QMDS200 STATISTICS AND DATA ANALYSIS
Assignment # 3
Please put the course code, section number, course title, student name and student number on
the top left corner of the first page.
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Most of the optimization problems considered
to this point have had a single objective.
Two other modeling techniques:
Goal Programming (GP): no one specific objective
function but r
When one or more variables in an LP problem
must assume an integer value we have an
Integer Linear Programming (ILP) problem.
ILPs occur frequently
Integer variables als
2. Let X = no. of customers who purchased a particular coupon special
N = 2000
n = 50
X = 894.50
unknown population distribution of X
n = 50 > 30 Central Limit Theorem
Sampling distribution of is approximately normal.
1. An orange juice producer buys all his oranges from a large orange orchard. The
amount of juice squeezed from each of these oranges is assumed to be normally
distributed with a mean of 4.70 ounces and a standard deviation of 0.40 ounce.
Confidence Interval Estimation
There are two types of estimates for unknown population parameters:
1. Point estimate: consists of a single sample statistic value which is used to estimate the
true population parameter value.
2. Interval estimate
QMDS SATISTICS AND DATA ANALYSIS
Assignment # 3
Textbook, Problem 8.47, p.307
Let X = number of companies that informally monitored social networking sites to stay
on top of information related to their company
n = 500
p = 500 =
Fundamentals of Hypothesis Testing: One-Sample Tests
Hypothesis testing is a procedure based on sample evidence and probability theory to
determine whether the hypothesis is a reasonable statement and should not be rejected, or
is unreasonable a
1. A representative of a community group claims that the average income per
household in the area is $25,000. Suppose that for the type of area involved
household income can be assumed to be approximately normally distributed, and
the standard d
1. Let X = household income
1 = first community
2 = second community
H0: 1 2 = 0
H1: 1 2 0
Both population distributions are normal Sampling distribution of ( X 1 X 2 ) is normal.
n1 = 30 & n 2 = 40
X 1 = 35500 & X 2 = 34600
1. A developer is considering two alternative sites for a regional shopping center.
Since household income in the community is one important consideration in such
site selection, he wishes to perform the test that there is no difference between
Two-Sample Tests and One-Way ANOVA
In Topics 8 and 9, we learned how to obtain confidence intervals and perform hypothesis tests for one population parameter (, , or 2).
Frequently, however, inferential statistics is used to compare the paramet
1. Let X = income per household in the area
H0: = 25000
n = 15
Population distribution of X is normal.
Sampling distribution of is normal.
Test statistic: Z STAT =
Decision rule: Reject H0 if ZSTAT