Lab 1: Introduction to JMP
Objectives
Familiarizing yourself with JMP
Analysis of data with one variable in JMP
Recitation/Labs
The weekly recitation/lab sections are integral to the ChE320 course to learn how to apply the
concepts that are introduced in
ChE 320 Homework 7:
Due Friday, October 21, 2016 by 11:30 am
Instructions: Please complete the following problems on separate, numbered pages. Start each problem on a new page
and write only on one side of a piece of paper. Only one side of a piece of pap
ChE 320 Homework 4:
Due Friday, September 23, 2016 by 11:30 am
Instructions: Please complete the following problems on separate, numbered pages. Start each problem on a new page
and write only on one side of a piece of paper. Only one side of a piece of p
ChE 320 Homework 5:
Due Friday, September 30, 2016 by 11:30 am
Instructions: Please complete the following problems on separate, numbered pages. Start each problem on a new page
and write only on one side of a piece of paper. Only one side of a piece of p
ChE 320 Homework 1:
Due Wednesday, Januarry 25, 2017 by 11:30 am at EE129
Instructions: Please complete the following problems on separate, numbered pages. Use the homework
cover page available in Blackboard. Start each problem on a new page and write onl
ChE 320_Spr_17_HW 1 Solution_Grading Scale
Total: 100 pts
(Please do not cut point more than once for the same mistake, e.g. If there are 3 parts in a question, answer was calculated wrong
in the 1st part. But the method was correct for the 2nd and 3rd pa
Data Summary and presentation
Population and sample
Representative picture. Obtained from Google Images
Population and sample
Representative picture. Obtained from Google Images
Sample Mean
Data: Tensile strength (psi) of randomly selected 8 O-Rings:
1037
1/26/17
1
Random Variables
A variable whose measured value can change
from one replicate of the experiment to another
is referred to as a random variable.
1/26/17
Conductivity of glass from (seemingly)
identical experiments.
Age of a randomly selected pe
ChE 320_Spr_17_HW 1 Solution
1-6. Open-ended question, your answers may be different. Representative answer is provided here:
Example of conceptual population: Conducting glass manufactured by the new method. (If used by industry, the population
Will exis
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TEST A
CHE 320
Statistical Modeling and Quality Enhancement
Midterm Examination 1 (TEST A)
Spring 2014
This is a closed book examination. Only a calculator is allowed. Please print your name on the
upper right corner of each page and on the last page wher
Lab 3: Continuous Distributions and
Discrete Distributions (Chapter 3)
Objectives
Explore the various continuous distribution types
Use JMP and Excel to solve problems with normal distributions
Solve problems regarding discrete distributions (binomial and
Problem 1: 6-12
Summary of Fit
RSquare
RSquare Adj
Root Mean Square Error
Mean of Response
Observations (or Sum Wgts)
0.748257
0.734271
1.317963
4.3
20
Analysis of Variance
Source
DF
Model
Error
C. Total
1
18
19
Sum of
Squares
92.93353
31.26647
124.20000
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CHE32000
Statistical Modeling and Quality Enhancement
Mid Term Exam 2 Spring Semester 2012
Time Allowed: 60 minutes
Total Marks: 50 marks
Tables of statistical data on Z, T and F distributions supplied.
Any calculators allowed.
Student may bring two sheet
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ChE 320 Spring 2017
Practice Exam 1 (100pts)
Instructions: Please read the questions carefully and plan accordingly before answering.
Name:_ _
Signature:_
Question 1. (20 pts)
A study was performed on wear of a bearing y and its relationship to x1 = oil v
ChE 320 Spring 2017
Practice Exam 1 (100pts)
Instructions: Please read the questions carefully and plan accordingly before answering.
Name:_ _
Signature:_
Question 1. (20 pts)
A study was performed on wear of a bearing y and its relationship to x1 = oil v
Example 1: Build Time of Computers
Suppose the time required to build a computer is nor
mally distributed with a mean of 50 minutes and a
standard deviation of 10 minutes.
What is the probability for the assembly time of a com
puter to be between 45 and 6
Under the above assumptions, let A be the rate at which
events occur, t be the length of a time interval, and X be
the total number of events in that time interval. Then, X
is called a Poisson random variable and the proba-
bility distribution of X is cal
Binomial Mean and Variance. . .
It can be Shown that
u=E(X)=np
and
For the previous example, we have
. E(X) = 10- 0.25 = 2.5.
. V(X) = 10- (0.25) - (1 0.25) = 1.875.
Poisson Distribution. . .
The Poisson distribution is another family of distributions
that arises in a great number of business situations. It
usually is applicable in situations where random events
occur at a certain rate over a period of time.
Consider
Canonical Framework. . .
Like the Binomial distribution, the Poisson distribution
arises when a set of canonical assumptions are reasonably
valid. These are:
a The number of events that occur in any time interval
is independent of the number of events in
Normal Distribution. . .
The normal distribution is the most important distrib
ution in statistics, since it arises naturally in numerous
applications. The key reason is that large sums of
(small) random variables often turn out to be normally
distributed
Mean and Variance
It can be shown that
and
Interpretation of (2)
The form of (2) seems mysterious. The best way to un-
derstand it is via the binomial distribution.
Consider a time interval and divide it into n equally-sized
subintervals. Suppose n is ver
Poisson Probability-Mass Function. . .
Let X be a Poisson random variable. Then, its probability
mass function is:
P(X = 33') = ai (2)
for$=0, 1,2,
The value of ,a is the parameter of the distribution. For
a given time interval of interest, in an applicat