(1) The sample variance
(a) indicates the center of a set of data.
(b) is roughly the average squared deviation from the mean.
(c) is a measure of the skewness of data.
(d) measures the extent to which data are normally distributed.
(2) Let S denote the n
A gas station with an attached car wash and convenience
store performed the following regression analysis to
understand what drives Sales (daily sales in dollars) at
its convenience store. It considers Volume (gallons of
gas pumped on a day), Washes (numb
(1) Which of the following is true?
(a) The sampling distribution of the mean is the distribution of the sample data.
(b) The product of a joint probability and a marginal probability yields a conditional
(c) When X and Y are independent rand
To understand the relationship between performance and experience, a company collected data on
performance (measured on a 0 to 100 scale) and years of experience for 150 employees. It then fit the two
regressions equations summarized below.
(1) In a simple random sample of 100 apartments in Philly, the average rent was found to
be $1150/month, with a standard deviation of $250. What is the approximate upper
limit of the 95% confidence interval for the average rent in all of Philly?
101. Introductory Business Statistics. (C) Staff.Prerequisite(s): MATH 104 or equivalent;
successful completion of STAT 101 is prerequisite to STAT 102.
Data summaries and descriptive statistics; introduction to a statistical comp
Readings. Finish reading Chapter 6 in W. These notes cover Sections 6.3 and 6.5 of the book.
In MHE read Section 5.2.
Sampling schemes other than random samples. To date most of our discussion has focused
Overview and readings. These notes address system-wide OLS estimation of a system of linear
equations. Applications in this framework are for the seemingly unrelated regressions (SUR)
model and the linear panel
Readings. Read Section 4.4 in W. It deals with the bias in OLS estimation when there is
measurement error. Measurement error can be in the response variable, and it can be in the
explanatory variables. Well look
Readings. Finish reading Chapter 5 of W. These notes address Sections 5.2.3, 5.2.4 and
5.2.5, and they present an example. In the next set of notes Ill present some final
comments about 2SLS estimation and turn t
Readings. Read Chapter 5 in W. These notes cover Sections 5.1, 5.2.1, 5.2.2, 5.2.4 and 5.2.5.
In the next set of notes Ill go back to cover Section 5.2.3, resume with 5.2.6, and present
examples. Chapter 5 deals
Readings. Read sections 4.1 and 4.2 in W (for now skip Section 4.2.4). These deal with
ordinary least squares (OLS) estimation of the single equation linear model. Model
assumptions are presented, and the asympto
Readings. Finish reading Chapter 5 of W, and read Chapter 6. These notes cover
Sections 5.2.6 and 5.3, and Section 6.1. In MHE read Section 4.6.4.
Difficulties posed by 2SLS estimation. There are two topics to d
Readings. Section 4.2.3 in W, which deals with calculation of standard errors of estimates when
there is heteroscedasticity. Ill calculate standard errors in Example 2 of Notes 3 assuming
heteroscedastic data. In
Readings. Read Section 12.8 in W, which deals with simulation and resampling. In
MHE bootstrapping is discussed on pages 300302. Also, read the remainder of Section
8.1 in MHE, which addresses the calculation of
Readings. Section 4.3 in W. The section deals with omitted explanatory variables in the context
of OLS estimation. Often the reason a variable is omitted from a regression calculation is that it
has not been or c
Readings. Read chapter 3 in W. The subject matter is asymptotic theory, and it is drawn from
probability theory and statistical theory. The important topics are big oh and little oh notation,
convergence in proba
2012 Fall STAT 111 Final Rev
University of Pennsylvania
this slide, I will review the important
materials after the midterm, including r
egression and hypothesis testing. For
each kind of problem, Ill illustrate by g