# 1. Suppose that a score on a final exam depends upon attendance and unobserved factors that affect exam performance (such as student ability).

Save this PDF as:

Size: px
Start display at page:

Download "1. Suppose that a score on a final exam depends upon attendance and unobserved factors that affect exam performance (such as student ability)."

## Transcription

1 Examples of Questions on Regression Analysis: 1. Suppose that a score on a final exam depends upon attendance and unobserved factors that affect exam performance (such as student ability). Then,. When would you expect this model to satisfy the assumption E( attendance) = 0? (That is, when would you expect the conditional expectation of the unobserved error term to be zero?) Answer: you would expect this to be the case if the unobserved variables that can affect exam performance (such as student ability) are NOT correlated with attendance. This seems pretty unlikely, though. 2. Let kids denote the number of children ever born to a woman, and let educ denote years of education for this woman. A simple model relating fertility to years of education is:. a. What kinds of factors are contained in? Are these likely to be correlated with the level of education? b. Will a simple regression analysis uncover the ceteris paribus effect of education on fertility? Explain. Answer: a. Other factors that might be contained in the error term would be age of the mother, income level, religion. Surely age and income would be highly correlated with education. b. If you ran the above regression, you wouldn t be controlling for any other effects (there are no other effects in the model: no other RHS variables) so you would NOT be looking at the ceteris paribus effect of education on fertility. A simple regression would tell you the OVER-ALL effect of education on kids (controlling for nothing else at all). 3. In the estimated linear consumption function: the (estimated) marginal propensity to consume (MPC) out of income is simply the slope, and the average propensity to consume out of income (APC) is given by. Using 100 families randomly chosen and taking their annual income and consumption data (both measured in dollars) the following equation is obtained:

2 Answer: a. Interpret the intercept in this equation, and comment on its sign and magnitude. b. What is the prediction consumption when family income is \$30,000? a. xxx b. predicted consumption = (30,000) 4. The OLS fitted line explaining college GPA in terms of high school GPA and ACT score is estimated as: If the average high school GPA is about 3.4 and the average ACT score is about 24.2, what is the average college GPA in the sample? Answer: (3.4) (24.2) = Suppose there are two candidates for office and we wanted to explain the share of votes that candidate A gets and we model it as: Where expenda is the campaign expenditures spent by candidate A (and expendb is the campaign expenditures of candidate B), and sharea = expenda/totalexpend where totalexpend is the TOTAL amount of campaign expenditures put out by both candidates. Does this model violate the perfect multicollinearity assumption? Answer: No. There is no perfect linear relationship between the explanatory variables. (There is, however, a perfect NON-LINEAR relationship between the explanatory variables... but that s okay as far as OLS is concerned.) 6. Suppose that you postulate a model explaining final exam score in terms of class attendance. Thus, the dependent variable is final exam score, and the key explanatory variable is number of classes attended. To control for student abilities and efforts outside the classroom, you include among the explanatory variables cumulative GPA, SAT score, and measures of highschool performance. Someone says, You cannot hope to learn anything from this exercise because cumulative GPA, SAT score and highschool performance are likely to be highly collinear. What should be your response? Answer: You re interested in how class attendance affects final exam scores. Even if GPA, SAT score and highschool performance are highly collinear (and therefore might have low t-statistics because OLS can t sort out their relative contribution to

3 explaining the variation in final exam score), that will NOT affect the OLS estimator on class attendance. The OLS estimator will still be unbiased, as will the variance on the coefficient estimator. If we were to DROP those variables and they were correlated with attendance, then we d really be in trouble: we would have omitted variable bias. 7. Data on working men was used to estimate the following equation: were educ = years of schooling, sibs = number of siblings, meduc = mother s years of schooling, feduc = father s years of scooling. a. Does sibs have the expected effect? Explain. Holding meduc and feduc fixed, by how much does sibs have to increase to reduce predicted years of education by one year? (A non integer answer is acceptable here.) The estimated model shows that as the number of siblings is negatively related to the education level of a working man. Holding the mother and father s levels of education fixed, a increasing in siblings will reduce the education level by 1 year. b. Discuss the interpretation of the coefficient on medec. There is a positive relationship between a mother s level of education and her son s level of education. For every additional year of mother s education, holding the father s level of education fixed and the number of siblings, a son s education will go up by 0.13 years. c. Suppose that Man A has no siblings, and his mother and father each have 12 years of schooling. Man B has no siblings, and his mother and father each have 16 years of schooling. What is the predicted difference in years of education between and B? Predicted difference between Man B and Man A s education level: (16) (16) (12) (12) = 4(0.131) + 4 (021) = some number. 8. The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the tradeoff between time spent sleeping and working and to look at other factors affecting sleep: where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. a. If adults trade off sleep for work, what is the sign of 1?

4 If there is a sleep-work trade off, then the sign on 1 should be negative. b. What signs do you think 2 and 3 will have? 2 might be negative: the more education you have, the more demanding your job, the less sleep you get...although this could go the other way as well. 3 is probably negative: the older you get, the less sleep you need (and get!). c. Suppose the following equation is estimated: If someone works for five more hours per week, by how many minutes is sleep predicted to fall? Is this a large trade-off? Holding all other things constant, sleep will fall by 5(0.148)(60) minutes. d. Discuss the sign and magnitude of the estimated coefficient on educ. More education, less sleep. Very depressing. 4 years of college means 44 minutes less sleep (approximately) compared to a high school graduate. e. Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk? Only 11.3% of the variation in minutes of sleep is explained by the model. Other factors: NUMBER OF CHILDREN. Almost surely this would be correlated with totwrk. 9. The median starting salary for new law school graduates is determined by: where LSAT is the median LSAT score for the graduating class, GPA is the median college GPA for the class, libvol is the number of volumes in the law school library, cost is the annual cost of attending law school, and rank is a law school ranking (with rank = 1 being the best). a. Explain why we expect 5 0. Better ranked school (better school), higher wage. Rank is measured backwards with 1 being the best so we d expect a negative relationship.

5 b. What signs do you expect for the other slope coefficients? Justify your answers. c. Suppose the following equation is estimated: What is the predicted ceteris paribus difference in salary for schools with a median GPA different by one point? (Report your answer as a percent.) 24.8% d. Interpret the coefficient on the variable log(libvol). It s an elasticity. e. Would you say it is better to attend a higher ranked law school? How much is a difference in ranking of 20 worth in terms of predicted starting salary? It s better to be at a better law school (ranking closer to 1). A 20 point difference in ranking leads to a 20(0.0033)% difference in starting salary. 10. Suppose the average worker productivity at manufacturing firms (avgprod) depends on two factors: average hours of training (avgtrain) and average worker ability (avgabil): Assume that this equation satisfies the Gauss-Markov assumptions. If grants have been given to firms whose workers have less than average ability, so that avgtrain and avgabil are negatively correlated, what is the likely bias in obtained from the simple regression of avgprod on avgtrain? See notes on bias given in the multiple regression handout. 11. The following equation represents the effects of tax revenue mix on subsequent employment growth for the population of counties in the United States: where growth is the percentage change in employment from 1980 to 1990, share P is the share of property taxes in total tax revenue, share I is the share of income tax revenues, and share s is the share of sales tax revenues. All of these variables are measured in 1980 dollars. The omitted share, share F, includes fees and miscellaneous taxes. By definition, the four shares add up to one. Other

6 factors would include expenditures on education, infrastructure, and so on (all measured in 1980\$s). a. Why must be omit one of the tax share variables from the equation? Perfect collinearity would result. b. Give a careful interpretation of 1. This is tricky. It will be the incremental change in employment if property tax shares were to increase by 1%, holding income tax and sales tax shares constant.

### Econometrics Problem Set #2

Econometrics Problem Set #2 Nathaniel Higgins nhiggins@jhu.edu Assignment The homework assignment was to read chapter 2 and hand in answers to the following problems at the end of the chapter: 2.1 2.5

### Solución del Examen Tipo: 1

Solución del Examen Tipo: 1 Universidad Carlos III de Madrid ECONOMETRICS Academic year 2009/10 FINAL EXAM May 17, 2010 DURATION: 2 HOURS 1. Assume that model (III) verifies the assumptions of the classical

### C2.1. (i) (5 marks) The average participation rate is , the average match rate is summ prate

BOSTON COLLEGE Department of Economics EC 228 01 Econometric Methods Fall 2008, Prof. Baum, Ms. Phillips (tutor), Mr. Dmitriev (grader) Problem Set 2 Due at classtime, Thursday 2 Oct 2008 2.4 (i)(5 marks)

### IAPRI Quantitative Analysis Capacity Building Series. Multiple regression analysis & interpreting results

IAPRI Quantitative Analysis Capacity Building Series Multiple regression analysis & interpreting results How important is R-squared? R-squared Published in Agricultural Economics 0.45 Best article of the

### Answer: C. The strength of a correlation does not change if units change by a linear transformation such as: Fahrenheit = 32 + (5/9) * Centigrade

Statistics Quiz Correlation and Regression -- ANSWERS 1. Temperature and air pollution are known to be correlated. We collect data from two laboratories, in Boston and Montreal. Boston makes their measurements

### CHAPTER 9: SERIAL CORRELATION

Serial correlation (or autocorrelation) is the violation of Assumption 4 (observations of the error term are uncorrelated with each other). Pure Serial Correlation This type of correlation tends to be

### Statistics 151 Practice Midterm 1 Mike Kowalski

Statistics 151 Practice Midterm 1 Mike Kowalski Statistics 151 Practice Midterm 1 Multiple Choice (50 minutes) Instructions: 1. This is a closed book exam. 2. You may use the STAT 151 formula sheets and

### Regression. In this class we will:

AMS 5 REGRESSION Regression The idea behind the calculation of the coefficient of correlation is that the scatter plot of the data corresponds to a cloud that follows a straight line. This idea can be

### Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the

Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the explanatory variables is jointly determined with the dependent

### Chapter 5 Estimating Demand Functions

Chapter 5 Estimating Demand Functions 1 Why do you need statistics and regression analysis? Ability to read market research papers Analyze your own data in a simple way Assist you in pricing and marketing

### 17. SIMPLE LINEAR REGRESSION II

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

### Logs Transformation in a Regression Equation

Fall, 2001 1 Logs as the Predictor Logs Transformation in a Regression Equation The interpretation of the slope and intercept in a regression change when the predictor (X) is put on a log scale. In this

### Premaster Statistics Tutorial 4 Full solutions

Premaster Statistics Tutorial 4 Full solutions Regression analysis Q1 (based on Doane & Seward, 4/E, 12.7) a. Interpret the slope of the fitted regression = 125,000 + 150. b. What is the prediction for

### WIN AT ANY COST? How should sports teams spend their m oney to win more games?

Mathalicious 2014 lesson guide WIN AT ANY COST? How should sports teams spend their m oney to win more games? Professional sports teams drop serious cash to try and secure the very best talent, and the

### Econometrics Simple Linear Regression

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

### Instrumental Variables Regression. Instrumental Variables (IV) estimation is used when the model has endogenous s.

Instrumental Variables Regression Instrumental Variables (IV) estimation is used when the model has endogenous s. IV can thus be used to address the following important threats to internal validity: Omitted

### CHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression

Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the

### Econometrics. Week 9. Fall Institute of Economic Studies Faculty of Social Sciences Charles University in Prague

Econometrics Week 9 Institute of Economic Studies Faculty of Social Sciences Charles University in Prague Fall 2012 1 / 21 Recommended Reading For the today Simultaneous Equations Models Chapter 16 (pp.

### SELF-TEST: SIMPLE REGRESSION

ECO 22000 McRAE SELF-TEST: SIMPLE REGRESSION Note: Those questions indicated with an (N) are unlikely to appear in this form on an in-class examination, but you should be able to describe the procedures

### NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )

Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates

### Econometrics Problem Set #3

Econometrics Problem Set #3 Nathaniel Higgins nhiggins@jhu.edu Assignment The assignment was to read chapter 3 and hand in answers to the following problems at the end of the chapter: C3.1 C3.8. C3.1 A

### Financial Risk Management Exam Sample Questions/Answers

Financial Risk Management Exam Sample Questions/Answers Prepared by Daniel HERLEMONT 1 2 3 4 5 6 Chapter 3 Fundamentals of Statistics FRM-99, Question 4 Random walk assumes that returns from one time period

### c. Construct a boxplot for the data. Write a one sentence interpretation of your graph.

MBA/MIB 5315 Sample Test Problems Page 1 of 1 1. An English survey of 3000 medical records showed that smokers are more inclined to get depressed than non-smokers. Does this imply that smoking causes depression?

### Notes on indifference curve analysis of the choice between leisure and labor, and the deadweight loss of taxation. Jon Bakija

Notes on indifference curve analysis of the choice between leisure and labor, and the deadweight loss of taxation Jon Bakija This example shows how to use a budget constraint and indifference curve diagram

### A Comparison of Course Delivery Methods: An Exercise in Experimental Economics

JOURNAL OF ECONOMICS AND FINANCE EDUCATION Volume 7 Number 1 Summer 2008 21 A Comparison of Course Delivery Methods: An Exercise in Experimental Economics Roy Howsen and Stephen Lile 1 ABSTRACT This paper

### Regression analysis in practice with GRETL

Regression analysis in practice with GRETL Prerequisites You will need the GNU econometrics software GRETL installed on your computer (http://gretl.sourceforge.net/), together with the sample files that

### Elementary Statistics. Scatter Plot, Regression Line, Linear Correlation Coefficient, and Coefficient of Determination

Scatter Plot, Regression Line, Linear Correlation Coefficient, and Coefficient of Determination What is a Scatter Plot? A Scatter Plot is a plot of ordered pairs (x, y) where the horizontal axis is used

### Chapter 7: Dummy variable regression

Chapter 7: Dummy variable regression Why include a qualitative independent variable?........................................ 2 Simplest model 3 Simplest case.............................................................

### DEPARTMENT OF PSYCHOLOGY UNIVERSITY OF LANCASTER MSC IN PSYCHOLOGICAL RESEARCH METHODS ANALYSING AND INTERPRETING DATA 2 PART 1 WEEK 9

DEPARTMENT OF PSYCHOLOGY UNIVERSITY OF LANCASTER MSC IN PSYCHOLOGICAL RESEARCH METHODS ANALYSING AND INTERPRETING DATA 2 PART 1 WEEK 9 Analysis of covariance and multiple regression So far in this course,

### 17.0 Linear Regression

17.0 Linear Regression 1 Answer Questions Lines Correlation Regression 17.1 Lines The algebraic equation for a line is Y = β 0 + β 1 X 2 The use of coordinate axes to show functional relationships was

### Chapter 7: Simple linear regression Learning Objectives

Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -

### Introduction to Regression and Data Analysis

Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it

### Economics 345 Applied Econometrics

Economics 345 Applied Econometrics Lab 3: Some Useful Functional Forms for Regression Analysis Prof: Martin Farnham TAs: Joffré Leroux, Rebecca Wortzman, Yiying Yang Open EViews, and open the EViews workfile,

### 1 OLS under Measurement Error

ECON 370: Measurement Error 1 Violations of Gauss Markov Assumptions: Measurement Error Econometric Methods, ECON 370 1 OLS under Measurement Error We have found out that another source of endogeneity

### August 2012 EXAMINATIONS Solution Part I

August 01 EXAMINATIONS Solution Part I (1) In a random sample of 600 eligible voters, the probability that less than 38% will be in favour of this policy is closest to (B) () In a large random sample,

### Correlation. What Is Correlation? Perfect Correlation. Perfect Correlation. Greg C Elvers

Correlation Greg C Elvers What Is Correlation? Correlation is a descriptive statistic that tells you if two variables are related to each other E.g. Is your related to how much you study? When two variables

### Models for Longitudinal and Clustered Data

Models for Longitudinal and Clustered Data Germán Rodríguez December 9, 2008, revised December 6, 2012 1 Introduction The most important assumption we have made in this course is that the observations

### An Analysis of the Effect of Income on Life Insurance. Justin Bryan Austin Proctor Kathryn Stoklosa

An Analysis of the Effect of Income on Life Insurance Justin Bryan Austin Proctor Kathryn Stoklosa 1 Abstract This paper aims to analyze the relationship between the gross national income per capita and

### Regents Exam Questions A2.S.8: Correlation Coefficient

A2.S.8: Correlation Coefficient: Interpret within the linear regression model the value of the correlation coefficient as a measure of the strength of the relationship 1 Which statement regarding correlation

### Problems with OLS Considering :

Problems with OLS Considering : we assume Y i X i u i E u i 0 E u i or var u i E u i u j 0orcov u i,u j 0 We have seen that we have to make very specific assumptions about u i in order to get OLS estimates

### Econometrics Regression Analysis with Time Series Data

Econometrics Regression Analysis with Time Series Data João Valle e Azevedo Faculdade de Economia Universidade Nova de Lisboa Spring Semester João Valle e Azevedo (FEUNL) Econometrics Lisbon, May 2011

### Is the person a permanent immigrant. A non permanent resident. Does the person identify as male. Person appearing Chinese

Cole Sprague Kai Addae Economics 312 Canadian Census Project Introduction This project is based off of the 2001 Canadian Census data, and examines the relationship between wages and education, while controlling

### In previous chapters, the dependent and independent variables in our multiple regression

C h a p t e r Seven Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables In previous chapters, the dependent and independent variables in our multiple regression models

### A Primer on Forecasting Business Performance

A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.

### Regression Analysis: Basic Concepts

The simple linear model Regression Analysis: Basic Concepts Allin Cottrell Represents the dependent variable, y i, as a linear function of one independent variable, x i, subject to a random disturbance

### 4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4

4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Non-linear functional forms Regression

### Multicollinearity in Regression Models

00 Jeeshim and KUCC65. (003-05-09) Multicollinearity.doc Introduction Multicollinearity in Regression Models Multicollinearity is a high degree of correlation (linear dependency) among several independent

### Several scatterplots are given with calculated correlations. Which is which? 4) 1) 2) 3) 4) a) , b) , c) 0.002, d) 0.

AP Statistics Review Chapters 7-8 Practice Problems Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Suppose you were to collect data for the

### . 58 58 60 62 64 66 68 70 72 74 76 78 Father s height (inches)

PEARSON S FATHER-SON DATA The following scatter diagram shows the heights of 1,0 fathers and their full-grown sons, in England, circa 1900 There is one dot for each father-son pair Heights of fathers and

### Credit Spending And Its Implications for Recent U.S. Economic Growth

Credit Spending And Its Implications for Recent U.S. Economic Growth Meghan Bishop, Mary Washington College Since the early 1990's, the United States has experienced the longest economic expansion in recorded

### Marginal Person. Average Person. (Average Return of College Goers) Return, Cost. (Average Return in the Population) (Marginal Return)

1 2 3 Marginal Person Average Person (Average Return of College Goers) Return, Cost (Average Return in the Population) 4 (Marginal Return) 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

### CHAPTER 7. Exercise Solutions

CHAPTER 7 Exercise Solutions 141 Chapter 7, Exercise Solutions, Principles of Econometrics, 3e 14 EXERCISE 7.1 (a) (b) When a GPA is increased by one unit, and other variables are held constant, average

### An Exploration into the Relationship of MLB Player Salary and Performance

1 Nicholas Dorsey Applied Econometrics 4/30/2015 An Exploration into the Relationship of MLB Player Salary and Performance Statement of the Problem: When considering professionals sports leagues, the common

### Chapter 9: Univariate Time Series Analysis

Chapter 9: Univariate Time Series Analysis In the last chapter we discussed models with only lags of explanatory variables. These can be misleading if: 1. The dependent variable Y t depends on lags of

### Multiple Linear Regression in Data Mining

Multiple Linear Regression in Data Mining Contents 2.1. A Review of Multiple Linear Regression 2.2. Illustration of the Regression Process 2.3. Subset Selection in Linear Regression 1 2 Chap. 2 Multiple

### Mgmt 469. Model Specification: Choosing the Right Variables for the Right Hand Side

Mgmt 469 Model Specification: Choosing the Right Variables for the Right Hand Side Even if you have only a handful of predictor variables to choose from, there are infinitely many ways to specify the right

### Lecture 18 Linear Regression

Lecture 18 Statistics Unit Andrew Nunekpeku / Charles Jackson Fall 2011 Outline 1 1 Situation - used to model quantitative dependent variable using linear function of quantitative predictor(s). Situation

### Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares

Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares Many economic models involve endogeneity: that is, a theoretical relationship does not fit

### Validity of Selection Criteria in Predicting MBA Success

Validity of Selection Criteria in Predicting MBA Success Terry C. Truitt Anderson University Abstract GMAT scores are found to be a robust predictor of MBA academic performance. Very little support is

### Using SPSS for Multiple Regression. UDP 520 Lab 7 Lin Lin December 4 th, 2007

Using SPSS for Multiple Regression UDP 520 Lab 7 Lin Lin December 4 th, 2007 Step 1 Define Research Question What factors are associated with BMI? Predict BMI. Step 2 Conceptualizing Problem (Theory) Individual

### Mgmt 469. Fixed Effects Models. Suppose you want to learn the effect of price on the demand for back massages. You

Mgmt 469 Fixed Effects Models Suppose you want to learn the effect of price on the demand for back massages. You have the following data from four Midwest locations: Table 1: A Single Cross-section of

### DEPARTMENT OF ECONOMICS. Unit ECON 12122 Introduction to Econometrics. Notes 4 2. R and F tests

DEPARTMENT OF ECONOMICS Unit ECON 11 Introduction to Econometrics Notes 4 R and F tests These notes provide a summary of the lectures. They are not a complete account of the unit material. You should also

### Paper No 19. FINALTERM EXAMINATION Fall 2009 MTH302- Business Mathematics & Statistics (Session - 2) Ref No: Time: 120 min Marks: 80

Paper No 19 FINALTERM EXAMINATION Fall 2009 MTH302- Business Mathematics & Statistics (Session - 2) Ref No: Time: 120 min Marks: 80 Question No: 1 ( Marks: 1 ) - Please choose one Scatterplots are used

### Simultaneous Equations Models. Sanjaya DeSilva

Simultaneous Equations Models Sanjaya DeSilva 1 Reduced Form and Structural Models We will begin with definitions; 1. Exogenous variables are variables that are determined outside of the model. For the

### Final Review Sheet. Mod 2: Distributions for Quantitative Data

Things to Remember from this Module: Final Review Sheet Mod : Distributions for Quantitative Data How to calculate and write sentences to explain the Mean, Median, Mode, IQR, Range, Standard Deviation,

### Experimental Designs leading to multiple regression analysis

Experimental Designs leading to multiple regression analysis 1. (Randomized) designed experiments. 2. Randomized block experiments. 3. Observational studies: probability based sample surveys 4. Observational

### Department of Economics, Session 2012/2013. EC352 Econometric Methods. Exercises from Week 03

Department of Economics, Session 01/013 University of Essex, Autumn Term Dr Gordon Kemp EC35 Econometric Methods Exercises from Week 03 1 Problem P3.11 The following equation describes the median housing

### Module 5: Multiple Regression Analysis

Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College

### The Simple Linear Regression Model: Specification and Estimation

Chapter 3 The Simple Linear Regression Model: Specification and Estimation 3.1 An Economic Model Suppose that we are interested in studying the relationship between household income and expenditure on

### REMITTANCE TRANSFERS TO ARMENIA: PRELIMINARY SURVEY DATA ANALYSIS

REMITTANCE TRANSFERS TO ARMENIA: PRELIMINARY SURVEY DATA ANALYSIS microreport# 117 SEPTEMBER 2008 This publication was produced for review by the United States Agency for International Development. It

### Wooldridge, Introductory Econometrics, 4th ed. Multiple regression analysis:

Wooldridge, Introductory Econometrics, 4th ed. Chapter 4: Inference Multiple regression analysis: We have discussed the conditions under which OLS estimators are unbiased, and derived the variances of

Wiswall, Labor Economics (Undergraduate), Fall 2005 1 QUIZ Instructions: Write all answers on the separate answer sheet. Make sure you write your name on this answer sheet. (68 points total) Multiple Choice

### LOGIT AND PROBIT ANALYSIS

LOGIT AND PROBIT ANALYSIS A.K. Vasisht I.A.S.R.I., Library Avenue, New Delhi 110 012 amitvasisht@iasri.res.in In dummy regression variable models, it is assumed implicitly that the dependent variable Y

### Homework 2 answers. Nathaniel Higgins

Homework 2 answers Nathaniel Higgins nhiggins@umd.edu, nhiggins@ers.usda.gov Assignment The assignment was to do the following book problems: 17.2 17.2, 17.6, 17.7 C17.1, C17.2, C17.3 Let grad be a dummy

### Statistics & Regression: Easier than SAS

ST003 Statistics & Regression: Easier than SAS Vincent Maffei, Anthem Blue Cross and Blue Shield, North Haven, CT Michael Davis, Bassett Consulting Services, Inc., North Haven, CT ABSTRACT In this paper

### 2. Simple Linear Regression

Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according

### 16 : Demand Forecasting

16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical

### Name: Date: Use the following to answer questions 2-3:

Name: Date: 1. A study is conducted on students taking a statistics class. Several variables are recorded in the survey. Identify each variable as categorical or quantitative. A) Type of car the student

### , has mean A) 0.3. B) the smaller of 0.8 and 0.5. C) 0.15. D) which cannot be determined without knowing the sample results.

BA 275 Review Problems - Week 9 (11/20/06-11/24/06) CD Lessons: 69, 70, 16-20 Textbook: pp. 520-528, 111-124, 133-141 An SRS of size 100 is taken from a population having proportion 0.8 of successes. An

### 5. Multiple regression

5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful

### Chapter 8. Linear Regression. Copyright 2012, 2008, 2005 Pearson Education, Inc.

Chapter 8 Linear Regression Copyright 2012, 2008, 2005 Pearson Education, Inc. Fat Versus Protein: An Example The following is a scatterplot of total fat versus protein for 30 items on the Burger King

### Model Building and Gains from Trade

Model Building and Gains from Trade Previously... Economics is the study of how people allocate their limited resources to satisfy their nearly unlimited wants. Scarcity refers to the limited nature of

### 1. The Classical Linear Regression Model: The Bivariate Case

Business School, Brunel University MSc. EC5501/5509 Modelling Financial Decisions and Markets/Introduction to Quantitative Methods Prof. Menelaos Karanasos (Room SS69, Tel. 018956584) Lecture Notes 3 1.

### Keynesian Theory of Consumption. Theoretical and Practical Aspects. Kirill Breido, Ilona V. Tregub

Keynesian Theory of Consumption. Theoretical and Practical Aspects Kirill Breido, Ilona V. Tregub The Finance University Under The Government Of The Russian Federation International Finance Faculty, Moscow,

### Door County 2015 Common Scholarship Application

1. APPLICANT S FULL NAME: Door County 2015 Common Scholarship Application The Door County Common Scholarship Application is designed to simplify the scholarship application process for Door County students.

### 1 Logit & Probit Models for Binary Response

ECON 370: Limited Dependent Variable 1 Limited Dependent Variable Econometric Methods, ECON 370 We had previously discussed the possibility of running regressions even when the dependent variable is dichotomous

### Simple Linear Regression Chapter 11

Simple Linear Regression Chapter 11 Rationale Frequently decision-making situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related

### Find the median temperature. A) 33 F B) 59 F C) 51 F D) 67 F Answer: B

Review for TEST 2 STA 2023 FALL 2013 Name Find the mean of the data summarized in the given frequency distribution. 1) A company had 80 employees whose salaries are summarized in the frequency distribution

### Can Annuity Purchase Intentions Be Influenced?

Can Annuity Purchase Intentions Be Influenced? Jodi DiCenzo, CFA, CPA Behavioral Research Associates, LLC Suzanne Shu, Ph.D. UCLA Anderson School of Management Liat Hadar, Ph.D. The Arison School of Business,

### The Effect of Minimum Wage and Unemployment across Varying Economic Climates. Scott Greer Isai Castrejon Sarah Lee

The Effect of Minimum Wage and Unemployment across Varying Economic Climates Scott Greer Isai Castrejon Sarah Lee The Effect of Minimum Wage and Unemployment across Varying Economic Climates Scott Greer

### Second Midterm Exam (MATH1070 Spring 2012)

Second Midterm Exam (MATH1070 Spring 2012) Instructions: This is a one hour exam. You can use a notecard. Calculators are allowed, but other electronics are prohibited. 1. [60pts] Multiple Choice Problems

### RELEVANT TO ACCA QUALIFICATION PAPER P3. Studying Paper P3? Performance objectives 7, 8 and 9 are relevant to this exam

RELEVANT TO ACCA QUALIFICATION PAPER P3 Studying Paper P3? Performance objectives 7, 8 and 9 are relevant to this exam Business forecasting and strategic planning Quantitative data has always been supplied

### Chapter 9 Assessing Studies Based on Multiple Regression

Chapter 9 Assessing Studies Based on Multiple Regression Solutions to Empirical Exercises 1. Age 0.439** (0.030) Age 2 Data from 2004 (1) (2) (3) (4) (5) (6) (7) (8) Dependent Variable AHE ln(ahe) ln(ahe)

### OLS is not only unbiased it is also the most precise (efficient) unbiased estimation technique - ie the estimator has the smallest variance

Lecture 5: Hypothesis Testing What we know now: OLS is not only unbiased it is also the most precise (efficient) unbiased estimation technique - ie the estimator has the smallest variance (if the Gauss-Markov

### The Life-Cycle Motive and Money Demand: Further Evidence. Abstract

The Life-Cycle Motive and Money Demand: Further Evidence Jan Tin Commerce Department Abstract This study takes a closer look at the relationship between money demand and the life-cycle motive using panel

### AMS7: WEEK 8. CLASS 1. Correlation Monday May 18th, 2015

AMS7: WEEK 8. CLASS 1 Correlation Monday May 18th, 2015 Type of Data and objectives of the analysis Paired sample data (Bivariate data) Determine whether there is an association between two variables This

### SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question.

Practice for Chapter 9 and 10 The acutal exam differs. SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Find the number of successes x suggested by the