The Simple Linear Regression Model
1.
The Model
y i 0 1 xi i
(1)
Equation (1) is known as the population regression function
y i is the dependent variable or endogenous variable
x i is the independent variable or exogenous variable
0 and 1 are the popula
Economics 4950
Problem Set Three
1.
1a.
The file calschool.xls contains data on average student test scores and the demographic characteristics of
students in 420 school districts located in California. A complete description of the data can be found in
t
DIRECTIONS: There are 40 questions. Read each question CAREFULLY, then choose
the best answer. Fill your answers on the bubble sheet with a pencil. Answer all
questions; there is no penalty for guessing.
Questions 13 use the following information
FACT: T
Estimators and Sampling Distributions
Overview
Over the last few lectures we have focused on how to describe the distribution of random
variables. For example, how do we characterize the distribution of wages, grades, stock prices or
housing values? Our f
Multivariate Regression Analysis
The Model
Multivariate regression analysis is perhaps the most commonly used tool in applied economic and
business analysis. It's wide spread use is due to the fact that it allows the researcher to impose the Ceteris
Parib
CHAPTER 1
TEACHING NOTES
You have substantial latitude about what to emphasize in Chapter 1. I find it useful to talk about
the economics of crime example (Example 1.1) and the wage example (Example 1.2) so that
students see, at the outset, that econometr
Economics 4950
Problem Set One
1.
Why are housing prices and incomes usually summarized by medians rather than means?
2.
Jenn owns a gourmet coffee house. Jenns total revenue varies from year to year due to the weather and
several economic factors. Based
Case study 1: Pelican Stores
Pelican Stores, a division of National Clothing, is a chain of womens apparel stores operating through
out the country. The chain recently ran a promotion in which discount coupons were sent to customers
of other National Clot
Statistical Inference:
Confidence Intervals and Hypothesis Testing
Overview
In the last section we discussed estimators and sampling distributions. In this section we discuss
how to make statements about how close the point estimate provided by a sample i
Economics 4950
Problem Set Two
1.
A college admissions officer for an M.B.A. program has determined that historically, applicants have
undergraduate grade point averages that are normally distributed with a population standard deviation of
0.45. From a ra
ECON4950 Problem Set 3
Georgia State University
Chapter 4: Inference in Multiple Regression Analysis
General Problems
Problem 4.4
Are rent rates inuenced by the student population in a college town? Let rent be the average monthly rent
paid on rental unit
ECON4950 Problem Set 1
Georgia State University
Questions on Background Material
1. A random sample of 22 businessa economists were asked to predict the
percentage growth in the consumer price index over the next year. The
forcasts were:
3.6, 3.1, 3.9, 3.
Homework #5 Solutions
6.4 The following model allows the return to education to depend upon the total amount
of both parents education, called pareduc:
log(wage) 0 1 educ 2 educ * pareduc 3 exp er 4 tenure u
(i)
Show that, in decimal form, the return to a
CHAPTER 4
TEACHING NOTES
At the start of this chapter is good time to remind students that a specific error distribution
played no role in the results of Chapter 3. That is because only the first two moments were
derived under the full set of GaussMarkov
CHAPTER 8
TEACHING NOTES
This is a good place to remind students that homoskedasticity played no role in showing that
OLS is unbiased for the parameters in the regression equation. In addition, you probably should
discuss how there is nothing wrong with t
CHAPTER 2
TEACHING NOTES
This is the chapter where I expect students to follow most, if not all, of the algebraic derivations.
In class I like to derive at least the unbiasedness of the OLS slope coefficient, and usually I
derive the variance. At a minimu
CHAPTER 7
TEACHING NOTES
This is a fairly standard chapter on using qualitative information in regression analysis, although
I try to emphasize examples with policy relevance (and only crosssectional applications are
included.).
In allowing for different
I, Introduction
Petroleum resources are the most important resource of the countries which
have natural gas and oil. They contribute to promoting economic growth as well as
raising the national income to the country. Currently, the oil consumption (84.3
m
Statistics for
Business and Economics
Chapter 7
Estimation: Single Population
Chapter Goals
After completing this chapter, you should be
able to:
Distinguish between a point estimate and a
confidence interval estimate
Construct and interpret a confidence
Statistics for
Business and Economics
Chapter 4
Discrete Random Variables and
Probability Distributions
Chap 41
Chapter Goals
After completing this chapter, you should be
able to:
Interpret the mean and standard deviation for a
discrete random variable
Statistics for
Business and Economics
Chapter 10
Hypothesis Testing: Additional Topics
Chapter Goals
After completing this chapter, you should be able to:
Test hypotheses for the difference between two population means
Two means, matched pairs
Independent
NATIONAL ECONOMICS UNIVERSITY
CENTER FOR ADVANCED
EDUCATIONAL PROGRAMS

SOCIALIST REPUBLIC OF VIETNAM
Independence Freedom Happiness

SYLLABUS
Course name:
1. MODULE PROFILE
Business Stsatistics
Course code:
IS310
Credit: 3
Term 2
Class: Advanced Progra
Statistics for
Business and Economics
Chapter 9
Hypothesis Testing
Chapter Goals
After completing this chapter, you should be
able to:
Formulate null and alternative hypotheses for
applications involving
a single population mean from a normal distribution
Regression with Panel Data
(SW Chapter 10)
Outline
1. Panel Data: What and Why
2. Panel Data with Two Time Periods
3. Fixed Effects Regression
4. Regression with Time Fixed Effects
5. Standard Errors for Fixed Effects Regression
6. Application to Drunk Dr
MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
1) When you have an omitted variable problem, the assumption that E(ui Xi) = 0 is violated. This
implies that
A) the OLS estimator is no longer consiste
Nonlinear Regression Functions
(SW Chapter 8)
Outline
1. Nonlinear regression functions general comments
2. Nonlinear functions of one variable
3. Nonlinear functions of two variables: interactions
4. Application to the California Test Score data set
SW C
Estimation of Dynamic Causal Effects
(SW Chapter 15)
Outline
1. Dynamic Causal Effects and the Orange Juice Data
2. Estimation of Dynamic Causal Effects with Exogenous
Regressors: The Distributed Lag Model
3. HAC Standard Errors
4. Application to Orange J
Introduction to Time Series Regression
and Forecasting
(SW Chapter 14)
Outline
1. Time Series Data: Whats Different?
2. Using Regression Models for Forecasting
3. Lags, Differences, Autocorrelation, & Stationarity
4. Autoregressions
5. The Autoregressive
Introduction to Time Series Regression
and Forecasting
(SW Chapter 14)
Outline
1. Time Series Data: Whats Different?
2. Using Regression Models for Forecasting
3. Lags, Differences, Autocorrelation, & Stationarity
4. Autoregressions
5. The Autoregressive