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STATS Quick Question About the Homework


I believe on problem 3 of the homework section “Two Sample Tests and Confidence Intervals”, there is a mistake with the answer key. I followed the same method I followed for questions 1 and 4 of the homework, but the answer does not work in this case. I have attached a picture of my homework assignment. The problem is the rejection region, it does not appear to work.

Also, is there any chance I can get some help determining the degrees of freedom? I was unable to figure it out.


Math colloquium tomorrow during the open key

Structural Equation Modeling



Advising Professor

Xiaohui Zhong

Department of

Mathematics, University of Detroit Mercy

The objective of the essay is to study the Structural Equation Modeling (SEM) and apply it to real life example. Structural equation modeling is a statistical technique that combines multivariate modeling methods, such as regression analysis, factor analysis and simultaneous equation modeling. The procedure in SEM includes data treatment, factor analysis, model building with latent variables, and interpretation of the model. The first step is Data treatment, where we discuss how to treat undesired data like outliners and missing data using methods such as regression substitution. In factor analysis, exploratory factor analysis (EFA) helps determine the amount of common factors while confirmatory factor analysis (CFA) interpret how well the hypothetical model is. After the model is built, correlation, multivariate and other characteristics between factors and variables can be studied.

During the

winter semester, University of Detroit Mercy conducted a pilot survey on public opinion regarding public transit in Southeast Michigan. The goal of the survey is to understand the public’s perspective on current conditions and future directions for the public transit system of the Southeast Michigan Region. In this essay, SEM was applied to the data acquired from the survey. As a result, models were built to establish the relations between three dependent variables and the four factors extracted. The three dependent variables are the willingness he publics are going to contribute to improve the public transit system, the amount of such contribution, and degree of trust of the publics to the government agencies in handling the money. The four factors extracted are the usage of public transit, the satisfaction of public transit, the importance of public transit, and the influence of public transit on the community as large. Using the statistical software SPSS and AMOS, the following models are built:

With the

models, we are able to study the different relations between the factors and variables.


Faculty/Course Evaluations for McNichols Courses – Advance Notice – Starting 12/2/13

The faculty/course evaluation process is important in providing feedback for continuous improvement.


course evaluation site is


of Engineering & Science, School of Business and ALL other programs Faculty/Course evaluations for the standard 14 week course sections may be completed during “dead” week between Monday, December 2, 2013 and Sunday, December 8, 2013.


may access the system from any internet connected computer whether on or off campus. For those students who prefer to use a University computer, systems are available in the ITS labs across the McNichols campus. All evaluations are completely anonymous.


you are unable to complete your online evaluation(s) due to technical reasons, please contact the Helpdesk with details ASAP. All evals must be in the system by the published end-date. If contacting the Helpdesk afterhours, please leave a voice-mail at 313.993.1500 or e-mail at helpdesk or submit a ticket into the system and someone will follow up with you the next business day.


Colloquium this Friday 11/22

Friday Engineering &Science Symposium

Control from the Continuous

to the Discrete”

Dr. Rick Hill, UDM Dept. of Mechanical Engineering

Friday, November 22nd, in Engineering 220

The complexity of modern engineered systems and the amount of autonomy they have is astounding, yet the design of such systems still relies heavily on heuristic and ad hoc techniques. Verification of such systems is thus achieved through extensive system-level simulation and testing. These techniques are very time consuming and susceptible to error.

A common approach for dealing with the complexity of modern engineered systems is to model the systems and their control at a high level of abstraction by using discrete-event models. In recent years, a formal framework has been developed that not only verifies the correctness of discrete control logic, but is also able to automatically synthesize this logic so that it is correct by construction.

These tools are useful to a wide range of industrial problems ranging from routing parts through a factory to tasking fleets of autonomous vehicles. However, these techniques result in overwhelming computational complexity when applied to systems with even a moderate amount of concurrency. Some techniques for addressing this complexity by employing modularity and hierarchy are discussed.


Reception beforehand