The SAS System 11:03 Monday, November 11,

Size: px
Start display at page:

Download "The SAS System 11:03 Monday, November 11,"

Transcription

1 The SAS System 11:3 Monday, November 11, The CONTENTS Procedure Data Set Name BIO.AUTO_PREMIUMS Observations 5 Member Type DATA Variables 3 Engine V9 Indexes Created Monday, November 11, :4:19 AM Observation Length 24 Last Modified Monday, November 11, :4:19 AM Deleted Observations Protection Compressed NO Data Set Type Sorted NO Label Data Representation Encoding WINDOWS_64 wlatin1 Western (Windows) Engine/Host Dependent Information Data Set Page Size 496 Number of Data Set Pages 1 First Data Page 1 Max Obs per Page 168 Obs in First Data Page 5 Number of Data Set Repairs Filename H:\_Amy Docs\UF\ Fall 213\ \SAS Library\auto_premiums.sas7bdat Release Created 9.31M Host Created X64_7PRO Alphabetic List of Variables and Attributes # Variable Type Len 1 Experience Num 8 2 Gender Num 8 3 Premium Num 8

2 11:3 Monday, November 11, Premium Experience Gender 1

3 11:3 Monday, November 11, Analysis for Males 9 8 Premium Experience Gender

4 Analysis for Males 11:3 Monday, November 11, The MEANS Procedure Variable Minimum Lower Quartile Median Upper Quartile Maximum Mean Std Dev Lower 95% CL for Mean Upper 95% CL for Mean Experience Premium

5 11:3 Monday, November 11, Analysis for Males 25 2 Percent Experience Normal Kernel

6 11:3 Monday, November 11, Analysis for Males 3 2 Percent Premium Normal Kernel

7 11:3 Monday, November 11, Analysis for Males 15 Experience 1 5

8 11:3 Monday, November 11, Analysis for Males 9 8 Premium 7 6 5

9 Analysis for Males 11:3 Monday, November 11, The UNIVARIATE Procedure 2 Q-Q Plot for Experience 15 Experience Normal Quantiles Normal Line Mu=1.897, Sigma=5.79

10 Analysis for Males 11:3 Monday, November 11, The UNIVARIATE Procedure 1 Q-Q Plot for Premium 9 8 Premium Normal Quantiles Normal Line Mu=69.34, Sigma=11.939

11 Analysis for Males 11:3 Monday, November 11, The CORR Procedure 2 Variables: Experience Premium Simple Statistics Variable N Mean Std Dev Median Minimum Maximum Experience Premium Pearson Correlation Coefficients, N = 29 Prob > r under H: Rho= Experience Premium Experience Premium Spearman Correlation Coefficients, N = 29 Prob > r under H: Rho= Experience Premium Experience Premium Pearson Correlation Statistics (Fisher's z Transformation) Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for H:Rho= Experience Premium <.1 Spearman Correlation Statistics (Fisher's z Transformation) Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for H:Rho= Experience Premium

12 Analysis for Males 11:3 Monday, November 11, Number of Observations Read 29 Number of Observations Used 29 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total Root MSE R-Square.4298 Dependent Mean Adj R-Sq.487 Coeff Var Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > t 95% Confidence Limits Intercept < Experience

13 Analysis for Males 11:3 Monday, November 11, Distribution of Residuals for Premium 25 Normal Kernel 2 Percent Residual

14 Analysis for Males 11:3 Monday, November 11, Residual by Predicted for Premium 1 5 Residual Predicted Value

15 Analysis for Males 11:3 Monday, November 11, RStudent by Predicted for Premium 1 RStudent Predicted Value

16 Analysis for Males 11:3 Monday, November 11, Observed by Predicted for Premium 9 8 Premium Predicted Value

17 Analysis for Males 11:3 Monday, November 11, Cook's D for Premium Cook's D Observation

18 Analysis for Males 11:3 Monday, November 11, Outlier and Leverage Diagnostics for Premium 2 1 RStudent Leverage Outlier Leverage Outlier and Leverage

19 Analysis for Males 11:3 Monday, November 11, Q-Q Plot of Residuals for Premium 1 Residual Quantile

20 Analysis for Males 11:3 Monday, November 11, Residual-Fit Spread Plot for Premium 15 Fit Mean Residual Proportion Less

21 Analysis for Males 11:3 Monday, November 11, Residuals for Premium 1 5 Residual Experience

22 Analysis for Males 11:3 Monday, November 11, Fit Plot for Premium 1 Premium 8 6 Observations Parameters Error DF MSE R-Square Adj R-Square Experience Fit 95% Confidence Limits 95% Prediction Limits

23 11:3 Monday, November 11, Analysis for Females 8 7 Premium Experience Gender 1

24 Analysis for Females 11:3 Monday, November 11, The MEANS Procedure Variable Minimum Lower Quartile Median Upper Quartile Maximum Mean Std Dev Lower 95% CL for Mean Upper 95% CL for Mean Experience Premium

25 11:3 Monday, November 11, Analysis for Females 2 15 Percent Experience Normal Kernel

26 11:3 Monday, November 11, Analysis for Females 3 Percent Premium Normal Kernel

27 11:3 Monday, November 11, Analysis for Females 15 1 Experience 5

28 11:3 Monday, November 11, Analysis for Females 8 7 Premium 6 5 4

29 Analysis for Females 11:3 Monday, November 11, The UNIVARIATE Procedure 2 Q-Q Plot for Experience 15 Experience Normal Quantiles Normal Line Mu=8.4286, Sigma=4.789

30 Analysis for Females 11:3 Monday, November 11, The UNIVARIATE Procedure 9 Q-Q Plot for Premium 8 7 Premium Normal Quantiles Normal Line Mu=54.619, Sigma=15.439

31 Analysis for Females 11:3 Monday, November 11, The CORR Procedure 2 Variables: Experience Premium Simple Statistics Variable N Mean Std Dev Median Minimum Maximum Experience Premium Pearson Correlation Coefficients, N = 21 Prob > r under H: Rho= Experience Premium Experience <.1 Premium <.1 1. Spearman Correlation Coefficients, N = 21 Prob > r under H: Rho= Experience Premium Experience <.1 Premium <.1 1. Pearson Correlation Statistics (Fisher's z Transformation) Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for H:Rho= Experience Premium <.1 Spearman Correlation Statistics (Fisher's z Transformation) Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for H:Rho= Experience Premium <.1

32 Analysis for Females 11:3 Monday, November 11, Number of Observations Read 21 Number of Observations Used 21 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model <.1 Error Corrected Total Root MSE R-Square.7691 Dependent Mean Adj R-Sq.7569 Coeff Var Parameter Estimates Variable DF Parameter Estimate Standard Error t Value Pr > t 95% Confidence Limits Intercept < Experience <

33 Analysis for Females 11:3 Monday, November 11, Distribution of Residuals for Premium 3 Normal Kernel 2 Percent Residual

34 Analysis for Females 11:3 Monday, November 11, Residual by Predicted for Premium 1 5 Residual Predicted Value

35 Analysis for Females 11:3 Monday, November 11, RStudent by Predicted for Premium 2 1 RStudent Predicted Value

36 Analysis for Females 11:3 Monday, November 11, Observed by Predicted for Premium 8 7 Premium Predicted Value

37 Analysis for Females 11:3 Monday, November 11, Cook's D for Premium.3.2 Cook's D Observation

38 Analysis for Females 11:3 Monday, November 11, Outlier and Leverage Diagnostics for Premium 2 1 RStudent Leverage Outlier Leverage Outlier and Leverage

39 Analysis for Females 11:3 Monday, November 11, Q-Q Plot of Residuals for Premium 1 5 Residual Quantile

40 Analysis for Females 11:3 Monday, November 11, Residual-Fit Spread Plot for Premium Fit Mean Residual Proportion Less

41 Analysis for Females 11:3 Monday, November 11, Residuals for Premium 1 5 Residual Experience

42 Analysis for Females 11:3 Monday, November 11, Fit Plot for Premium 8 Premium 6 Observations Parameters Error DF MSE R-Square Adj R-Square Experience Fit 95% Confidence Limits 95% Prediction Limits

SAS Simple Linear Regression Example

SAS 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 information

EXST7015: Multiple Regression from Snedecor & Cochran (1967) RAW DATA LISTING

EXST7015: 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 information

GGraph. Males Only. Premium. Experience. GGraph. Gender. 1 0: R 2 Linear = : R 2 Linear = Page 1

GGraph. 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 information

Notice that X2 and Y2 are skewed. Taking the SQRT of Y2 reduces the skewness greatly.

Notice that X2 and Y2 are skewed. Taking the SQRT of Y2 reduces the skewness greatly. 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

More information

Topic 8: Model Diagnostics

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 information

Chapter 11 : Model checking and refinement An example: Blood-brain barrier study on rats

Chapter 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 information

Stat 328, Summer 2005

Stat 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 information

Lecture 13: Identifying unusual observations In lecture 12, we learned how to investigate variables. Now we learn how to investigate cases.

Lecture 13: Identifying unusual observations In lecture 12, we learned how to investigate variables. Now we learn how to investigate cases. Lecture 13: Identifying unusual observations In lecture 12, we learned how to investigate variables. Now we learn how to investigate cases. Goal: Find unusual cases that might be mistakes, or that might

More information

Empirical Rule (P148)

Empirical 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 information

> attach(grocery) > boxplot(sales~discount, ylab="sales",xlab="discount")

> attach(grocery) > boxplot(sales~discount, ylab=sales,xlab=discount) Example of More than 2 Categories, and Analysis of Covariance Example > attach(grocery) > boxplot(sales~discount, ylab="sales",xlab="discount") Sales 160 200 240 > tapply(sales,discount,mean) 10.00% 15.00%

More information

Labor Force Participation and the Wage Gap Detailed Notes and Code Econometrics 113 Spring 2014

Labor Force Participation and the Wage Gap Detailed Notes and Code Econometrics 113 Spring 2014 Labor Force Participation and the Wage Gap Detailed Notes and Code Econometrics 113 Spring 2014 In class, Lecture 11, we used a new dataset to examine labor force participation and wages across groups.

More information

is the bandwidth and controls the level of smoothing of the estimator, n is the sample size and

is 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 information

One Sample T-Test With Howell Data, IQ of Students in Vermont

One Sample T-Test With Howell Data, IQ of Students in Vermont One Sample T-Test With Howell Data, IQ of Students in Vermont data howell; infile 'C:\Users\Vati\Documents\StatData\howell.dat'; input addsc sex repeat iq engl engg gpa socprob dropout; IQ_diff = iq -

More information

Homework 0 Key (not to be handed in) due? Jan. 10

Homework 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 information

ARIMA ANALYSIS WITH INTERVENTIONS / OUTLIERS

ARIMA ANALYSIS WITH INTERVENTIONS / OUTLIERS TASK Run intervention analysis on the price of stock M: model a function of the price as ARIMA with outliers and interventions. SOLUTION The document below is an abridged version of the solution provided

More information

Labor Market Returns to Two- and Four- Year Colleges. Paper by Kane and Rouse Replicated by Andreas Kraft

Labor Market Returns to Two- and Four- Year Colleges. Paper by Kane and Rouse Replicated by Andreas Kraft Labor Market Returns to Two- and Four- Year Colleges Paper by Kane and Rouse Replicated by Andreas Kraft Theory Estimating the return to two-year colleges Economic Return to credit hours or sheepskin effects

More information

Are the movements of stocks, bonds, and housing linked? Zachary D Easterling Department of Economics The University of Akron

Are 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 information

tm / / / / / / / / / / / / Statistics/Data Analysis User: Klick Project: Limited Dependent Variables{space -6}

tm / / / / / / / / / / / / 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 information

Dummy variables 9/22/2015. Are wages different across union/nonunion jobs. Treatment Control Y X X i identifies treatment

Dummy 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 information

11/28/2018. Overview. Multiple Linear Regression Analysis. Multiple regression. Multiple regression. Multiple regression. Multiple regression

11/28/2018. Overview. Multiple Linear Regression Analysis. Multiple regression. Multiple regression. Multiple regression. Multiple regression Multiple Linear Regression Analysis BSAD 30 Dave Novak Fall 208 Source: Ragsdale, 208 Spreadsheet Modeling and Decision Analysis 8 th edition 207 Cengage Learning 2 Overview Last class we considered the

More information

Review: 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. 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 information

Introduction to General and Generalized Linear Models

Introduction to General and Generalized Linear Models Introduction to General and Generalized Linear Models Generalized Linear Models - IIIb Henrik Madsen March 18, 2012 Henrik Madsen () Chapman & Hall March 18, 2012 1 / 32 Examples Overdispersion and Offset!

More information

You created this PDF from an application that is not licensed to print to novapdf printer (http://www.novapdf.com)

You 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 information

Time series data: Part 2

Time 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 information

Econ 371 Problem Set #4 Answer Sheet. 6.2 This question asks you to use the results from column (1) in the table on page 213.

Econ 371 Problem Set #4 Answer Sheet. 6.2 This question asks you to use the results from column (1) in the table on page 213. Econ 371 Problem Set #4 Answer Sheet 6.2 This question asks you to use the results from column (1) in the table on page 213. a. The first part of this question asks whether workers with college degrees

More information

WesVar Analysis Example Replication C7

WesVar Analysis Example Replication C7 WesVar Analysis Example Replication C7 WesVar 5.1 is primarily a point and click application and though a text file of commands can be used in the WesVar (V5.1) batch processing environment, all examples

More information

The Multivariate Regression Model

The 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 information

Appendix. A.1 Independent Random Effects (Baseline)

Appendix. A.1 Independent Random Effects (Baseline) A Appendix A.1 Independent Random Effects (Baseline) 36 Table 2: Detailed Monte Carlo Results Logit Fixed Effects Clustered Random Effects Random Coefficients c Coeff. SE SD Coeff. SE SD Coeff. SE SD Coeff.

More information

The 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

The 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 information

Solutions for Session 5: Linear Models

Solutions 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 information

Valid 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%

Valid 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 information

Models of Patterns. Lecture 3, SMMD 2005 Bob Stine

Models of Patterns. Lecture 3, SMMD 2005 Bob Stine Models of Patterns Lecture 3, SMMD 2005 Bob Stine Review Speculative investing and portfolios Risk and variance Volatility adjusted return Volatility drag Dependence Covariance Review Example Stock and

More information

1. Distinguish three missing data mechanisms:

1. 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 information

Advanced Econometrics

Advanced Econometrics Advanced Econometrics Instructor: Takashi Yamano 11/14/2003 Due: 11/21/2003 Homework 5 (30 points) Sample Answers 1. (16 points) Read Example 13.4 and an AER paper by Meyer, Viscusi, and Durbin (1995).

More information

Analysis of Variance in Matrix form

Analysis of Variance in Matrix form Analysis of Variance in Matrix form The ANOVA table sums of squares, SSTO, SSR and SSE can all be expressed in matrix form as follows. week 9 Multiple Regression A multiple regression model is a model

More information

İnsan 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 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 information

2016 FACULTY SALARY EQUITY ANALYSIS

2016 FACULTY SALARY EQUITY ANALYSIS 2016 FACULTY SALARY EQUITY ANALYSIS UNIVERSITY OF CALIFORNIA, SANTA BARBARA OFFICE OF THE EXECUTIVE VICE CHANCELLOR & THE FACULTY SALARY EQUITY STUDY COMMITTEE APRIL 2017 INTRODUCTION This report contains

More information

Stat 101 Exam 1 - Embers Important Formulas and Concepts 1

Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1 Chapter 1 1.1 Definitions Stat 101 Exam 1 - Embers Important Formulas and Concepts 1 1. Data Any collection of numbers, characters, images, or other items that provide information about something. 2.

More information

Final Exam - section 1. Thursday, December hours, 30 minutes

Final Exam - section 1. Thursday, December hours, 30 minutes Econometrics, ECON312 San Francisco State University Michael Bar Fall 2013 Final Exam - section 1 Thursday, December 19 1 hours, 30 minutes Name: Instructions 1. This is closed book, closed notes exam.

More information

The FREQ Procedure. Table of Sex by Gym Sex(Sex) Gym(Gym) No Yes Total Male Female Total

The 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 information

The relationship between GDP, labor force and health expenditure in European countries

The relationship between GDP, labor force and health expenditure in European countries Econometrics-Term paper The relationship between GDP, labor force and health expenditure in European countries Student: Nguyen Thu Ha Contents 1. Background:... 2 2. Discussion:... 2 3. Regression equation

More information

Random Effects ANOVA

Random Effects ANOVA Random Effects ANOVA Grant B. Morgan Baylor University This post contains code for conducting a random effects ANOVA. Make sure the following packages are installed: foreign, lme4, lsr, lattice. library(foreign)

More information

Problem Set 9 Heteroskedasticty Answers

Problem Set 9 Heteroskedasticty Answers Problem Set 9 Heteroskedasticty Answers /* INVESTIGATION OF HETEROSKEDASTICITY */ First graph data. u hetdat2. gra manuf gdp, s([country].) xlab ylab 300000 manufacturing output (US$ miilio 200000 100000

More information

u panel_lecture . sum

u 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 information

Let us assume that we are measuring the yield of a crop plant on 5 different plots at 4 different observation times.

Let us assume that we are measuring the yield of a crop plant on 5 different plots at 4 different observation times. Mixed-effects models An introduction by Christoph Scherber Up to now, we have been dealing with linear models of the form where ß0 and ß1 are parameters of fixed value. Example: Let us assume that we are

More information

1.1 ANNUAL PRICE MODEL

1.1 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 information

Regression Review and Robust Regression. Slides prepared by Elizabeth Newton (MIT)

Regression Review and Robust Regression. Slides prepared by Elizabeth Newton (MIT) Regression Review and Robust Regression Slides prepared by Elizabeth Newton (MIT) S-Plus Oil City Data Frame Monthly Excess Returns of Oil City Petroleum, Inc. Stocks and the Market SUMMARY: The oilcity

More information

Modeling Panel Data: Choosing the Correct Strategy. Roberto G. Gutierrez

Modeling 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 information

Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing

Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing Data Mining: An Overview of Methods and Technologies for Increasing Profits in Direct Marketing C. Olivia Rud, President, OptiMine Consulting, West Chester, PA ABSTRACT Data Mining is a new term for the

More information

Quantitative Techniques Term 2

Quantitative Techniques Term 2 Quantitative Techniques Term 2 Laboratory 7 2 March 2006 Overview The objective of this lab is to: Estimate a cost function for a panel of firms; Calculate returns to scale; Introduce the command cluster

More information

boxcox() returns the values of α and their loglikelihoods,

boxcox() returns the values of α and their loglikelihoods, Solutions to Selected Computer Lab Problems and Exercises in Chapter 11 of Statistics and Data Analysis for Financial Engineering, 2nd ed. by David Ruppert and David S. Matteson c 2016 David Ruppert and

More information

Econometrics is. The estimation of relationships suggested by economic theory

Econometrics 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 information

Data screening, transformations: MRC05

Data screening, transformations: MRC05 Dale Berger Data screening, transformations: MRC05 This is a demonstration of data screening and transformations for a regression analysis. Our interest is in predicting current salary from education level

More information

Two-Sample T-Test for Superiority by a Margin

Two-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 information

Point-Biserial and Biserial Correlations

Point-Biserial and Biserial Correlations Chapter 302 Point-Biserial and Biserial Correlations Introduction This procedure calculates estimates, confidence intervals, and hypothesis tests for both the point-biserial and the biserial correlations.

More information

Handout seminar 6, ECON4150

Handout 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 information

Two-Sample T-Test for Non-Inferiority

Two-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 information

Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Final Exam

Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Final Exam Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay Final Exam GSB Honor Code: I pledge my honor that I have not violated the Honor Code during this

More information

Impact of Household Income on Poverty Levels

Impact 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 information

Effect of Education on Wage Earning

Effect of Education on Wage Earning Effect of Education on Wage Earning Group Members: Quentin Talley, Thomas Wang, Geoff Zaski Abstract The scope of this project includes individuals aged 18-65 who finished their education and do not have

More information

EC327: Limited Dependent Variables and Sample Selection Binomial probit: probit

EC327: Limited Dependent Variables and Sample Selection Binomial probit: probit EC327: Limited Dependent Variables and Sample Selection Binomial probit: probit. summarize work age married children education Variable Obs Mean Std. Dev. Min Max work 2000.6715.4697852 0 1 age 2000 36.208

More information

Multiple Regression. Review of Regression with One Predictor

Multiple Regression. Review of Regression with One Predictor Fall Semester, 2001 Statistics 621 Lecture 4 Robert Stine 1 Preliminaries Multiple Regression Grading on this and other assignments Assignment will get placed in folder of first member of Learning Team.

More information

Spring, Beta and Regression

Spring, Beta and Regression Spring, 2000-1 - Administrative Items Getting help See me Monday 3-5:30 or tomorrow after 2:30. Send me an e-mail with your question. (stine@wharton) Visit the StatLab/TAs, particularly for help using

More information

############################ ### toxo.r ### ############################

############################ ### toxo.r ### ############################ ############################ ### toxo.r ### ############################ toxo < read.table(file="n:\\courses\\stat8620\\fall 08\\toxo.dat",header=T) #toxo < read.table(file="c:\\documents and Settings\\dhall\\My

More information

Session 5: Associations

Session 5: Associations Session 5: Associations Li (Sherlly) Xie http://www.nemoursresearch.org/open/statclass/february2013/ Session 5 Flow 1. Bivariate data visualization Cross-Tab Stacked bar plots Box plot Scatterplot 2. Correlation

More information

Chapter 11 Part 6. Correlation Continued. LOWESS Regression

Chapter 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 information

LAMPIRAN PERHITUNGAN EVIEWS

LAMPIRAN 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 information

ECON Introductory Econometrics. Seminar 4. Stock and Watson Chapter 8

ECON Introductory Econometrics. Seminar 4. Stock and Watson Chapter 8 ECON4150 - Introductory Econometrics Seminar 4 Stock and Watson Chapter 8 empirical exercise E8.2: Data 2 In this exercise we use the data set CPS12.dta Each month the Bureau of Labor Statistics in the

More information

Lecture 1: Review and Exploratory Data Analysis (EDA)

Lecture 1: Review and Exploratory Data Analysis (EDA) Lecture 1: Review and Exploratory Data Analysis (EDA) Ani Manichaikul amanicha@jhsph.edu 16 April 2007 1 / 40 Course Information I Office hours For questions and help When? I ll announce this tomorrow

More information

Assignment #5 Solutions: Chapter 14 Q1.

Assignment #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 information

6 Multiple Regression

6 Multiple Regression More than one X variable. 6 Multiple Regression Why? Might be interested in more than one marginal effect Omitted Variable Bias (OVB) 6.1 and 6.2 House prices and OVB Should I build a fireplace? The following

More information

Problem Set 6 ANSWERS

Problem Set 6 ANSWERS Economics 20 Part I. Problem Set 6 ANSWERS Prof. Patricia M. Anderson The first 5 questions are based on the following information: Suppose a researcher is interested in the effect of class attendance

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 *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 information

Professor 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 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 information

Intro. Econometrics Fall 2015

Intro. 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 information

. tsset year, yearly time variable: year, 1959 to 1994 delta: 1 year. . reg lhous ldpi lrealp

. tsset year, yearly time variable: year, 1959 to 1994 delta: 1 year. . reg lhous ldpi lrealp - opened on: 24 Mar 2012, 21:29:52. use "G:\stata\( )\demand_2011.dta", clear. de - variable name type format label variable label - year float %ty year time float %9.0g trend pop float %9.0g population,

More information

Today's Agenda Hour 1 Correlation vs association, Pearson s R, non-linearity, Spearman rank correlation,

Today's Agenda Hour 1 Correlation vs association, Pearson s R, non-linearity, Spearman rank correlation, Today's Agenda Hour 1 Correlation vs association, Pearson s R, non-linearity, Spearman rank correlation, Hour 2 Hypothesis testing for correlation (Pearson) Correlation and regression. Correlation vs association

More information

Determination of the Optimal Stratum Boundaries in the Monthly Retail Trade Survey in the Croatian Bureau of Statistics

Determination of the Optimal Stratum Boundaries in the Monthly Retail Trade Survey in the Croatian Bureau of Statistics Determination of the Optimal Stratum Boundaries in the Monthly Retail Trade Survey in the Croatian Bureau of Statistics Ivana JURINA (jurinai@dzs.hr) Croatian Bureau of Statistics Lidija GLIGOROVA (gligoroval@dzs.hr)

More information

Non-linearities in Simple Regression

Non-linearities in Simple Regression Non-linearities in Simple Regression 1. Eample: Monthly Earnings and Years of Education In this tutorial, we will focus on an eample that eplores the relationship between total monthly earnings and years

More information

Study 2: data analysis. Example analysis using R

Study 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 information

Sean Howard Econometrics Final Project Paper. An Analysis of the Determinants and Factors of Physical Education Attendance in the Fourth Quarter

Sean Howard Econometrics Final Project Paper. An Analysis of the Determinants and Factors of Physical Education Attendance in the Fourth Quarter Sean Howard Econometrics Final Project Paper An Analysis of the Determinants and Factors of Physical Education Attendance in the Fourth Quarter Introduction This project attempted to gain a more complete

More information

CSC Advanced Scientific Programming, Spring Descriptive Statistics

CSC Advanced Scientific Programming, Spring Descriptive Statistics CSC 223 - Advanced Scientific Programming, Spring 2018 Descriptive Statistics Overview Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions.

More information

Negative Binomial Model for Count Data Log-linear Models for Contingency Tables - Introduction

Negative Binomial Model for Count Data Log-linear Models for Contingency Tables - Introduction Negative Binomial Model for Count Data Log-linear Models for Contingency Tables - Introduction Statistics 149 Spring 2006 Copyright 2006 by Mark E. Irwin Negative Binomial Family Example: Absenteeism from

More information

Don t worry one bit about multicollinearity, because at the end of the day, you're going to be working with a favorite coefficient model.

Don t worry one bit about multicollinearity, because at the end of the day, you're going to be working with a favorite coefficient model. In theory, you might think that dummy variables would facilitate a simple and compelling test for bias or discrimination. For example, suppose you wanted to test for gender bias in pay. It's really very

More information

Statistics for Business and Economics

Statistics for Business and Economics Statistics for Business and Economics Chapter 7 Estimation: Single Population Copyright 010 Pearson Education, Inc. Publishing as Prentice Hall Ch. 7-1 Confidence Intervals Contents of this chapter: Confidence

More information

MODEL SELECTION CRITERIA IN R:

MODEL 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 information

Measures of Center. Mean. 1. Mean 2. Median 3. Mode 4. Midrange (rarely used) Measure of Center. Notation. Mean

Measures of Center. Mean. 1. Mean 2. Median 3. Mode 4. Midrange (rarely used) Measure of Center. Notation. Mean Measure of Center Measures of Center The value at the center or middle of a data set 1. Mean 2. Median 3. Mode 4. Midrange (rarely used) 1 2 Mean Notation The measure of center obtained by adding the values

More information

Risk Analysis. å To change Benchmark tickers:

Risk Analysis. å To change Benchmark tickers: Property Sheet will appear. The Return/Statistics page will be displayed. 2. Use the five boxes in the Benchmark section of this page to enter or change the tickers that will appear on the Performance

More information

Master s in Financial Engineering Foundations of Buy-Side Finance: Quantitative Risk and Portfolio Management. > Teaching > Courses

Master s in Financial Engineering Foundations of Buy-Side Finance: Quantitative Risk and Portfolio Management.  > Teaching > Courses Master s in Financial Engineering Foundations of Buy-Side Finance: Quantitative Risk and Portfolio Management www.symmys.com > Teaching > Courses Spring 2008, Monday 7:10 pm 9:30 pm, Room 303 Attilio Meucci

More information

Technical Documentation for Household Demographics Projection

Technical Documentation for Household Demographics Projection Technical Documentation for Household Demographics Projection REMI Household Forecast is a tool to complement the PI+ demographic model by providing comprehensive forecasts of a variety of household characteristics.

More information

R & R Study. Chapter 254. Introduction. Data Structure

R & R Study. Chapter 254. Introduction. Data Structure Chapter 54 Introduction A repeatability and reproducibility (R & R) study (sometimes called a gauge study) is conducted to determine if a particular measurement procedure is adequate. If the measurement

More information

Your Name (Please print) Did you agree to take the optional portion of the final exam Yes No. Directions

Your Name (Please print) Did you agree to take the optional portion of the final exam Yes No. Directions Your Name (Please print) Did you agree to take the optional portion of the final exam Yes No (Your online answer will be used to verify your response.) Directions There are two parts to the final exam.

More information

Chapter 3. Populations and Statistics. 3.1 Statistical populations

Chapter 3. Populations and Statistics. 3.1 Statistical populations Chapter 3 Populations and Statistics This chapter covers two topics that are fundamental in statistics. The first is the concept of a statistical population, which is the basic unit on which statistics

More information

Subject 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 ` 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 information

Table I Descriptive Statistics This table shows the breakdown of the eligible funds as at May 2011. AUM refers to assets under management. Panel A: Fund Breakdown Fund Count Vintage count Avg AUM US$ MM

More information

Numerical Descriptions of Data

Numerical Descriptions of Data Numerical Descriptions of Data Measures of Center Mean x = x i n Excel: = average ( ) Weighted mean x = (x i w i ) w i x = data values x i = i th data value w i = weight of the i th data value Median =

More information

Regression Model Assumptions Solutions

Regression Model Assumptions Solutions Regression Model Assumptions Solutions Below are the solutions to these exercises on model diagnostics using residual plots. # Exercise 1 # data("cars") head(cars) speed dist 1 4 2 2 4 10 3 7 4 4 7 22

More information

Parameter Estimation

Parameter Estimation Parameter Estimation Bret Larget Departments of Botany and of Statistics University of Wisconsin Madison April 12, 2007 Statistics 572 (Spring 2007) Parameter Estimation April 12, 2007 1 / 14 Continue

More information

NCSS Statistical Software. Reference Intervals

NCSS Statistical Software. Reference Intervals Chapter 586 Introduction A reference interval contains the middle 95% of measurements of a substance from a healthy population. It is a type of prediction interval. This procedure calculates one-, and

More information

Statistics S1 Advanced/Advanced Subsidiary

Statistics S1 Advanced/Advanced Subsidiary Paper Reference(s) 6683/01 Edexcel GCE Statistics S1 Advanced/Advanced Subsidiary Tuesday 10 June 2014 Morning Time: 1 hour 30 minutes Materials required for examination Mathematical Formulae (Pink) Items

More information