.

More information

STATISTICA Formula Guide: Logistic Regression. Table of Contents

STATISTICA Formula Guide: Logistic Regression. Table of Contents : Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

More information

General Sampling Methods

General Sampling Methods General Sampling Methods Reference: Glasserman, 2.2 and 2.3 Claudio Pacati academic year 2016 17 1 Inverse Transform Method Assume U U(0, 1) and let F be the cumulative distribution function of a distribution

More information

Forecasting in STATA: Tools and Tricks

Forecasting in STATA: Tools and Tricks Forecasting in STATA: Tools and Tricks Introduction This manual is intended to be a reference guide for time series forecasting in STATA. It will be updated periodically during the semester, and will be

More information

The Capital Asset Pricing Model (CAPM)

The Capital Asset Pricing Model (CAPM) Prof. Alex Shapiro Lecture Notes 9 The Capital Asset Pricing Model (CAPM) I. Readings and Suggested Practice Problems II. III. IV. Introduction: from Assumptions to Implications The Market Portfolio Assumptions

More information

Using Formulas, Functions, and Data Analysis Tools Excel 2010 Tutorial

Using Formulas, Functions, and Data Analysis Tools Excel 2010 Tutorial Using Formulas, Functions, and Data Analysis Tools Excel 2010 Tutorial Excel file for use with this tutorial Tutor1Data.xlsx File Location http://faculty.ung.edu/kmelton/data/tutor1data.xlsx Introduction:

More information

Chapter 15. Mixed Models. 15.1 Overview. A flexible approach to correlated data.

Chapter 15. Mixed Models. 15.1 Overview. A flexible approach to correlated data. Chapter 15 Mixed Models A flexible approach to correlated data. 15.1 Overview Correlated data arise frequently in statistical analyses. This may be due to grouping of subjects, e.g., students within classrooms,

More information

Exercise 1.12 (Pg. 22-23)

Exercise 1.12 (Pg. 22-23) Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.

More information

Estimation of σ 2, the variance of ɛ

Estimation of σ 2, the variance of ɛ Estimation of σ 2, the variance of ɛ The variance of the errors σ 2 indicates how much observations deviate from the fitted surface. If σ 2 is small, parameters β 0, β 1,..., β k will be reliably estimated

More information

amleague PROFESSIONAL PERFORMANCE DATA

amleague PROFESSIONAL PERFORMANCE DATA amleague PROFESSIONAL PERFORMANCE DATA APPENDIX 2 amleague Performance Ratios Definition Contents This document aims at describing the performance ratios calculated by amleague: 1. Standard Deviation 2.

More information

03 The full syllabus. 03 The full syllabus continued. For more information visit www.cimaglobal.com PAPER C03 FUNDAMENTALS OF BUSINESS MATHEMATICS

03 The full syllabus. 03 The full syllabus continued. For more information visit www.cimaglobal.com PAPER C03 FUNDAMENTALS OF BUSINESS MATHEMATICS 0 The full syllabus 0 The full syllabus continued PAPER C0 FUNDAMENTALS OF BUSINESS MATHEMATICS Syllabus overview This paper primarily deals with the tools and techniques to understand the mathematics

More information

Risk and return (1) Class 9 Financial Management, 15.414

Risk and return (1) Class 9 Financial Management, 15.414 Risk and return (1) Class 9 Financial Management, 15.414 Today Risk and return Statistics review Introduction to stock price behavior Reading Brealey and Myers, Chapter 7, p. 153 165 Road map Part 1. Valuation

More information

DOES IT PAY TO HAVE FAT TAILS? EXAMINING KURTOSIS AND THE CROSS-SECTION OF STOCK RETURNS

DOES IT PAY TO HAVE FAT TAILS? EXAMINING KURTOSIS AND THE CROSS-SECTION OF STOCK RETURNS DOES IT PAY TO HAVE FAT TAILS? EXAMINING KURTOSIS AND THE CROSS-SECTION OF STOCK RETURNS By Benjamin M. Blau 1, Abdullah Masud 2, and Ryan J. Whitby 3 Abstract: Xiong and Idzorek (2011) show that extremely

More information

Directions for using SPSS

Directions for using SPSS Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...

More information

Chicago Booth BUSINESS STATISTICS 41000 Final Exam Fall 2011

Chicago Booth BUSINESS STATISTICS 41000 Final Exam Fall 2011 Chicago Booth BUSINESS STATISTICS 41000 Final Exam Fall 2011 Name: Section: I pledge my honor that I have not violated the Honor Code Signature: This exam has 34 pages. You have 3 hours to complete this

More information

1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number

1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number 1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x - x) B. x 3 x C. 3x - x D. x - 3x 2) Write the following as an algebraic expression

More information

EXCEL Tutorial: How to use EXCEL for Graphs and Calculations.

EXCEL Tutorial: How to use EXCEL for Graphs and Calculations. EXCEL Tutorial: How to use EXCEL for Graphs and Calculations. Excel is powerful tool and can make your life easier if you are proficient in using it. You will need to use Excel to complete most of your

More information

Advanced Statistical Analysis of Mortality. Rhodes, Thomas E. and Freitas, Stephen A. MIB, Inc. 160 University Avenue. Westwood, MA 02090

Advanced Statistical Analysis of Mortality. Rhodes, Thomas E. and Freitas, Stephen A. MIB, Inc. 160 University Avenue. Westwood, MA 02090 Advanced Statistical Analysis of Mortality Rhodes, Thomas E. and Freitas, Stephen A. MIB, Inc 160 University Avenue Westwood, MA 02090 001-(781)-751-6356 fax 001-(781)-329-3379 trhodes@mib.com Abstract

More information

3 The spreadsheet execution model and its consequences

3 The spreadsheet execution model and its consequences Paper SP06 On the use of spreadsheets in statistical analysis Martin Gregory, Merck Serono, Darmstadt, Germany 1 Abstract While most of us use spreadsheets in our everyday work, usually for keeping track

More information

Chapter 11. Topics Covered. Chapter 11 Objectives. Risk, Return, and Capital Budgeting

Chapter 11. Topics Covered. Chapter 11 Objectives. Risk, Return, and Capital Budgeting Chapter 11 Risk, Return, and Capital Budgeting Topics Covered Measuring Market Risk Portfolio Betas Risk and Return CAPM and Expected Return Security Market Line CAPM and Stock Valuation Chapter 11 Objectives

More information

Package dsmodellingclient

Package dsmodellingclient Package dsmodellingclient Maintainer Author Version 4.1.0 License GPL-3 August 20, 2015 Title DataSHIELD client site functions for statistical modelling DataSHIELD

More information

Traditional Conjoint Analysis with Excel

Traditional Conjoint Analysis with Excel hapter 8 Traditional onjoint nalysis with Excel traditional conjoint analysis may be thought of as a multiple regression problem. The respondent s ratings for the product concepts are observations on the

More information

The Volatility Index Stefan Iacono University System of Maryland Foundation

The Volatility Index Stefan Iacono University System of Maryland Foundation 1 The Volatility Index Stefan Iacono University System of Maryland Foundation 28 May, 2014 Mr. Joe Rinaldi 2 The Volatility Index Introduction The CBOE s VIX, often called the market fear gauge, measures

More information

Important Probability Distributions OPRE 6301

Important Probability Distributions OPRE 6301 Important Probability Distributions OPRE 6301 Important Distributions... Certain probability distributions occur with such regularity in real-life applications that they have been given their own names.

More information

Simple Methods and Procedures Used in Forecasting

Simple Methods and Procedures Used in Forecasting Simple Methods and Procedures Used in Forecasting The project prepared by : Sven Gingelmaier Michael Richter Under direction of the Maria Jadamus-Hacura What Is Forecasting? Prediction of future events

More information

Multi scale random field simulation program

Multi scale random field simulation program Multi scale random field simulation program 1.15. 2010 (Updated 12.22.2010) Andrew Seifried, Stanford University Introduction This is a supporting document for the series of Matlab scripts used to perform

More information

International Stock Market Integration: A Dynamic General Equilibrium Approach

International Stock Market Integration: A Dynamic General Equilibrium Approach International Stock Market Integration: A Dynamic General Equilibrium Approach Harjoat S. Bhamra London Business School 2003 Outline of talk 1 Introduction......................... 1 2 Economy...........................

More information

17. SIMPLE LINEAR REGRESSION II

17. SIMPLE LINEAR REGRESSION II 17. SIMPLE LINEAR REGRESSION II The Model In linear regression analysis, we assume that the relationship between X and Y is linear. This does not mean, however, that Y can be perfectly predicted from X.

More information

Ordinal Regression. Chapter

Ordinal Regression. Chapter Ordinal Regression Chapter 4 Many variables of interest are ordinal. That is, you can rank the values, but the real distance between categories is unknown. Diseases are graded on scales from least severe

More information

Interpretation of Somers D under four simple models

Interpretation of Somers D under four simple models Interpretation of Somers D under four simple models Roger B. Newson 03 September, 04 Introduction Somers D is an ordinal measure of association introduced by Somers (96)[9]. It can be defined in terms

More information

hp calculators HP 50g Trend Lines The STAT menu Trend Lines Practice predicting the future using trend lines

hp calculators HP 50g Trend Lines The STAT menu Trend Lines Practice predicting the future using trend lines The STAT menu Trend Lines Practice predicting the future using trend lines The STAT menu The Statistics menu is accessed from the ORANGE shifted function of the 5 key by pressing Ù. When pressed, a CHOOSE

More information

Microsoft Excel Tutorial

Microsoft Excel Tutorial Microsoft Excel Tutorial by Dr. James E. Parks Department of Physics and Astronomy 401 Nielsen Physics Building The University of Tennessee Knoxville, Tennessee 37996-1200 Copyright August, 2000 by James

More information

Using Excel s Analysis ToolPak Add-In

Using Excel s Analysis ToolPak Add-In Using Excel s Analysis ToolPak Add-In S. Christian Albright, September 2013 Introduction This document illustrates the use of Excel s Analysis ToolPak add-in for data analysis. The document is aimed at

More information

Dimensionality Reduction: Principal Components Analysis

Dimensionality Reduction: Principal Components Analysis Dimensionality Reduction: Principal Components Analysis In data mining one often encounters situations where there are a large number of variables in the database. In such situations it is very likely

More information

Internet Appendix to CAPM for estimating cost of equity capital: Interpreting the empirical evidence

Internet Appendix to CAPM for estimating cost of equity capital: Interpreting the empirical evidence Internet Appendix to CAPM for estimating cost of equity capital: Interpreting the empirical evidence This document contains supplementary material to the paper titled CAPM for estimating cost of equity

More information

HLM software has been one of the leading statistical packages for hierarchical

HLM software has been one of the leading statistical packages for hierarchical Introductory Guide to HLM With HLM 7 Software 3 G. David Garson HLM software has been one of the leading statistical packages for hierarchical linear modeling due to the pioneering work of Stephen Raudenbush

More information

Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model

Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written

More information

Chapter 11, Risk and Return

Chapter 11, Risk and Return Chapter 11, Risk and Return 1. A portfolio is. A) a group of assets, such as stocks and bonds, held as a collective unit by an investor B) the expected return on a risky asset C) the expected return on

More information

MICROSOFT EXCEL 2007-2010 FORECASTING AND DATA ANALYSIS

MICROSOFT EXCEL 2007-2010 FORECASTING AND DATA ANALYSIS MICROSOFT EXCEL 2007-2010 FORECASTING AND DATA ANALYSIS Contents NOTE Unless otherwise stated, screenshots in this book were taken using Excel 2007 with a blue colour scheme and running on Windows Vista.

More information

Joint Exam 1/P Sample Exam 1

Joint Exam 1/P Sample Exam 1 Joint Exam 1/P Sample Exam 1 Take this practice exam under strict exam conditions: Set a timer for 3 hours; Do not stop the timer for restroom breaks; Do not look at your notes. If you believe a question

More information

Logistic Regression (1/24/13)

Logistic Regression (1/24/13) STA63/CBB540: Statistical methods in computational biology Logistic Regression (/24/3) Lecturer: Barbara Engelhardt Scribe: Dinesh Manandhar Introduction Logistic regression is model for regression used

More information

1 Sufficient statistics

1 Sufficient statistics 1 Sufficient statistics A statistic is a function T = rx 1, X 2,, X n of the random sample X 1, X 2,, X n. Examples are X n = 1 n s 2 = = X i, 1 n 1 the sample mean X i X n 2, the sample variance T 1 =

More information

Simple Linear Regression Inference

Simple Linear Regression Inference Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation

More information

Notes on Continuous Random Variables

Notes on Continuous Random Variables Notes on Continuous Random Variables Continuous random variables are random quantities that are measured on a continuous scale. They can usually take on any value over some interval, which distinguishes

More information

Least Squares Estimation

Least Squares Estimation Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David

More information

Part 2: Analysis of Relationship Between Two Variables

Part 2: Analysis of Relationship Between Two Variables Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable

More information

So, using the new notation, P X,Y (0,1) =.08 This is the value which the joint probability function for X and Y takes when X=0 and Y=1.

So, using the new notation, P X,Y (0,1) =.08 This is the value which the joint probability function for X and Y takes when X=0 and Y=1. Joint probabilit is the probabilit that the RVs & Y take values &. like the PDF of the two events, and. We will denote a joint probabilit function as P,Y (,) = P(= Y=) Marginal probabilit of is the probabilit

More information

Holding Period Return. Return, Risk, and Risk Aversion. Percentage Return or Dollar Return? An Example. Percentage Return or Dollar Return? 10% or 10?

Holding Period Return. Return, Risk, and Risk Aversion. Percentage Return or Dollar Return? An Example. Percentage Return or Dollar Return? 10% or 10? Return, Risk, and Risk Aversion Holding Period Return Ending Price - Beginning Price + Intermediate Income Return = Beginning Price R P t+ t+ = Pt + Dt P t An Example You bought IBM stock at $40 last month.

More information

FEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL

FEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL FEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL STATIsTICs 4 IV. RANDOm VECTORs 1. JOINTLY DIsTRIBUTED RANDOm VARIABLEs If are two rom variables defined on the same sample space we define the joint

More information