The Coefficient of Determination

Size: px
Start display at page:

Download "The Coefficient of Determination"

Transcription

1 The Coefficient of Determination Lecture 46 Section 13.9 Robb T. Koether Hampden-Sydney College Tue, Apr 13, 2010 Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

2 Outline 1 The Regression Identity 2 Sums of Squares on the TI-83 3 Explaining Variation 4 TI-83 - The Coefficient of Determination 5 Assignment Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

3 Outline 1 The Regression Identity 2 Sums of Squares on the TI-83 3 Explaining Variation 4 TI-83 - The Coefficient of Determination 5 Assignment Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

4 Explaining the Variation in y Statisticians use regression models to explain y. More specifically, through the model they use variation in x to explain variation in y. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

5 Explaining the Variation in y For example, why do some people weigh more than other people? One explanation is that some people weigh more than others because they are taller. That is, there is variation in weight because their is variation in height and because weight and height are correlated. But that is only a partial explanation. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

6 Explaining the Variation in y Statisticians want to quantify how much of the variation in y is explained by the variation in x. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

7 The Regression Identity As always, variation is measure by calculating a sum of squared deviations. There are three different deviations that we can measure. Deviations of y from y (variation in the data). Deviations of ŷ from y (variation in the model). Deviations of y from ŷ (difference between the data and the model). Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

8 The Regression Identity Variation in the data (Total sum of squares): SST = (y y) 2. Variation in the model (Regression sum of squares): SSR = (ŷ y) 2. Residues (Sum of squared Errors): SSE = (y ŷ) 2. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

9 Example - SST, SSR, and SSE The following data represent the heights and weights of 10 adult males. Height (x) Weight (y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

10 Example - SST, SSR, and SSE The regression line is ŷ = x. The model predicts, for example, that if a person is 70 inches tall, he will weigh 180 pounds. The model also predicts that a person will weigh an additional 7 pounds for each additional inch of height. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

11 Example - SST, SSR, and SSE Compute the predicted weight: Y 1 (L 1 ) L 3. Height (x) Weight (y) Pred. Wgt. (ŷ) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

12 Example - SST, SSR, and SSE The regression line Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

13 Example - SST, SSR, and SSE The deviations of y from y Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

14 Example - SST, SSR, and SSE The deviations of ŷ from y Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

15 Example - SST, SSR, and SSE The deviations of y from ŷ Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

16 Example Compute SST. x y y y (y y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

17 Example Compute SST: L 2 -y. x y y y (y y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

18 Example Compute SST: Ans 2. x y y y (y y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

19 Example Compute SST: sum(ans). x y y y (y y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

20 Example Compute SSR. x y ŷ ŷ y (ŷ y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

21 Example Compute SSR: Y 1 (L 1 ) L 3. x y ŷ ŷ y (ŷ y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

22 Example Compute SSR: L 3 -y. x y ŷ ŷ y (ŷ y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

23 Example Compute SSR: Ans 2. x y ŷ ŷ y (ŷ y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

24 Example Compute SSR: sum(ans). x y ŷ ŷ y (ŷ y) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

25 Example Compute SSE. x y ŷ y ŷ (y ŷ) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

26 Example Compute SSE: Y 1 (L 1 ) L 3. x y ŷ y ŷ (y ŷ) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

27 Example Compute SSE: L 2 -L 3 L 4. x y ŷ y ŷ (y ŷ) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

28 Example Compute SSE: Ans 2. x y ŷ y ŷ (y ŷ) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

29 Example Compute SSE: sum(ans). x y ŷ y ŷ (y ŷ) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

30 Example We have now found that SSR = SSE = SST = We see that SSR + SSE = SST. This is called the regression identity. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

31 Outline 1 The Regression Identity 2 Sums of Squares on the TI-83 3 Explaining Variation 4 TI-83 - The Coefficient of Determination 5 Assignment Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

32 TI-83 - Finding SSR, SSE, and SST TI-83 SSR, SSE, and SST Put the x values into L 1 and the y values into L 2. Use LinReg(a+bx) L 1,L 2,Y 1. Enter Y 1 (L 1 ) L 3. To get SSR, evaluate sum((l 3 -y) 2 ). To get SSE, evaluate sum((l 2 -L 3 ) 2 ). To get SST, evaluate sum((l 2 -y) 2 ). Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

33 Outline 1 The Regression Identity 2 Sums of Squares on the TI-83 3 Explaining Variation 4 TI-83 - The Coefficient of Determination 5 Assignment Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

34 Explaining Variation One goal of regression is to explain the variation in y. For example, if y were weight, how would we explain the variation in weight? That is, why do some people weigh more than others? A partial answer is that some people weigh more because they are taller. That is, an explanatory variable is height x. What are some other partial answers? Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

35 Explaining Variation How much of the variation in weight is explained by variation in height? The total variation in weight is SST. The linear model (the regression line) explains some of the variation. The model predicts the variation SSR. The remainder is SSE, the variation not predicted by the model. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

36 Explaining Variation Statisticians consider the predicted variation SSR to be the amount of variation in y that is explained by the model. The residual variation SSE is the remaining variation in y that is not explained by the model. It all checks out because SST = SSR + SSE. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

37 Variation Explained by the Model The regression line Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

38 Variation Explained by the Model The total variation in y (SST) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

39 Variation Explained by the Model The variation in y that is explained by the model (SSR) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

40 Variation Explained by the Model The variation in y that is unexplained by the model (SSE) Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

41 Explaining Variation It can be shown that and, therefore, r 2 = SSR SST 1 r 2 = SSE SST. Therefore, r 2 is the proportion of variation in y that is explained by the model. It is called the coefficient of determination. 1 r 2 is the proportion that is not explained by the model. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

42 Outline 1 The Regression Identity 2 Sums of Squares on the TI-83 3 Explaining Variation 4 TI-83 - The Coefficient of Determination 5 Assignment Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

43 TI-83 - Coefficient of Determination TI-83 Coefficient of Determination To calculate r 2 on the TI-83, follow the procedure that produces the regression line and r. In the same window, the TI-83 reports the value of r 2. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

44 TI-83 - Finding SSR, SSE, and SST Practice The data on the next slide represent crude oil prices a (x) vs. gasoline prices b (y). Draw the scatter plot. Find the equation of the regression line. Perform the residual analysis. Find the correlation coefficient. Find the coefficient of determination. Compute SST, SSR, and SSE. a b Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

45 TI-83 - Finding SSR, SSE, and SST Practice Date Crude Oil Date Gasoline Jan Jan Jan Jan Jan Feb Feb Feb Feb Feb Feb Feb Feb Mar Mar Mar Mar Mar Mar Mar Mar Mar Apr Apr Find SST, SSR, and SSE. Find r 2 and interpret the value. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

46 Outline 1 The Regression Identity 2 Sums of Squares on the TI-83 3 Explaining Variation 4 TI-83 - The Coefficient of Determination 5 Assignment Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

47 Assignment Homework Read Section 13.9, pages Work the practice problem on the previous slide. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

48 Answers to Even-Numbered Exercises Answers to Even-Numbered Exercises SST = , SSR = , SSE = r 2 = About 65.44% of the variation in gas prices is due to variation in oil prices. Robb T. Koether (Hampden-Sydney College) The Coefficient of Determination Tue, Apr 13, / 48

Chapter 5 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL

Chapter 5 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL Chapter 5 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL 87 ESTIMATION OF MAINTENANCE COST PER HOUR USING AGE REPLACEMENT COST MODEL 5.1 INTRODUCTION Maintenance is usually carried

More information

Meeting product specifications

Meeting product specifications Optimisation of a diesel hydrotreating unit A model based on operating data is used to meet sulphur product specifications at lower DHT reactor temperatures with longer catalyst life Jose Bird Valero Energy

More information

Commercial-in-Confidence Ashton Old Baths Financial Model - Detailed Cashflow

Commercial-in-Confidence Ashton Old Baths Financial Model - Detailed Cashflow Year 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 2,038 2,922 4,089 4,349 6,256 7,124 8,885 8,885 8,885 8,885 8,885 8,885 9,107

More information

Stat 301 Lecture 30. Model Selection. Explanatory Variables. A Good Model. Response: Highway MPG Explanatory: 13 explanatory variables

Stat 301 Lecture 30. Model Selection. Explanatory Variables. A Good Model. Response: Highway MPG Explanatory: 13 explanatory variables Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)

More information

Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR

Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR Tutorial 1 Getting Started with Correlated Component Regression (CCR) in XLSTAT-CCR Dataset for running Correlated Component Regression This tutorial 1 is based on data provided by Michel Tenenhaus and

More information

Predicting Tractor Fuel Consumption

Predicting Tractor Fuel Consumption University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Biological Systems Engineering: Papers and Publications Biological Systems Engineering 24 Predicting Tractor Fuel Consumption

More information

Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran

Statistics and Quantitative Analysis U4320. Segment 8 Prof. Sharyn O Halloran Statistics and Quantitative Analysis U4320 Segment 8 Prof. Sharyn O Halloran I. Introduction A. Overview 1. Ways to describe, summarize and display data. 2.Summary statements: Mean Standard deviation Variance

More information

Improving CERs building

Improving CERs building Improving CERs building Getting Rid of the R² tyranny Pierre Foussier pmf@3f fr.com ISPA. San Diego. June 2010 1 Why abandon the OLS? The ordinary least squares (OLS) aims to build a CER by minimizing

More information

Introduction. Materials and Methods. How to Estimate Injection Percentage

Introduction. Materials and Methods. How to Estimate Injection Percentage How to Estimate Injection Percentage Introduction The Marel IN33-3 injector for pork bellies is a 5 needle, low-pressure conveyor type machine which utilizes a 3-gpm positive displacement pump and control

More information

Preface... xi. A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content...

Preface... xi. A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content... Contents Preface... xi A Word to the Practitioner... xi The Organization of the Book... xi Required Software... xii Accessing the Supplementary Content... xii Chapter 1 Introducing Partial Least Squares...

More information

Stat 401 B Lecture 31

Stat 401 B Lecture 31 Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan 1 Explanatory Variables Engine size (liters) Cylinders (number)

More information

LAMPIRAN 1. Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari Februari 2012

LAMPIRAN 1. Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari Februari 2012 LAMPIRAN 1 Tabel 1. Data Indeks Harga Saham PT. ANTAM, tbk Periode 20 Januari 2011 29 Februari 2012 No Tanggal Indeks Harga Saham No Tanggal Indeks Harga Saham 1 20-Jan-011 2.35 138 05-Agst-011 1.95 2

More information

Stat 301 Lecture 26. Model Selection. Indicator Variables. Explanatory Variables

Stat 301 Lecture 26. Model Selection. Indicator Variables. Explanatory Variables Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan There is an indicator for Pickup but there are no pickups

More information

d / cm t 2 / s 2 Fig. 3.1

d / cm t 2 / s 2 Fig. 3.1 7 5 A student has been asked to determine the linear acceleration of a toy car as it moves down a slope. He sets up the apparatus as shown in Fig. 3.1. d Fig. 3.1 The time t to move from rest through a

More information

Motor Trend MPG Analysis

Motor Trend MPG Analysis Motor Trend MPG Analysis SJ May 15, 2016 Executive Summary For this project, we were asked to look at a data set of a collection of cars in the automobile industry. We are going to explore the relationship

More information

2.007 Design and Manufacturing I

2.007 Design and Manufacturing I MIT OpenCourseWare http://ocw.mit.edu 2.007 Design and Manufacturing I Spring 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Page 1 of 4 2.007 Design

More information

The PRINCOMP Procedure

The PRINCOMP Procedure Grizzly Bear Project - Coastal Sites - invci 15:14 Friday, June 11, 2010 1 Food production variables The PRINCOMP Procedure Observations 16 Variables 4 Simple Statistics PRECIP ndvi aet temp Mean 260.8102476

More information

HASIL OUTPUT SPSS. Reliability Scale: ALL VARIABLES

HASIL OUTPUT SPSS. Reliability Scale: ALL VARIABLES 139 HASIL OUTPUT SPSS Reliability Scale: ALL VARIABLES Case Processing Summary N % 100 100.0 Cases Excluded a 0.0 Total 100 100.0 a. Listwise deletion based on all variables in the procedure. Reliability

More information

STUDENT ACTIVITY SHEET Name Period Fire Hose Friction Loss The Varying Variables for the One That Got Away Part 1

STUDENT ACTIVITY SHEET Name Period Fire Hose Friction Loss The Varying Variables for the One That Got Away Part 1 STUDENT ACTIVITY SHEET Name Period Fire Hose Friction Loss The Varying Variables for the One That Got Away Part 1 The questions: How does Friction Loss change with the quality of the fire hose? How does

More information

Professor Dr. Gholamreza Nakhaeizadeh. Professor Dr. Gholamreza Nakhaeizadeh

Professor Dr. Gholamreza Nakhaeizadeh. Professor Dr. Gholamreza Nakhaeizadeh Statistic Methods in in Data Mining Business Understanding Data Understanding Data Preparation Deployment Modelling Evaluation Data Mining Process (Part 2) 2) Professor Dr. Gholamreza Nakhaeizadeh Professor

More information

9.3 Tests About a Population Mean (Day 1)

9.3 Tests About a Population Mean (Day 1) Bellwork In a recent year, 73% of first year college students responding to a national survey identified being very well off financially as an important personal goal. A state university finds that 132

More information

TOOL #5: C&S WASDE PRICE STUDY FOR DECEMBER CORN 7/09/10 For the July 9 th to the August 12 th time frame for CZ 2010

TOOL #5: C&S WASDE PRICE STUDY FOR DECEMBER CORN 7/09/10 For the July 9 th to the August 12 th time frame for CZ 2010 TOOL #5: C&S WASDE PRICE STUDY FOR DECEMBER CORN 7/09/10 For the July 9 th to the August 12 th time frame for CZ 2010 Brief summary: In the month ahead, my best estimate is that CZ 2010 could trade in

More information

HUIZHI XIE (JOINTLY WITH HONGFEI LI AND YASUO AMEMIYA)

HUIZHI XIE (JOINTLY WITH HONGFEI LI AND YASUO AMEMIYA) A statistical framework for hydrant reliability analysis and prediction 1 HUIZHI XIE (JOINTLY WITH HONGFEI LI AND YASUO AMEMIYA) Outline Introduction Statistical modeling and analysis Summary Future research

More information

Stat 401 B Lecture 27

Stat 401 B Lecture 27 Model Selection Response: Highway MPG Explanatory: 13 explanatory variables Indicator variables for types of car Sports Car, SUV, Wagon, Minivan There is an indicator for Pickup but there are no pickups

More information

1ACE Exercise 1. Name Date Class

1ACE Exercise 1. Name Date Class 1ACE Exercise 1 Investigation 1 1. A group of students conducts the bridge-thickness experiment with construction paper. Their results are shown in this table. Bridge-Thickness Experiment Thickness (layers)

More information

Sharif University of Technology. Graduate School of Management and Economics. Econometrics I. Fall Seyed Mahdi Barakchian

Sharif University of Technology. Graduate School of Management and Economics. Econometrics I. Fall Seyed Mahdi Barakchian Sharif University of Technology Graduate School of Management and Economics Econometrics I Fall 2010 Seyed Mahdi Barakchian Textbook: Wooldridge, J., Introductory Econometrics: A Modern Approach, South

More information

Passive Investors and Managed Money in Commodity Futures. Part 2: Liquidity. Prepared for: The CME Group. Prepared by:

Passive Investors and Managed Money in Commodity Futures. Part 2: Liquidity. Prepared for: The CME Group. Prepared by: Passive Investors and Managed Money in Commodity Futures Part 2: Liquidity Prepared for: The CME Group Prepared by: October, 2008 Table of Contents Section Slide Number Objectives and Approach 3 Findings

More information

ESSAYS ESSAY B ESSAY A and 2009 are given below:

ESSAYS ESSAY B ESSAY A and 2009 are given below: ESSAYS -7- -8- Suggested time: 5 minutes Maximum score: 120 points ESSAY A Suggested time: 5 minutes Maximum score: 120 points I. Define a time series and its components. Discuss the importance and the

More information

Problem Set 3 - Solutions

Problem Set 3 - Solutions Ecn 102 - Analysis of Economic Data University of California - Davis January 22, 2011 John Parman Problem Set 3 - Solutions This problem set will be due by 5pm on Monday, February 7th. It may be turned

More information

LET S ARGUE: STUDENT WORK PAMELA RAWSON. Baxter Academy for Technology & Science Portland, rawsonmath.

LET S ARGUE: STUDENT WORK PAMELA RAWSON. Baxter Academy for Technology & Science Portland, rawsonmath. LET S ARGUE: STUDENT WORK PAMELA RAWSON Baxter Academy for Technology & Science Portland, Maine pamela.rawson@gmail.com @rawsonmath rawsonmath.com Contents Student Movie Data Claims (Cycle 1)... 2 Student

More information

C67_2_27_Investigation 5. February 27, Rewrite in slope intercept form. Homework: Page 78, 2 Page 80, 13 & 14

C67_2_27_Investigation 5. February 27, Rewrite in slope intercept form. Homework: Page 78, 2 Page 80, 13 & 14 Find ten possible car miles, SUV miles pairs that give a total of no more than 1,000 miles. One month the family drove the car 500 miles and the SUV 500 miles. Was the total for this month no more than

More information

delivery<-read.csv(file="d:/chilo/regression 4/delivery.csv", header=t) delivery

delivery<-read.csv(file=d:/chilo/regression 4/delivery.csv, header=t) delivery Regression Analysis lab 4 1 Model Adequacy Checking 1.1 Import data delivery

More information

TRINITY COLLEGE DUBLIN THE UNIVERSITY OF DUBLIN. Faculty of Engineering, Mathematics and Science. School of Computer Science and Statistics

TRINITY COLLEGE DUBLIN THE UNIVERSITY OF DUBLIN. Faculty of Engineering, Mathematics and Science. School of Computer Science and Statistics ST7003-1 TRINITY COLLEGE DUBLIN THE UNIVERSITY OF DUBLIN Faculty of Engineering, Mathematics and Science School of Computer Science and Statistics Postgraduate Certificate in Statistics Hilary Term 2015

More information

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS*

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS* COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) 2 Fixed Rates Variable Rates For Internal Use Only. FIXED RATES OF THE PAST 25 YEARS AVERAGE RESIDENTIAL MORTGAGE LENDING RATE - 5 YEAR* (Per cent) Year

More information

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS*

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS* COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) 2 Fixed Rates Variable Rates FIXED RATES OF THE PAST 25 YEARS AVERAGE RESIDENTIAL MORTGAGE LENDING RATE - 5 YEAR* (Per cent) Year Jan Feb Mar Apr May Jun

More information

Vehicle Scrappage and Gasoline Policy. Online Appendix. Alternative First Stage and Reduced Form Specifications

Vehicle Scrappage and Gasoline Policy. Online Appendix. Alternative First Stage and Reduced Form Specifications Vehicle Scrappage and Gasoline Policy By Mark R. Jacobsen and Arthur A. van Benthem Online Appendix Appendix A Alternative First Stage and Reduced Form Specifications Reduced Form Using MPG Quartiles The

More information

Performance of VAV Parallel Fan-Powered Terminal Units: Experimental Results and Models

Performance of VAV Parallel Fan-Powered Terminal Units: Experimental Results and Models NY-08-013 (RP-1292) Performance of VAV Parallel Fan-Powered Terminal Units: Experimental Results and Models James C. Furr Dennis L. O Neal, PhD, PE Michael A. Davis Fellow ASHRAE John A. Bryant, PhD, PE

More information

Objective: Students will investigate rate of change (slope) using spring data from RC cars.

Objective: Students will investigate rate of change (slope) using spring data from RC cars. Objective: Students will investigate rate of change (slope) using spring data from RC cars. About the Lesson: In NASCAR, the selection of springs (and spring rate) determines the ride height of the car.

More information

OPTIMIZATION OF BIODIESEL PRODCUTION FROM TRANSESTERIFICATION OF WASTE COOKING OILS USING ALKALINE CATALYSTS

OPTIMIZATION OF BIODIESEL PRODCUTION FROM TRANSESTERIFICATION OF WASTE COOKING OILS USING ALKALINE CATALYSTS OPTIMIZATION OF BIODIESEL PRODCUTION FROM TRANSESTERIFICATION OF WASTE COOKING OILS USING ALKALINE CATALYSTS M.M. Zamberi 1,2 a, F.N.Ani 1,b and S. N. H. Hassan 2,c 1 Department of Thermodynamics and Fluid

More information

Study of Fuel Economy Standard and Testing Procedure for Motor Vehicles in Thailand

Study of Fuel Economy Standard and Testing Procedure for Motor Vehicles in Thailand Study of Fuel Economy Standard and Testing Procedure for Motor Vehicles in Thailand MR.WORAWUTH KOVONGPANICH TESTING MANAGER THAILAND AUTOMOTIVE INSTITUTE June 20 th, 2014 Overview Background Terminology

More information

. Enter. Model Summary b. Std. Error. of the. Estimate. Change. a. Predictors: (Constant), Emphaty, reliability, Assurance, responsive, Tangible

. Enter. Model Summary b. Std. Error. of the. Estimate. Change. a. Predictors: (Constant), Emphaty, reliability, Assurance, responsive, Tangible LAMPIRAN Variables Entered/Removed b Variables Model Variables Entered Removed Method 1 Emphaty, reliability, Assurance, responsive, Tangible a. Enter a. All requested variables entered. b. Dependent Variable:

More information

PREPARING YOUR PITCH. Arnold Chen, Managing Director Burton D. Morgan Center for Entrepreneurship

PREPARING YOUR PITCH. Arnold Chen, Managing Director Burton D. Morgan Center for Entrepreneurship PREPARING YOUR PITCH Arnold Chen, Managing Director Burton D. Morgan Center for Entrepreneurship OUTLINE Before your pitch The Pitch After your pitch Fun real examples WHAT IS YOUR GOAL? WHY ARE YOU MEETING?

More information

Pros and cons of hybrid cars

Pros and cons of hybrid cars GRADE 7 Hybrid cars are increasingly popular. In this lesson, students investigate the costs and benefits of using hybrid cars over gasoline-powered cars by comparing the cost and environmental impact

More information

US biofuel Indicators and a changing market dynamic

US biofuel Indicators and a changing market dynamic US biofuel Indicators and a changing market dynamic Seth Meyer AMIS October 1-2, 13, Rome Movement of Ag and Energy Ethanol production and capacity 16 35 Crude oil and maize prices 14 3 billion gallons

More information

1103 Period 16: Electrical Resistance and Joule Heating

1103 Period 16: Electrical Resistance and Joule Heating Name Section 1103 Period 16: Electrical Resistance and Joule Heating Activity 16.1: What Does the Electrical Resistance of a Wire Depend Upon? 1) Measuring resistance a) Resistor length, L Use a multimeter

More information

Exercise 2. Discharge Characteristics EXERCISE OBJECTIVE DISCUSSION OUTLINE DISCUSSION. Cutoff voltage versus discharge rate

Exercise 2. Discharge Characteristics EXERCISE OBJECTIVE DISCUSSION OUTLINE DISCUSSION. Cutoff voltage versus discharge rate Exercise 2 Discharge Characteristics EXERCISE OBJECTIVE When you have completed this exercise, you will be familiar with the discharge characteristics of lead-acid batteries. DISCUSSION OUTLINE The Discussion

More information

For full credit, show all your work.

For full credit, show all your work. ccelerated Review 8: Measurement & Statistics Name: For full credit, show all your work. hoose the answers that best match the following measurements. quarter would weight about: 1. a kilogram a gram a

More information

AN EVALUATION OF THE 50 KM/H DEFAULT SPEED LIMIT IN REGIONAL QUEENSLAND

AN EVALUATION OF THE 50 KM/H DEFAULT SPEED LIMIT IN REGIONAL QUEENSLAND AN EVALUATION OF THE 50 KM/H DEFAULT SPEED LIMIT IN REGIONAL QUEENSLAND by Simon Hosking Stuart Newstead Effie Hoareau Amanda Delaney November 2005 Report No: 265 Project Sponsored By ii MONASH UNIVERSITY

More information

HELICOPTER OPERATIONS WITHIN THE LONDON HEATHROW AND LONDON CITY CONTROL ZONES

HELICOPTER OPERATIONS WITHIN THE LONDON HEATHROW AND LONDON CITY CONTROL ZONES Column Title Explanation 1 2 Total daily number of helicopter flights recorded within the London Heathrow and London City CTRs. 3 to 6 Total daily number of helicopter flights recorded within the London

More information

Growth cycles in Industrial production (IIP) (percentage deviation from trend*, seasonally adjusted) Sep 88 Sep 94. Dec 96. Mar 96

Growth cycles in Industrial production (IIP) (percentage deviation from trend*, seasonally adjusted) Sep 88 Sep 94. Dec 96. Mar 96 China The reference series The reference series used for constructing OECD Composite Leading (CLI) for China is the monthly index of industrial production (IIP). The IIP series starts in 1978, it is timely

More information

SINGLE-PHASE CONVECTIVE HEAT TRANSFER AND PRESSURE DROP COEFFICIENTS IN CONCENTRIC ANNULI

SINGLE-PHASE CONVECTIVE HEAT TRANSFER AND PRESSURE DROP COEFFICIENTS IN CONCENTRIC ANNULI UNIVERSITY OF PRETORIA SOUTH AFRICA SINGLE-PHASE CONVECTIVE HEAT TRANSFER AND PRESSURE DROP COEFFICIENTS IN CONCENTRIC ANNULI By: Warren Van Zyl Supervisors: Dr J Dirker Prof J.P Meyer 1 Topic Overview

More information

Project Advisory Committee

Project Advisory Committee Meredith US 3/NH 25 Improvements Transportation Planning Study Project Advisory Committee March 18, 2008 Meredith US 3/NH 25 Improvements Transportation Planning Study Meeting Agenda Welcome Traffic Model

More information

tool<-read.csv(file="d:/chilo/regression 7/tool.csv", header=t) tool

tool<-read.csv(file=d:/chilo/regression 7/tool.csv, header=t) tool Regression nalysis lab 7 1 Indicator variables 1.1 Import data tool

More information

Objectives. Materials TI-73 CBL 2

Objectives. Materials TI-73 CBL 2 . Objectives To understand the relationship between dry cell size and voltage Activity 4 Materials TI-73 Unit-to-unit cable Voltage from Dry Cells CBL 2 Voltage sensor New AAA, AA, C, and D dry cells Battery

More information

2.007 Design and Manufacturing I

2.007 Design and Manufacturing I MIT OpenCourseWare http://ocw.mit.edu 2.7 Design and Manufacturing I Spring 29 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Page 1 of 8 2.7 Design

More information

Laboratory 5: Electric Circuits Prelab

Laboratory 5: Electric Circuits Prelab Phys 132L Fall 2018 Laboratory 5: Electric Circuits Prelab 1 Current and moving charges Atypical currentinanelectronic devicemightbe5.0 10 3 A.Determinethenumber of electrons that pass through the device

More information

Identify Formula for Throughput with Multi-Variate Regression

Identify Formula for Throughput with Multi-Variate Regression DECISION SCIENCES INSTITUTE Using multi-variate regression and simulation to identify a generic formula for throughput of flow manufacturing lines with identical stations Samrawi Berhanu Gebermedhin and

More information

CEMENT AND CONCRETE REFERENCE LABORATORY PROFICIENCY SAMPLE PROGRAM

CEMENT AND CONCRETE REFERENCE LABORATORY PROFICIENCY SAMPLE PROGRAM CEMENT AND CONCRETE REFERENCE LABORATORY PROFICIENCY SAMPLE PROGRAM Final Report ASR ASTM C1260 Proficiency Samples Number 5 and Number 6 August 2018 www.ccrl.us www.ccrl.us August 24, 2018 TO: Participants

More information

Using Statistics To Make Inferences 6. Wilcoxon Matched Pairs Signed Ranks Test. Wilcoxon Rank Sum Test/ Mann-Whitney Test

Using Statistics To Make Inferences 6. Wilcoxon Matched Pairs Signed Ranks Test. Wilcoxon Rank Sum Test/ Mann-Whitney Test Using Statistics To Make Inferences 6 Summary Non-parametric tests Wilcoxon Signed Ranks Test Wilcoxon Matched Pairs Signed Ranks Test Wilcoxon Rank Sum Test/ Mann-Whitney Test Goals Perform and interpret

More information

Modeling Ignition Delay in a Diesel Engine

Modeling Ignition Delay in a Diesel Engine Modeling Ignition Delay in a Diesel Engine Ivonna D. Ploma Introduction The object of this analysis is to develop a model for the ignition delay in a diesel engine as a function of four experimental variables:

More information

Grand Challenge VHG Test Article 2 Test 4

Grand Challenge VHG Test Article 2 Test 4 Grand Challenge Prediction Article #: TA2 Test 4 Test Apparatus: VHG Organization: ARDEC Grand Challenge VHG Test Article 2 Test 4 Miroslav Tesla, Jennifer A. Cordes, Janet Wolfson RDAR-MEF-E, Building

More information

Algebra 1 Predicting Patterns & Examining Experiments. Unit 2: Maintaining Balance Section 1: Balance with Addition

Algebra 1 Predicting Patterns & Examining Experiments. Unit 2: Maintaining Balance Section 1: Balance with Addition Algebra 1 Predicting Patterns & Examining Experiments Unit 2: Maintaining Balance Section 1: Balance with Addition What is the weight ratio of basketballs to softballs? (Partner Discussion) Have students

More information

Measurements. In part 1 the markings of the 4 devices will be examined.

Measurements. In part 1 the markings of the 4 devices will be examined. Measurements In this lab we will be measuring 9 ml of water in 4 devices; a beaker, a 10 ml graduated cylinder, a 25 ml graduated cylinder, and a 100 ml graduated cylinder. I expect that the 10 ml graduated

More information

Represent and solve problems involving addition and subtraction. Work with equal groups of objects to gain foundations for multiplication.

Represent and solve problems involving addition and subtraction. Work with equal groups of objects to gain foundations for multiplication. Correlation S T A N D A R D S F O R M A T H E M A T I C A L C O N T E N T This correlation includes Classroom Routines but does not include ongoing review in Daily Practice and Homework. Domain 2.OA Operations

More information

EE4351 Aircraft Electrical and Actuation System

EE4351 Aircraft Electrical and Actuation System EE4351 Aircraft Electrical and Actuation System The syllabus: Electrical Systems - Aircraft electrical and distribution system, Aircraft power generation, Ground Power Supply, Power distribution, Power

More information

Cylinder Balance and Percent Changes Lesson 11

Cylinder Balance and Percent Changes Lesson 11 Cylinder Balance and Percent Changes Lesson 11 Remember: Pretty Please My Dear Aunt Sally (From left to right; Parentheses; Power; Multiply; Divide; Add, Subtract) Identify The Math, Math Terms, Vocabulary,

More information

Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear. Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method

Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear. Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method Econometrics for Health Policy, Health Economics, and Outcomes Research Topic 5 Lecture 3 Estimating Policy Effects via the Simple Linear Regression Model (SLRM) and the Ordinary Least Squares (OLS) Method

More information

Important Formulas. Discrete Probability Distributions. Probability and Counting Rules. The Normal Distribution. Confidence Intervals and Sample Size

Important Formulas. Discrete Probability Distributions. Probability and Counting Rules. The Normal Distribution. Confidence Intervals and Sample Size blu38582_if_1-8.qxd 9/27/10 9:19 PM Page 1 Important Formulas Chapter 3 Data Description Mean for individual data: Mean for grouped data: Standard deviation for a sample: X2 s X n 1 or Standard deviation

More information

Appendix F. Ship Drift Analysis West Coast of North America: Alaska to Southern California HAZMAT Report ; April 2000

Appendix F. Ship Drift Analysis West Coast of North America: Alaska to Southern California HAZMAT Report ; April 2000 Appendix F Ship Drift Analysis West Coast of North America: Alaska to Southern California HAZMAT Report 2000-2; April 2000 2.2 Drift Factors When its propulsion or steering device fails, a ship will drift

More information

The Truth About Light Trucks

The Truth About Light Trucks RISK Despite critics claims, SUVs are saving lives. The Truth About Light Trucks The american love affair with the automobile has grown to include the class of vehicles known as light trucks, which includes

More information

Zone 1. Zone 3. Zone 2. PROBLEM 1 (40 points) Fixed Load Auction (No transmission limits considered):

Zone 1. Zone 3. Zone 2. PROBLEM 1 (40 points) Fixed Load Auction (No transmission limits considered): EE5721 Practice Final Exam PROBLEM 1 (40 points) A power system consists of three zones. Each zone will be represented by a single generator and a single load on a single bus as shown below: GA GC LA LC

More information

The goal of the study is to investigate the effect of spring stiffness on ride height and aerodynamic balance.

The goal of the study is to investigate the effect of spring stiffness on ride height and aerodynamic balance. OptimumDynamics - Case Study Investigating Aerodynamic Distribution Goals Investigate the effect of springs on aerodynamic distribution Select bump stop gap Software OptimumDynamics The case study is broken

More information

The Session.. Rosaria Silipo Phil Winters KNIME KNIME.com AG. All Right Reserved.

The Session.. Rosaria Silipo Phil Winters KNIME KNIME.com AG. All Right Reserved. The Session.. Rosaria Silipo Phil Winters KNIME 2016 KNIME.com AG. All Right Reserved. Past KNIME Summits: Merging Techniques, Data and MUSIC! 2016 KNIME.com AG. All Rights Reserved. 2 Analytics, Machine

More information

Appendices for: Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS with Product Indicators

Appendices for: Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS with Product Indicators Appendices for: Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS with Product Indicators Dale Goodhue Terry College of Business MIS Department University of Georgia

More information

Economics of Integrating Renewables DAN HARMS MANAGER OF RATE, TECHNOLOGY & ENERGY POLICY SEPTEMBER 2017

Economics of Integrating Renewables DAN HARMS MANAGER OF RATE, TECHNOLOGY & ENERGY POLICY SEPTEMBER 2017 Economics of Integrating Renewables DAN HARMS MANAGER OF RATE, TECHNOLOGY & ENERGY POLICY SEPTEMBER 2017 Presentation Outline Understanding LPEA s expenses and what drives them Economics of net metering

More information

Linking the Kansas KAP Assessments to NWEA MAP Growth Tests *

Linking the Kansas KAP Assessments to NWEA MAP Growth Tests * Linking the Kansas KAP Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. February 2016 Introduction Northwest Evaluation Association (NWEA

More information

When the points on the graph of a relation lie along a straight line, the relation is linear

When the points on the graph of a relation lie along a straight line, the relation is linear KEY CONCEPTS When the points on the graph of a relation lie along a straight line, the relation is linear A linear relationship implies equal changes over equal intervals any linear model can be represented

More information

Assignment 3 Hydraulic Brake Systems

Assignment 3 Hydraulic Brake Systems Name(s) Assign_3_Hydraulics Assignment 3 Hydraulic Brake Systems BE SURE TO SAVE THIS FILE before, during and after completing your work. (Hint if you write your name, then save and close this, your name

More information

Algebra 2 Plus, Unit 10: Making Conclusions from Data Objectives: S- CP.A.1,2,3,4,5,B.6,7,8,9; S- MD.B.6,7

Algebra 2 Plus, Unit 10: Making Conclusions from Data Objectives: S- CP.A.1,2,3,4,5,B.6,7,8,9; S- MD.B.6,7 Algebra 2 Plus, Unit 10: Making Conclusions from Data Objectives: S- CP.A.1,2,3,4,5,B.6,7,8,9; S- MD.B.6,7 Learner Levels Level 1: I can simulate an experiment. Level 2: I can interpret two- way tables.

More information

Inquiry-Based Physics in Middle School. David E. Meltzer

Inquiry-Based Physics in Middle School. David E. Meltzer Inquiry-Based Physics in Middle School David E. Meltzer Mary Lou Fulton Teachers College Arizona State University Mesa, Arizona U.S.A. Supported in part by a grant from Mary Lou Fulton Teachers College

More information

: ( .

: ( . 2 27 ( ) 2 3 4 2 ( ) 59 Y n n U i ( ) & smith H 98 Draper N Curran PJ,bauer DJ & Willoughby Kam,Cindy &Robert 23 MT24 Jaccard,J & Rebert T23 Franzese 23 Aiken LS & West SG 99 " Multiple Regression Testing

More information

Linking the Mississippi Assessment Program to NWEA MAP Tests

Linking the Mississippi Assessment Program to NWEA MAP Tests Linking the Mississippi Assessment Program to NWEA MAP Tests February 2017 Introduction Northwest Evaluation Association (NWEA ) is committed to providing partners with useful tools to help make inferences

More information

Student Exploration: Advanced Circuits

Student Exploration: Advanced Circuits Name: Date: Student Exploration: Advanced Circuits [Note to teachers and students: This Gizmo was designed as a follow-up to the Circuits Gizmo. We recommend doing that activity before trying this one.]

More information

Math 20 2 Statistics Review for the Final

Math 20 2 Statistics Review for the Final This is a long review. Attempt each style of question, but if you know how to do the question, move on to more challenging ones. DO NOT GO THROUGH THIS REVIEW QUESTION BY QUESTION! 1. Joel researched the

More information

DaimlerChrysler Alternative Particulate Measurement page 1/8

DaimlerChrysler Alternative Particulate Measurement page 1/8 DaimlerChrysler Alternative Particulate Measurement page 1/8 Investigation of Alternative Methods to Determine Particulate Mass Emissions Dr. Oliver Mörsch Petra Sorsche DaimlerChrysler AG Background and

More information

Figure 1 Fuel Injection Pump II. EXPERIMENTAL DETAILS. A. Design of experiments

Figure 1 Fuel Injection Pump II. EXPERIMENTAL DETAILS. A. Design of experiments Optimization of Fuel Injection Pump Parameters of TATA 63 & TATA 609 Engine Using Diesel & Biodiesel Samiyoddin Siddiqui, 2 M.Shakebuddin and 3 H.A.Hussain M.Tech Student, 2,3 Assistant Professor,,2,3

More information

2018 Linking Study: Predicting Performance on the NSCAS Summative ELA and Mathematics Assessments based on MAP Growth Scores

2018 Linking Study: Predicting Performance on the NSCAS Summative ELA and Mathematics Assessments based on MAP Growth Scores 2018 Linking Study: Predicting Performance on the NSCAS Summative ELA and Mathematics Assessments based on MAP Growth Scores November 2018 Revised December 19, 2018 NWEA Psychometric Solutions 2018 NWEA.

More information

The Magnetic Field in a Coil. Evaluation copy. Figure 1. square or circular frame Vernier computer interface momentary-contact switch

The Magnetic Field in a Coil. Evaluation copy. Figure 1. square or circular frame Vernier computer interface momentary-contact switch The Magnetic Field in a Coil Computer 25 When an electric current flows through a wire, a magnetic field is produced around the wire. The magnitude and direction of the field depends on the shape of the

More information

Voting Draft Standard

Voting Draft Standard page 1 of 7 Voting Draft Standard EL-V1M4 Sections 1.7.1 and 1.7.2 March 2013 Description This proposed standard is a modification of EL-V1M4-2009-Rev1.1. The proposed changes are shown through tracking.

More information

Drilling Example: Diagnostic Plots

Drilling Example: Diagnostic Plots Math 3080 1. Treibergs Drilling Example: Diagnostic Plots Name: Example March 1, 2014 This data is taken from Penner & Watts, Mining Information, American Statistician 1991, as quoted by Levine, Ramsey

More information

HSC Physics motors and generators magnetic flux and induction

HSC Physics motors and generators magnetic flux and induction PD32a HSC Physics motors and generators student name....................... Monday, 30 May 2016 number о number о 1 1 c 26 2 2 17 27 3 3 18 28 4 4 19 29 5 5 6 6 7 7 8 8 9 9 10 a 10 b 11 c 12 d 13 e 14

More information

Free Pre-Algebra Lesson 44! page 1. A bottle of salad dressing, consisting of oil and vinegar.

Free Pre-Algebra Lesson 44! page 1. A bottle of salad dressing, consisting of oil and vinegar. Free Pre-Algebra Lesson 44 page 1 Lesson 44 Percents in Mixtures Chemists may specify the strength of a solution by using a percent. For example, you can buy isopropyl alcohol at the drug store in a 91%

More information

WIM #41 CSAH 14, MP 14.9 CROOKSTON, MINNESOTA APRIL 2014 MONTHLY REPORT

WIM #41 CSAH 14, MP 14.9 CROOKSTON, MINNESOTA APRIL 2014 MONTHLY REPORT WIM #41 CSAH 14, MP 14.9 CROOKSTON, MINNESOTA APRIL 2014 MONTHLY REPORT In order to understand the vehicle classes and groupings, the MnDOT Vehicle Classification Scheme and the Vehicle Classification

More information

PREDICTION OF FUEL CONSUMPTION

PREDICTION OF FUEL CONSUMPTION PREDICTION OF FUEL CONSUMPTION OF AGRICULTURAL TRACTORS S. C. Kim, K. U. Kim, D. C. Kim ABSTRACT. A mathematical model was developed to predict fuel consumption of agricultural tractors using their official

More information

An Experimental Study on the Efficiency of Bicycle Transmissions

An Experimental Study on the Efficiency of Bicycle Transmissions An Experimental Study on the Efficiency of Bicycle Transmissions R. Bolen and C. M. Archibald Grove City College, Grove City, PA Abstract: The objective of this project is to measure the efficiencies of

More information

Impact of Traffic Congestion on Bus Travel Time in Northern New Jersey

Impact of Traffic Congestion on Bus Travel Time in Northern New Jersey Impact of Traffic Congestion on Bus Travel Time in Northern New Jersey Claire E. McKnight, Herbert S. Levinson, Kaan Ozbay, Camille Kamga, and Robert E. Paaswell Traffic congestion in Northern New Jersey

More information

Correlation to the Common Core State Standards

Correlation to the Common Core State Standards Correlation to the Common Core State Standards Go Math! 2011 Grade 3 Common Core is a trademark of the National Governors Association Center for Best Practices and the Council of Chief State School Officers.

More information

End-use petroleum product prices and average crude oil import costs January 2010

End-use petroleum product prices and average crude oil import costs January 2010 January 21 International Energy Agency L'Agence internationale de l'énergie 9, rue de la Fédération, 75739 PARIS CEDEX 15 FRANCE prices@iea.org 18 e-mail: prices@iea.org 14 Petroleum products (USD/unit)

More information

Storage in the energy market

Storage in the energy market Storage in the energy market Richard Green Energy Transitions 216, Trondheim 1 including The long-run impact of energy storage on prices and capacity Richard Green and Iain Staffell Imperial College Business

More information

Effects of differentiation in car purchase tax based on carbon-dioxide emissions in Finland

Effects of differentiation in car purchase tax based on carbon-dioxide emissions in Finland Effects of differentiation in car purchase tax based on carbon-dioxide emissions in Finland Andrey Zhukov University of Helsinki November 14, 2013 Background As of January 2008 new approach to car purchase

More information