Notice that X2 and Y2 are skewed. Taking the SQRT of Y2 reduces the skewness greatly.
|
|
- Percival Palmer
- 6 years ago
- Views:
Transcription
1 Notice that X2 and Y2 are skewed. Taking the SQRT of Y2 reduces the skewness greatly. The MEANS Procedure Variable Mean Std Dev Minimum Maximum Skewness ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ X Y X Y Y2_SQRT ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Predicting Y from X. Number of Observations Read 200 Number of Observations Used 200 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept <.0001 X <.0001
2 Predicting Y from X. ƒƒƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒƒƒ RESIDUAL 30 ˆ ˆ 20 ˆ ˆ ˆ ˆ R e s i d 0 ˆ ˆ u a l ˆ ˆ ˆ 1 1 ˆ -30 ˆ ˆ ŠƒƒƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒƒƒŒ Predicted Value of Y PRED
3 Distribution of the residuals is normal. The UNIVARIATE Procedure Variable: Y_Resid (Residual) Moments N 200 Sum Weights 200 Mean 0 Sum Observations 0 Std Deviation Variance Skewness Kurtosis Tests for Normality Test --Statistic p Value Shapiro-Wilk W Pr < W Kolmogorov-Smirnov D Pr > D > Cramer-von Mises W-Sq Pr > W-Sq > Anderson-Darling A-Sq Pr > A-Sq > Stem Leaf # Boxplot *--+--*
4 Predicting Y from skewed X2. Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept <.0001 X <.0001
5 Predicting Y from skewed X2. ƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒ RESIDUAL 30 ˆ ˆ 20 ˆ ˆ ˆ ˆ R e s i d 0 ˆ ˆ u a l ˆ ˆ ˆ 1 1 ˆ -30 ˆ ˆ ŠƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒˆƒƒƒƒƒŒ Predicted Value of Y PRED
6 Distribution of the residuals is normal. The UNIVARIATE Procedure Variable: Y_Resid (Residual) Moments N 200 Sum Weights 200 Mean 0 Sum Observations 0 Std Deviation Variance Skewness Kurtosis Tests for Normality Test --Statistic p Value Shapiro-Wilk W Pr < W Kolmogorov-Smirnov D Pr > D > Cramer-von Mises W-Sq Pr > W-Sq > Anderson-Darling A-Sq Pr > A-Sq > Stem Leaf # Boxplot *--+--*
7 Predicting skewed Y from X. 2 Number of Observations Read 200 Number of Observations Used 200 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept X <.0001
8 Predicting skewed Y from X. 2 ƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒ RESIDUAL 100 ˆ ˆ 80 ˆ 1 ˆ 60 ˆ ˆ ˆ ˆ R e s i d 20 ˆ ˆ u a l ˆ ˆ ˆ ˆ ˆ ˆ -60 ˆ ˆ ŠƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒŒ Predicted Value of Y2 PRED
9 Distribution of the residuals is skewed. The UNIVARIATE Procedure Variable: Y_Resid (Residual) Moments N 200 Sum Weights 200 Mean 0 Sum Observations 0 Std Deviation Variance Skewness Kurtosis Uncorrected SS Corrected SS Coeff Variation. Std Error Mean Tests for Normality Test --Statistic p Value Shapiro-Wilk W Pr < W < Kolmogorov-Smirnov D Pr > D Cramer-von Mises W-Sq Pr > W-Sq Anderson-Darling A-Sq Pr > A-Sq < Stem Leaf # Boxplot *-----* Multiply Stem.Leaf by 10**+1
10 Predicting transformed Y2 from X. 2_SQRT Number of Observations Read 200 Number of Observations Used 200 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept X <.0001
11 Predicting transformed Y2 from X. 2_SQRT ƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒ RESIDUAL 6 ˆ ˆ 4 ˆ 1 1 ˆ R e 2 ˆ ˆ s i d u a l 0 ˆ ˆ ˆ ˆ ˆ 1 ˆ ŠƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒŒ Predicted Value of Y2_SQRT PRED
12 Distribution of the residuals is nearly normal. The UNIVARIATE Procedure Variable: Y_Resid (Residual) Moments N 200 Sum Weights 200 Mean 0 Sum Observations 0 Std Deviation Variance Skewness Kurtosis Tests for Normality Test --Statistic p Value Shapiro-Wilk W Pr < W Kolmogorov-Smirnov D Pr > D > Cramer-von Mises W-Sq Pr > W-Sq > Anderson-Darling A-Sq Pr > A-Sq > Distribution of the residuals is nearly normal. The UNIVARIATE Procedure Variable: Y_Resid (Residual) Stem Leaf # Boxplot *-----*
13 New Data Set. The MEANS Procedure Variable Mean Std Dev Skewness ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Y X ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Predict Y from X. Number of Observations Read 100 Number of Observations Used 100 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept <.0001 X
14 Predict Y from X. ˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒ RESIDUAL 30 ˆ ˆ ˆ 3 ˆ ˆ 3 ˆ R e s i 2 2 d 0 ˆ ˆ u a 2 2 l ˆ 2 3 ˆ ˆ ˆ 2-30 ˆ ˆ ŠˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒŒ Predicted Value of Y PRED
15 Marginal distribution of residuals does not show the problem. The UNIVARIATE Procedure Variable: residuals (Residual) Moments N 100 Sum Weights 100 Mean 0 Sum Observations 0 Std Deviation Variance Skewness Kurtosis Tests for Normality Test --Statistic p Value Shapiro-Wilk W Pr < W Kolmogorov-Smirnov D Pr > D > Cramer-von Mises W-Sq Pr > W-Sq > Anderson-Darling A-Sq Pr > A-Sq > Stem Leaf # Boxplot *-----* Multiply Stem.Leaf by 10**+1
16 Polynomial regression: Quadratic. Number of Observations Read 100 Number of Observations Used 100 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept <.0001 X <.0001 X_SQ <.0001
17 Polynomial regression: Quadratic. ƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒ RESIDUAL 20 ˆ 1 1 ˆ ˆ ˆ R e 0 ˆ ˆ s i d u a l -10 ˆ 1 2 ˆ -20 ˆ 1 1 ˆ ˆ ˆ ŠƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒŒ Predicted Value of Y PRED
18 Polynomial regression: Quadratic. ƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒƒ Y 130 ˆ ˆ 120 ˆ. ˆ ˆ.. ˆ X ˆ?... ˆ ˆ.... ˆ..??...?.. 80 ˆ.... ˆ 70 ˆ ˆ 60 ˆ ˆ ŠƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒˆƒƒƒƒƒŒ X
19 Another new data set. The MEANS Procedure Variable Mean Std Dev Minimum Maximum Skewness ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ X Y ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ Heteroscedasticity. Number of Observations Read 500 Number of Observations Used 500 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model <.0001 Error Corrected Total Root MSE R-Square Dependent Mean Adj R-Sq Coeff Var Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > t Intercept <.0001 X <.0001
20 Heteroscedasticity. ƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒ RESIDUAL 600 ˆ.. ˆ ˆ.. ˆ ˆ ˆ R e s i d 0 ˆ ˆ u a l ˆ ˆ ˆ. ˆ ˆ. ˆ ŠƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒƒƒƒˆƒƒƒŒ Predicted Value of Y PRED
Topic 8: Model Diagnostics
Topic 8: Model Diagnostics Outline Diagnostics to check model assumptions Diagnostics concerning X Diagnostics using the residuals Diagnostics and remedial measures Diagnostics: look at the data to diagnose
More informationChapter 11 : Model checking and refinement An example: Blood-brain barrier study on rats
EXST3201 Chapter 11b Geaghan Fall 2005: Page 1 Chapter 11 : Model checking and refinement An example: Blood-brain barrier study on rats This study investigates the permeability of the blood-brain barrier
More information1. Distinguish three missing data mechanisms:
1 DATA SCREENING I. Preliminary inspection of the raw data make sure that there are no obvious coding errors (e.g., all values for the observed variables are in the admissible range) and that all variables
More informationDescriptive Analysis
Descriptive Analysis HERTANTO WAHYU SUBAGIO Univariate Analysis Univariate analysis involves the examination across cases of one variable at a time. There are three major characteristics of a single variable
More informationGGraph. Males Only. Premium. Experience. GGraph. Gender. 1 0: R 2 Linear = : R 2 Linear = Page 1
GGraph 9 Gender : R Linear =.43 : R Linear =.769 8 7 6 5 4 3 5 5 Males Only GGraph Page R Linear =.43 R Loess 9 8 7 6 5 4 5 5 Explore Case Processing Summary Cases Valid Missing Total N Percent N Percent
More informationEXST7015: Multiple Regression from Snedecor & Cochran (1967) RAW DATA LISTING
Multiple (Linear) Regression Introductory example Page 1 1 options ps=256 ls=132 nocenter nodate nonumber; 3 DATA ONE; 4 TITLE1 ''; 5 INPUT X1 X2 X3 Y; 6 **** LABEL Y ='Plant available phosphorus' 7 X1='Inorganic
More informationThe SAS System 11:03 Monday, November 11,
The SAS System 11:3 Monday, November 11, 213 1 The CONTENTS Procedure Data Set Name BIO.AUTO_PREMIUMS Observations 5 Member Type DATA Variables 3 Engine V9 Indexes Created Monday, November 11, 213 11:4:19
More informationEmpirical Rule (P148)
Interpreting the Standard Deviation Numerical Descriptive Measures for Quantitative data III Dr. Tom Ilvento FREC 408 We can use the standard deviation to express the proportion of cases that might fall
More informationIntroduction to Statistical Data Analysis II
Introduction to Statistical Data Analysis II JULY 2011 Afsaneh Yazdani Preface Major branches of Statistics: - Descriptive Statistics - Inferential Statistics Preface What is Inferential Statistics? Preface
More informationTwo Way ANOVA in R Solutions
Two Way ANOVA in R Solutions Solutions to exercises found here # Exercise 1 # #Read in the moth experiment data setwd("h:/datasets") moth.experiment = read.csv("moth trap experiment.csv", header = TRUE)
More information2018 AAPM: Normal and non normal distributions: Why understanding distributions are important when designing experiments and analyzing data
Statistical Failings that Keep Us All in the Dark Normal and non normal distributions: Why understanding distributions are important when designing experiments and Conflict of Interest Disclosure I have
More informationLAMPIRAN IV PENGUJIAN HIPOTESIS
LAMPIRAN IV PENGUJIAN HIPOTESIS 89 NORMALITAS I Explore Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent 390 00.0% 0.0% 390 00.0% Descriptives Mean 95% Confidence Interval
More informationSAS Simple Linear Regression Example
SAS Simple Linear Regression Example This handout gives examples of how to use SAS to generate a simple linear regression plot, check the correlation between two variables, fit a simple linear regression
More informationFinancial Time Series and Their Characteristics
Financial Time Series and Their Characteristics Egon Zakrajšek Division of Monetary Affairs Federal Reserve Board Summer School in Financial Mathematics Faculty of Mathematics & Physics University of Ljubljana
More informationSolutions for Session 5: Linear Models
Solutions for Session 5: Linear Models 30/10/2018. do solution.do. global basedir http://personalpages.manchester.ac.uk/staff/mark.lunt. global datadir $basedir/stats/5_linearmodels1/data. use $datadir/anscombe.
More informationLAMPIRAN 1: OUTPUT SPSS
LAMPIRAN : OUTPUT SPSS Statistik Deskriptif Descriptive Statistics N Minimum Maximum Mean Std. Deviation Daabs 95.0022.0902.03744.0226569 CAR 95.0789.339.43306.0463305 RORA 95 -.447.8074.052244.29802 ROA
More informationStat 328, Summer 2005
Stat 328, Summer 2005 Exam #2, 6/18/05 Name (print) UnivID I have neither given nor received any unauthorized aid in completing this exam. Signed Answer each question completely showing your work where
More informationReview: Chebyshev s Rule. Measures of Dispersion II. Review: Empirical Rule. Review: Empirical Rule. Auto Batteries Example, p 59.
Review: Chebyshev s Rule Measures of Dispersion II Tom Ilvento STAT 200 Is based on a mathematical theorem for any data At least ¾ of the measurements will fall within ± 2 standard deviations from the
More informationTable of Contents. New to the Second Edition... Chapter 1: Introduction : Social Research...
iii Table of Contents Preface... xiii Purpose... xiii Outline of Chapters... xiv New to the Second Edition... xvii Acknowledgements... xviii Chapter 1: Introduction... 1 1.1: Social Research... 1 Introduction...
More information**BEGINNING OF EXAMINATION** A random sample of five observations from a population is:
**BEGINNING OF EXAMINATION** 1. You are given: (i) A random sample of five observations from a population is: 0.2 0.7 0.9 1.1 1.3 (ii) You use the Kolmogorov-Smirnov test for testing the null hypothesis,
More informationHomework 0 Key (not to be handed in) due? Jan. 10
Homework 0 Key (not to be handed in) due? Jan. 10 The results of running diamond.sas is listed below: Note: I did slightly reduce the size of some of the graphs so that they would fit on the page. The
More informationTime series data: Part 2
Plot of Epsilon over Time -- Case 1 1 Time series data: Part Epsilon - 1 - - - -1 1 51 7 11 1 151 17 Time period Plot of Epsilon over Time -- Case Plot of Epsilon over Time -- Case 3 1 3 1 Epsilon - Epsilon
More informationThe FREQ Procedure. Table of Sex by Gym Sex(Sex) Gym(Gym) No Yes Total Male Female Total
Jenn Selensky gathered data from students in an introduction to psychology course. The data are weights, sex/gender, and whether or not the student worked-out in the gym. Here is the output from a 2 x
More informationAn Insight Into Heavy-Tailed Distribution
An Insight Into Heavy-Tailed Distribution Annapurna Ravi Ferry Butar Butar ABSTRACT The heavy-tailed distribution provides a much better fit to financial data than the normal distribution. Modeling heavy-tailed
More informationValid Missing Total. N Percent N Percent N Percent , ,0% 0,0% 2 100,0% 1, ,0% 0,0% 2 100,0% 2, ,0% 0,0% 5 100,0%
dimension1 GET FILE= validacaonestscoremédico.sav' (só com os 59 doentes) /COMPRESSED. SORT CASES BY UMcpEVA (D). EXAMINE VARIABLES=UMcpEVA BY NoRespostasSignif /PLOT BOXPLOT HISTOGRAM NPPLOT /COMPARE
More information1.1 ANNUAL PRICE MODEL
1.1 ANNUAL PRICE MODEL Annual ex-vessel price model is updated each year to take into account the recent changes in sea scallop markets both domestically and internationally. This model estimates the degree
More informationAppendix A (Pornprasertmanit & Little, in press) Mathematical Proof
Appendix A (Pornprasertmanit & Little, in press) Mathematical Proof Definition We begin by defining notations that are needed for later sections. First, we define moment as the mean of a random variable
More informationOne Way ANOVA with Tukey Post hoc. Case Processing Summary
EXAMINE VARIABLES=Score BY Group /PLOT BOXPLOT NPPLOT /COMPARE GROUP /STATISTICS DESCRIPTIVES /CINTERVAL 95 /MISSING LISTWISE /NOTOTAL. Explore Group One Way ANOVA with Tukey Post hoc Case Processing Summary
More informationMonetary Economics Risk and Return, Part 2. Gerald P. Dwyer Fall 2015
Monetary Economics Risk and Return, Part 2 Gerald P. Dwyer Fall 2015 Reading Malkiel, Part 2, Part 3 Malkiel, Part 3 Outline Returns and risk Overall market risk reduced over longer periods Individual
More informationLoss Simulation Model Testing and Enhancement
Loss Simulation Model Testing and Enhancement Casualty Loss Reserve Seminar By Kailan Shang Sept. 2011 Agenda Research Overview Model Testing Real Data Model Enhancement Further Development Enterprise
More informationYou created this PDF from an application that is not licensed to print to novapdf printer (http://www.novapdf.com)
Monday October 3 10:11:57 2011 Page 1 (R) / / / / / / / / / / / / Statistics/Data Analysis Education Box and save these files in a local folder. name:
More information9. Appendixes. Page 73 of 95
9. Appendixes Appendix A: Construction cost... 74 Appendix B: Cost of capital... 75 Appendix B.1: Beta... 75 Appendix B.2: Cost of equity... 77 Appendix C: Geometric Brownian motion... 78 Appendix D: Static
More informationPerformance of Credit Risk Management in Indian Commercial Banks
Int. J. Manag. Bus. Res., 5 (3), 169-188, Summer 2015 IAU Performance of Credit Risk Management in Indian Commercial Banks A. Singh Mewar University, Chittorgarh, Rajasthan, India Received 23 March 2014,
More informationSPSS t tests (and NP Equivalent)
SPSS t tests (and NP Equivalent) Descriptive Statistics To get all the descriptive statistics you need: Analyze > Descriptive Statistics>Explore. Enter the IV into the Factor list and the DV into the Dependent
More informationHandout seminar 6, ECON4150
Handout seminar 6, ECON4150 Herman Kruse March 17, 2013 Introduction - list of commands This week, we need a couple of new commands in order to solve all the problems. hist var1 if var2, options - creates
More informationtm / / / / / / / / / / / / Statistics/Data Analysis User: Klick Project: Limited Dependent Variables{space -6}
PS 4 Monday August 16 01:00:42 2010 Page 1 tm / / / / / / / / / / / / Statistics/Data Analysis User: Klick Project: Limited Dependent Variables{space -6} log: C:\web\PS4log.smcl log type: smcl opened on:
More informationAre the movements of stocks, bonds, and housing linked? Zachary D Easterling Department of Economics The University of Akron
Easerling 1 Are the movements of stocks, bonds, and housing linked? Zachary D Easterling 1140324 Department of Economics The University of Akron One of the key ideas in monetary economics is that the prices
More informationJoseph O. Marker Marker Actuarial Services, LLC and University of Michigan CLRS 2011 Meeting. J. Marker, LSMWP, CLRS 1
Joseph O. Marker Marker Actuarial Services, LLC and University of Michigan CLRS 2011 Meeting J. Marker, LSMWP, CLRS 1 Expected vs Actual Distribu3on Test distribu+ons of: Number of claims (frequency) Size
More informationMODEL SELECTION CRITERIA IN R:
1. R 2 statistics We may use MODEL SELECTION CRITERIA IN R R 2 = SS R SS T = 1 SS Res SS T or R 2 Adj = 1 SS Res/(n p) SS T /(n 1) = 1 ( ) n 1 (1 R 2 ). n p where p is the total number of parameters. R
More informationLAMPIRAN PERHITUNGAN EVIEWS
LAMPIRAN PERHITUNGAN EVIEWS DESCRIPTIVE PK PDRB TP TKM Mean 12.22450 10.16048 14.02443 12.63677 Median 12.41945 10.09179 14.22736 12.61400 Maximum 13.53955 12.73508 15.62581 13.16721 Minimum 10.34509 8.579417
More informationApplication of value at risk on Moroccan exchange rates
2018; 3(1): 118-125 ISSN: 2456-1452 Maths 2018; 3(1): 118-125 2018 Stats & Maths www.mathsjournal.com Received: 20-11-2017 Accepted: 22-12-2017 Karima Lamsaddak IbnTofail University, Faculty of Sciences,
More informationHeteroskedasticity. . reg wage black exper educ married tenure
Heteroskedasticity. reg Source SS df MS Number of obs = 2,380 -------------+---------------------------------- F(2, 2377) = 72.38 Model 14.4018246 2 7.20091231 Prob > F = 0.0000 Residual 236.470024 2,377.099482551
More informationFrequency Distribution Models 1- Probability Density Function (PDF)
Models 1- Probability Density Function (PDF) What is a PDF model? A mathematical equation that describes the frequency curve or probability distribution of a data set. Why modeling? It represents and summarizes
More informationThe data definition file provided by the authors is reproduced below: Obs: 1500 home sales in Stockton, CA from Oct 1, 1996 to Nov 30, 1998
Economics 312 Sample Project Report Jeffrey Parker Introduction This project is based on Exercise 2.12 on page 81 of the Hill, Griffiths, and Lim text. It examines how the sale price of houses in Stockton,
More informationF^3: F tests, Functional Forms and Favorite Coefficient Models
F^3: F tests, Functional Forms and Favorite Coefficient Models Favorite coefficient model: otherteams use "nflpricedata Bdta", clear *Favorite coefficient model: otherteams reg rprice pop pop2 rpci wprcnt1
More information*1A. Basic Descriptive Statistics sum housereg drive elecbill affidavit witness adddoc income male age literacy educ occup cityyears if control==1
*1A Basic Descriptive Statistics sum housereg drive elecbill affidavit witness adddoc income male age literacy educ occup cityyears if control==1 Variable Obs Mean Std Dev Min Max --- housereg 21 2380952
More informationPower comparisons of some selected normality tests
Proceedings of the Regional Conference on Statistical Sciences 010 (RCSS 10) June 010, 16-138 Power comparisons of some selected normality tests Nornadiah Mohd Razali 1 Yap Bee Wah 1, Faculty of Computer
More informationHASIL PENELITIAN BERUPA OUTPUT SPSS
VARIABEL INDEPENDEN HASIL PENELITIAN BERUPA OUTPUT SPSS 1. RATA-RATA ASUPAN ENERGI UJI NORMALITAS DATA Rata2AsupanE 70 100.0% 0.0% 70 100.0% Descriptives Rata2AsupanE 1375.77 44.068 Lower Bound 1287.85
More informationLecture 18 Section Mon, Feb 16, 2009
The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Feb 16, 2009 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income
More informationLecture 18 Section Mon, Sep 29, 2008
The s the Lecture 18 Section 5.3.4 Hampden-Sydney College Mon, Sep 29, 2008 Outline The s the 1 2 3 The 4 s 5 the 6 The s the Exercise 5.12, page 333. The five-number summary for the distribution of income
More informationAppendixes Appendix 1 Data of Dependent Variables and Independent Variables Period
Appendixes Appendix 1 Data of Dependent Variables and Independent Variables Period 1-15 1 ROA INF KURS FG January 1,3,7 9 -,19 February 1,79,5 95 3,1 March 1,3,7 91,95 April 1,79,1 919,71 May 1,99,7 955
More informationİnsan TUNALI 8 November 2018 Econ 511: Econometrics I. ASSIGNMENT 7 STATA Supplement
İnsan TUNALI 8 November 2018 Econ 511: Econometrics I ASSIGNMENT 7 STATA Supplement. use "F:\COURSES\GRADS\ECON511\SHARE\wages1.dta", clear. generate =ln(wage). scatter sch Q. Do you see a relationship
More informationChapter 11 Part 6. Correlation Continued. LOWESS Regression
Chapter 11 Part 6 Correlation Continued LOWESS Regression February 17, 2009 Goal: To review the properties of the correlation coefficient. To introduce you to the various tools that can be used to decide
More informationEmpirical Asset Pricing for Tactical Asset Allocation
Introduction Process Model Conclusion Department of Finance The University of Connecticut School of Business stephen.r.rush@gmail.com May 10, 2012 Background Portfolio Managers Want to justify fees with
More informationFinancial Time Series Analysis (FTSA)
Financial Time Series Analysis (FTSA) Lecture 6: Conditional Heteroscedastic Models Few models are capable of generating the type of ARCH one sees in the data.... Most of these studies are best summarized
More informationIntro. Econometrics Fall 2015
ECO 5350 Prof. Tom Fomby Intro. Econometrics Fall 2015 MIDTERM EXAM TAKE-HOME PART KEY Assignment of Points: Q5.5 (2, 2, 3, 3) = 10 Q5.9 (2, 3, 2, 3) = 10 Q5.15 (2, 3, 3) = 8 Q5.18 (3, 3) = 6 Total = 34
More informationTSP-Programm zu Abschnitt 6.1.: Zeitreihenanalyse
TSP-Programm zu Abschnitt 6.1.: Zeitreihenanalyse ------------------------------------- this copy licensed for use by: TSP/GiveWin update 4/07#50AGT0407 ------------------------------------- TSP Version
More informationData analysis methods in weather and climate research
Data analysis methods in weather and climate research Dr. David B. Stephenson Department of Meteorology University of Reading www.met.rdg.ac.uk/cag 5. Parameter estimation Fitting probability models he
More informationTechnology Arts Sciences Cologne Faculty of Economics, Business and Law Prof. Dr. Arrenberg Room 221, Tel
Technology Arts Sciences Cologne Faculty of Economics, Business and Law Prof. Dr. Arrenberg Room 221, Tel. 39 14 jutta.arrenberg@th-koeln.de Exercises Quantitative Methods Worksheet: Goodness-of-fit Test
More informationAssignment #5 Solutions: Chapter 14 Q1.
Assignment #5 Solutions: Chapter 14 Q1. a. R 2 is.037 and the adjusted R 2 is.033. The adjusted R 2 value becomes particularly important when there are many independent variables in a multiple regression
More informationQuestion 1a 1b 1c 1d 1e 1f 2a 2b 2c 2d 3a 3b 3c 3d M ult:choice Points
Economics 102: Analysis of Economic Data Cameron Spring 2015 April 23 Department of Economics, U.C.-Davis First Midterm Exam (Version A) Compulsory. Closed book. Total of 30 points and worth 22.5% of course
More informationData Distributions and Normality
Data Distributions and Normality Definition (Non)Parametric Parametric statistics assume that data come from a normal distribution, and make inferences about parameters of that distribution. These statistical
More informationA New Hybrid Estimation Method for the Generalized Pareto Distribution
A New Hybrid Estimation Method for the Generalized Pareto Distribution Chunlin Wang Department of Mathematics and Statistics University of Calgary May 18, 2011 A New Hybrid Estimation Method for the GPD
More informationConditional Heteroscedasticity and Testing of the Granger Causality: Case of Slovakia. Michaela Chocholatá
Conditional Heteroscedasticity and Testing of the Granger Causality: Case of Slovakia Michaela Chocholatá The main aim of presentation: to analyze the relationships between the SKK/USD exchange rate and
More informationWhere s the Beef Does the Mack Method produce an undernourished range of possible outcomes?
Where s the Beef Does the Mack Method produce an undernourished range of possible outcomes? Daniel Murphy, FCAS, MAAA Trinostics LLC CLRS 2009 In the GIRO Working Party s simulation analysis, actual unpaid
More informationDavid Tenenbaum GEOG 090 UNC-CH Spring 2005
Simple Descriptive Statistics Review and Examples You will likely make use of all three measures of central tendency (mode, median, and mean), as well as some key measures of dispersion (standard deviation,
More informationMonetary Economics Measuring Asset Returns. Gerald P. Dwyer Fall 2015
Monetary Economics Measuring Asset Returns Gerald P. Dwyer Fall 2015 WSJ Readings Readings this lecture, Cuthbertson Ch. 9 Readings next lecture, Cuthbertson, Chs. 10 13 Measuring Asset Returns Outline
More informationMean GMM. Standard error
Table 1 Simple Wavelet Analysis for stocks in the S&P 500 Index as of December 31 st 1998 ^ Shapiro- GMM Normality 6 0.9664 0.00281 11.36 4.14 55 7 0.9790 0.00300 56.58 31.69 45 8 0.9689 0.00319 403.49
More informationMeasures of Variability
Sample I: 30, 35, 40, 45, 50, 55, 60, 65, 70 Sample II: 30, 41, 48, 49, 50, 51, 52, 59, 70 Sample III: 41, 45, 48, 49, 50, 51, 52, 55, 59 Sample I: 30, 35, 40, 45, 50, 55, 60, 65, 70 Sample II: 30, 41,
More informationExploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) Introduction A Need to Explore Your Data The first step of data analysis should always be a detailed examination of the data. The examination of your data is called Exploratory
More informationLampiran 1 : Grafik Data HIV Asli
Lampiran 1 : Grafik Data HIV Asli 70 60 50 Penderita 40 30 20 10 2007 2008 2009 2010 2011 Tahun HIV Mean 34.15000 Median 31.50000 Maximum 60.00000 Minimum 19.00000 Std. Dev. 10.45057 Skewness 0.584866
More informationDummy variables 9/22/2015. Are wages different across union/nonunion jobs. Treatment Control Y X X i identifies treatment
Dummy variables Treatment 22 1 1 Control 3 2 Y Y1 0 1 2 Y X X i identifies treatment 1 1 1 1 1 1 0 0 0 X i =1 if in treatment group X i =0 if in control H o : u n =u u Are wages different across union/nonunion
More informationu panel_lecture . sum
u panel_lecture sum Variable Obs Mean Std Dev Min Max datastre 639 9039644 6369418 900228 926665 year 639 1980 2584012 1976 1984 total_sa 639 9377839 3212313 682 441e+07 tot_fixe 639 5214385 1988422 642
More informationSubject CS1 Actuarial Statistics 1 Core Principles. Syllabus. for the 2019 exams. 1 June 2018
` Subject CS1 Actuarial Statistics 1 Core Principles Syllabus for the 2019 exams 1 June 2018 Copyright in this Core Reading is the property of the Institute and Faculty of Actuaries who are the sole distributors.
More informationHomework Problems Stat 479
Chapter 2 1. Model 1 is a uniform distribution from 0 to 100. Determine the table entries for a generalized uniform distribution covering the range from a to b where a < b. 2. Let X be a discrete random
More informationTwo-Sample T-Test for Superiority by a Margin
Chapter 219 Two-Sample T-Test for Superiority by a Margin Introduction This procedure provides reports for making inference about the superiority of a treatment mean compared to a control mean from data
More informationImpact of Household Income on Poverty Levels
Impact of Household Income on Poverty Levels ECON 3161 Econometrics, Fall 2015 Prof. Shatakshee Dhongde Group 8 Annie Strothmann Anne Marsh Samuel Brown Abstract: The relationship between poverty and household
More informationThe Spot Forward Exchange Rate Relation in Indian Foreign Exchange Market - An Analysis
MPRA Munich Personal RePEc Archive The Spot Forward Exchange Rate Relation in Indian Foreign Exchange Market - An Analysis Golaka Nath CCIL 9. October 2013 Online at http://mpra.ub.uni-muenchen.de/51591/
More information9/17/2015. Basic Statistics for the Healthcare Professional. Relax.it won t be that bad! Purpose of Statistic. Objectives
Basic Statistics for the Healthcare Professional 1 F R A N K C O H E N, M B B, M P A D I R E C T O R O F A N A L Y T I C S D O C T O R S M A N A G E M E N T, LLC Purpose of Statistic 2 Provide a numerical
More informationOverview/Outline. Moving beyond raw data. PSY 464 Advanced Experimental Design. Describing and Exploring Data The Normal Distribution
PSY 464 Advanced Experimental Design Describing and Exploring Data The Normal Distribution 1 Overview/Outline Questions-problems? Exploring/Describing data Organizing/summarizing data Graphical presentations
More informationThe Multivariate Regression Model
The Multivariate Regression Model Example Determinants of College GPA Sample of 4 Freshman Collect data on College GPA (4.0 scale) Look at importance of ACT Consider the following model CGPA ACT i 0 i
More informationIntegration of Financial Markets: A Study of Egypt and Palestine Stock Markets
Integration of Financial Markets: A Study of Egypt and Palestine Stock Markets Applied Finance and Accounting Vol. 3, No. 1, February 2017 ISSN 2374-2410 E-ISSN 2374-2429 Published by Redfame Publishing
More informationTwo-Sample T-Test for Non-Inferiority
Chapter 198 Two-Sample T-Test for Non-Inferiority Introduction This procedure provides reports for making inference about the non-inferiority of a treatment mean compared to a control mean from data taken
More informationEconometrics is. The estimation of relationships suggested by economic theory
Econometrics is Econometrics is The estimation of relationships suggested by economic theory Econometrics is The estimation of relationships suggested by economic theory The application of mathematical
More informationFinancial Returns. Dakota Wixom Quantitative Analyst QuantCourse.com INTRO TO PORTFOLIO RISK MANAGEMENT IN PYTHON
INTRO TO PORTFOLIO RISK MANAGEMENT IN PYTHON Financial Returns Dakota Wixom Quantitative Analyst QuantCourse.com Course Overview Learn how to analyze investment return distributions, build portfolios and
More informationis the bandwidth and controls the level of smoothing of the estimator, n is the sample size and
Paper PH100 Relationship between Total charges and Reimbursements in Outpatient Visits Using SAS GLIMMIX Chakib Battioui, University of Louisville, Louisville, KY ABSTRACT The purpose of this paper is
More informationExample 1 of econometric analysis: the Market Model
Example 1 of econometric analysis: the Market Model IGIDR, Bombay 14 November, 2008 The Market Model Investors want an equation predicting the return from investing in alternative securities. Return is
More informationTwo Hours. Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER. 22 January :00 16:00
Two Hours MATH38191 Mathematical formula books and statistical tables are to be provided THE UNIVERSITY OF MANCHESTER STATISTICAL MODELLING IN FINANCE 22 January 2015 14:00 16:00 Answer ALL TWO questions
More informationExchange rate. Level and volatility FxRates
Comentario Económico y Financiero Carlos Sánchez Cerón VaR Financiero Exchange rate. Level and volatility Source: During 2015, the dollar gradually rose 13.9 MXN/USA from December 2014 to 17.21 at the
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay. Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay Midterm ChicagoBooth Honor Code: I pledge my honor that I have not violated the Honor Code during this
More information2 Exploring Univariate Data
2 Exploring Univariate Data A good picture is worth more than a thousand words! Having the data collected we examine them to get a feel for they main messages and any surprising features, before attempting
More informationGraduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Midterm
Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay Midterm GSB Honor Code: I pledge my honor that I have not violated the Honor Code during this examination.
More informationStudy 2: data analysis. Example analysis using R
Study 2: data analysis Example analysis using R Steps for data analysis Install software on your computer or locate computer with software (e.g., R, systat, SPSS) Prepare data for analysis Subjects (rows)
More informationAnalysis Variable : Y Analysis Variable : Y E
Here is the output from the SAS program in the document Skewness, Kurtosis, and the Normal Curve *g1g2.sas; data EDA; infile 'C:\Users\Vati\Documents\StatData\EDA.dat'; input Y; proc means mean skewness
More informationLecture 1: Empirical Properties of Returns
Lecture 1: Empirical Properties of Returns Econ 589 Eric Zivot Spring 2011 Updated: March 29, 2011 Daily CC Returns on MSFT -0.3 r(t) -0.2-0.1 0.1 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
More informationVariance clustering. Two motivations, volatility clustering, and implied volatility
Variance modelling The simplest assumption for time series is that variance is constant. Unfortunately that assumption is often violated in actual data. In this lecture we look at the implications of time
More informationChapter 3. Descriptive Measures. Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 3, Slide 1
Chapter 3 Descriptive Measures Copyright 2016, 2012, 2008 Pearson Education, Inc. Chapter 3, Slide 1 Chapter 3 Descriptive Measures Mean, Median and Mode Copyright 2016, 2012, 2008 Pearson Education, Inc.
More informationProfessor Brad Jones University of Arizona POL 681, SPRING 2004 INTERACTIONS and STATA: Companion To Lecture Notes on Statistical Interactions
Professor Brad Jones University of Arizona POL 681, SPRING 2004 INTERACTIONS and STATA: Companion To Lecture Notes on Statistical Interactions Preliminaries 1. Basic Regression. reg y x1 Source SS df MS
More informationModeling Panel Data: Choosing the Correct Strategy. Roberto G. Gutierrez
Modeling Panel Data: Choosing the Correct Strategy Roberto G. Gutierrez 2 / 25 #analyticsx Overview Panel data are ubiquitous in not only economics, but in all fields Panel data have intrinsic modeling
More informationHow To: Perform a Process Capability Analysis Using STATGRAPHICS Centurion
How To: Perform a Process Capability Analysis Using STATGRAPHICS Centurion by Dr. Neil W. Polhemus July 17, 2005 Introduction For individuals concerned with the quality of the goods and services that they
More information