MTH5401: App Statistical Analysis, Summer 2024

COURSE SYLLABUS

Location:  Online

Course Days and Times:  Online

Prerequisites: MTH 2001

Instructor:  Prof. John Nierwinski

E-Mail Address:  jnierwinski@fit.edu

Phone Numbers:  (410) 278-4932

Academic Biography:

John Nierwinski is an Adjunct Professor at the Florida Institute of Technology (2009 to Present).  He is also Statistician and Inventor with the U.S. Army Combat Capabilities Development Command, Data & Analysis Center at Aberdeen Proving Ground (APG, MD).  Before working at APG, he was a Jr. Actuary & Mathematical Programmer, Statistician, and Vice President in the insurance and credit card industries.  He was issued a U.S. Patent on December 18, 2012 by the United States Patent & Trademark Office (USPTO) for an innovative methodology and process.  He has authored various kinds of professional journal articles, web-page articles, and numerous technical reports on various research topics and methodologies.  He is a member of the Florida Tech Chapter of the National Academy of Inventors and the National Academy of Inventors (inducted in 2017).

TEXTBOOK

The text used for this course is:   Montgomery, D., Runger, G., Applied Statistics and Probability for Engineers, John Wiley & Sons, Inc., 7th Ed., 2018.

You need to get the ebook (it has the problems and answers to odd problems), only ebook is available.

ISBN: 9781119400363 – enhanced e-book

OBJECTIVE

  1. To provide students a foundation in basic design of experiment, test of hypotheses, regression, and analysis of variance. 
  2. To enable students with the abilities to relate this course content to real life.

  3. To empower students with a general framework for lifelong learning through the Feedback Learning Loop described in the section below.
 

This syllabus is just to serve as a general “guideline” for the course and may be changed at the discretion of the instructor.

ASSIGNMENT,  FEEDBACK/LEARNING LOOP, AND SCHEDULE

 This 11 week schedule will keep us very busy.  Each week a reading assignment will be given along with all or some of the following learning tools: outside videos, instructor created videos, power point slides, self created documents, and homework assignments with solutions in back of textbook.  We will begin with two-sample statistical inference, and then move into statistical modeling via the general linear model, first in terms of regression and then experimental design.

 

This course will run from Monday to Monday for the purpose of assignments being given and their due dates.  For example, the first Monday marks the beginning of the course and the first homework (HW) assignment is due the following Monday. HW assignments are already in Canvas.  In between these two Mondays, we will experience a feedback/learning loop (FLL).  Here is what this FLL means:  The students will study the reading assignment along with the learning tools stated in the prior paragraph.  If there is any confusion after reviewing this material and double checking the resources then the student will email the professor with any question(s) he or she may have regarding the assignment.  The professor will examine questions and respond within 48 hours.  If the student is still confused then the student will email the professor again and the professor will respond accordingly (by a written response, hand drawn solution, or whatever it takes to clarify the confusion).  This FLL continues until the student feels satisfied.  It is the students responsibility to let the professor know when problems or concepts are confusing or misunderstood.

 

Course Schedule

(Online)

Outline of Subject Matter – all exercises are loaded into the assignment section of Canvas: 

Week \ Topic

Week 1 – Sections 10.1 – 10.2, 10.4 - 10.7; Inferences on the Difference of Two Means; Inferences on the Difference of Two Variances or Proportions

Week 2 – Sections 11.1 – 11.4; Simple Linear Regression

Week 3 – Sections 11.5 – 11.10; Hypothesis Test, Confidence & Prediction Intervals in Simple Linear Regression

Week 4 – Sections 12.1 – 12.2; Multiple Linear Regression

Week 5 – Sections 12.3 – 12.6; Confidence & Prediction Intervals in Multiple Linear Regression

Week 6 – Sections 13.1 – 13.2; Completely Randomized Single-Factor Experiment

Week 7 - Exam on Chapters 10 - 12, released by Monday, and due in Canvas the following Monday

Week 8 – Sections 13.3 – 13.4; Random Effects Model; Randomized Complete Block Design

Week 9 – Sections 14.1 – 14.5; Factorial Experiments of Several Factors

Week 10 - Sections 14.6 - 14.9; 2^k Factorial Designs, Blocking, and Confounding

Week 11 – Exam on Chapters 13 & 14, released early Week 10, and due in Canvas beginning of Week 11

HOMEWORK & EXAMS

HW problems will be assigned each week with solutions in the back of the ebook – this will enable the student to determine if he/she understands the concepts.  If the student is struggling with a particular problem or concept then he or she needs to email the professor so he can address the problem with the FLL.

Material on the exams will be similar to HW content.  Data sets for HW problems are available at the companion website for the textbook.  HW and exams are to be saved to a pdf file with the flow of work easy to follow and answer easy to see.  Feel free to utilize a statistical software to ease the computational load.  Include outputs of your software into the pdf file if it enhances your work flow.  Some recommended software packages include: Analysis pack in Excel, Palisade StatTools, MATLAB, Python, R, Minitab, SAS JMP, etc.  You are on your own to learn the various software packages.  Some are easier to learn than others.  Over the years, students have told me that Minitab is fairly easy to learn.

GRADING

Exams:   35% for each exam

Home assignments:   30%

Grading Scale:

90-100   A

80-89     B

70-79     C

60-69     D

 <60      F

A Concern:

I expect that you will fully comply with the Florida Tech Academic Honesty Policy (Links to an external site.)Links to an external site..  For example, it means that you may not look at, copy, or solicit "information", "hints", "pointers", etc. concerning tests from other students or anyone else. It means that your on-line tests are completed honestly and a host of other things that you should review in the Academic Honesty Policy (Links to an external site.)Links to an external site.. The policy is pretty straightforward and has exactly the kinds of things that you would expect an honorable person to have as their moral code.

Technical Requirements:

Chrome and Firefox are the two best browsers to use for this course.  

What is Title IX?:

Title IX of the Educational Amendments Act of 1972 is the federal law prohibiting discrimination based on sex under any education program and/or activity operated by an institution receiving and/or benefiting from federal financial assistance. Behaviors that can be considered “sexual discrimination” include sexual assault, sexual harassment, stalking, relationship abuse (dating violence and domestic violence), sexual misconduct, and gender discrimination. You are encouraged to report these behaviors. Reporting: Florida Tech can better support students in trouble if we know about what is happening. Reporting also helps us to identify patterns that might arise – for example, if more than one complainant reports having been assaulted or harassed by the same individual. Florida Tech is committed to providing a safe and positive learning experience. To report a violation of sexual misconduct or gender discrimination, please contact Security at 321-674-8111. *Please note that as your professor, I am required to report any incidences to Security or to the Title IX Coordinator (321-674-8700). Confidential support for students is available by contacting the Student Counseling Center at 321-674-8050.

Academic Accommodations: 

Florida Tech is committed to equal opportunity for persons w/disabilities in the participation of activities operated/sponsored by the university. Therefore, students w/documented disabilities are entitled to reasonable educational accommodations. The Office of Disability Services (ODS) supports students by assisting w/accommodations, providing recommended interventions, and engaging in case management services.  It is the student’s responsibility to make a request to ODS before any accommodations can be approved/implemented.  Also, students w/approved accommodations are encouraged to speak w/the course instructor to discuss any arrangements and/or concerns relating to their accommodations for the class.  Office of Disability Services (ODS):  Telephone: 321-674-8285 / Email: disabilityservices@fit.edu Website: www.fit.edu/disability

 

NOTICE TO COURSE PARTICIPANTS

 

This course may be recorded for use by students or faculty. Enrolled students are subject to having their images and voices recorded during the classroom presentations, remote access learning, and online course discussions.  Course participants should have no expectation of privacy regarding their participation in the class. Recordings may not be reproduced, shared with those not registered in the course, or uploaded to other online environments. All recordings will be deleted at the conclusion of the academic term.