IE256
HOMEWORK 5
1. An investigator is interested in the effects of stress on reaction time. She gives a reaction
time test to three groups of subjects: one group that is under a great deal of stress, one
group under a moderate amount of stress, and a thi
IE256
HOMEWORK 4
We are interested in the burning rate of a solid propellant used to power aircrew escape systems. Now
burning rate is a random variable that can be described by a probability distribution. Suppose that our
interest focuses on the mean bur
IE256.01
Yard.Do.Dr. Mjde Erol Genevois
REVISIONS
1.
A sample of 20 glass bottles of a particular type was selected and the internal pressure strength of each bottle
was determined. Consider the following partial sample information:
Median = 202.2; Q1 = 1
t Table
cum. prob
t .50
t .75
t .80
t .85
t .90
t .95
t .975
t .99
t .995
t .999
t .9995
one-tail
0.50
1.00
0.25
0.50
0.20
0.40
0.15
0.30
0.10
0.20
0.05
0.10
0.025
0.05
0.01
0.02
0.005
0.01
0.001
0.002
0.0005
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.00
Summarizing Quantitative Data
Tabular Methods:
Frequency Distribution
Relative Frequency and Percent Frequency Distributions
Cumulative Distributions
Graphical Methods:
Stem-and-Leaf
Dot Plot
Histogram
Ogive
Scatter
Example: Hudson Auto Repair
The manager
Numerical Measures
Measures of Central Location
Mean, Median, Mode
Measures of Variability
Range, Standard Deviation, Variance, Coefficient of Variation,
Coefficient of Correlation
Measures of Relative Standing
Percentiles, Quartiles
Box-Plots
Arithmetic
Confidence Interval
Population Mean
Interpreting the Meaning of Confidence Intervals
A confidence interval for a parameter is an interval of numbers
within which we expect the true value of the population
parameter to be contained. The endpoints of the i
Learning Objectives until Midterm 2
1. State What Is Estimated
2.
Explain Point Estimates
3. Explain Interval Estimates
4.
Compute Confidence Interval Estimates for Population Mean &
Proportion
5.
Compute Sample Size
6.
Solve Hypothesis Testing for Popula
IE256 ENGINEERING STATISTICS
MJDE EROL GENEVOIS
Statistics: Basic Notions
From Data to Decision
Probability vs Statistics Reasoning
In probability: starting with a population, you imagine taking many
samples and investigate how sample statistics were dist
One population, mean test, unkown
One
Population
Unknown
Proportion
Z Test
t Test
Z Test
(1 & 2
tail)
(1 & 2
tail)
(1 & 2
tail)
Known
Mean
1
Hypothesis Testing: The Critical Value Approach
1. Develop null and alternative hypothesis.
2. Select level of sig
Hypothesis Testing
Mean
Hypothesis Testing
2
Nonstatistical Hypothesis Testing
A criminal trial is an example of hypothesis testing without the
statistics.
In a trial a jury must decide between two hypotheses. The null
hypothesis is
H0: The defendant is i
Sampling
Real Image of the Population
1
Sampling
Population
Sample
A sample is a subset of a
larger population of objects
individuals, households,
businesses, organizations
and so forth.
Sampling enables researchers
to make estimates of some
unknown chara
IE256
HOMEWORK 1
1. What type of graph would you use to present the following? Explain your choice.
a. The number of female students in each grade in your school.
b. The annual number of road fatalities (the road toll) in your province or territory over t
IE256
HOMEWORK 1
1. What type of graph would you use to present the following? Explain your choice.
a. The number of female students in each grade in your school.
b. The annual number of road fatalities (the road toll) in your province or territory over t
IE256
HOMEWORK 2
The deflection temperature (in F) under load for 2 different types of plastic pipes is being
investigated and reported below.
Type 1 plastic pipe
182
193
184
202
219
225
225
194
214
202
219
227
223
209
194
201
228
209
201
216
212
209
218
2 Test of Independence
2 Test
Type of
Measurement
Differences between
two independent groups
Nominal
Chi-square test
Chi Square Independence Test: Examples
Determine whether high school graduation (Yes, No) is independent
of socioeconomic (Low, High) sta
IE256
HOMEWORK 3
The heigth of 2 different junior basket team is being investigated. 10 juniors from each team
are measured and the data are reported (in cm).
Team 1
183
197
178
205
189
209
186
199
193
203
Team 2
188
195
201
185
192
203
190
199
186
197
1.
F distribution critical value landmarks
Figure of F distribution (like in Moore, 2004, p. 656)
here.
Table entries are critical values for F *
with probably p in right tail of the
distribution.
p
2
49.50
199.5
799.5
4999
499725
3
53.59
215.7
864.2
5404
54