MATH 241 – Principles of Statistics

Latest Course Offering: Spring 2011, Term 4
Course Time: Tuesday and Thursday, 8:00 – 10:25
Location: Elings 205
Contact: tomschenkjr at gmail dot com

Instructional Resources

  1. Textbook: Introductory Statistics (Preliminary Edition): A Problem-Solving Approach by Stephen Kokoska, 1st Edition, Wadsworth Publishing: 2008 ISBN-13: 978-0716757627
  2. Calculator: TI-83 Plus, TI-83 Plus (Silver Edition), TI-84 Plus, or TI-84 Plus (Silver Edition)
  3. Data: Census @ Schools, Data and Story Library, Classroom Survey of Basic Data (SPSS Version), CensusAtSchools (website), Right to Work
  4. Blogs: See Statistical Modeling, Social Science Statistics, Observational Epidemiology
  5. Other materials will be distributed via the course website:


All assignments will be scored on a 100 percent scale. The final grade, Y, will be derived using the following formula:

Y = 0.25*E + 0.35 * F + 0.15 * A + 0.25 * T


F = Final
A = Assignments
P = Participation
T = Task/Project

Exam & Final: The exam will cover half of the course to ensure you are able to perform basic statistical calculations on your own in a timed environment. The final will explicitly be cumulative, although the emphasis will be slightly more on the last half of the class.

Assignments: Assignments will be given throughout the semester through the course website. Assignments will generally be short to ensure you are understanding the lectures.

Task/Project: Students will need to complete a semester project will cover statistics from sampling through analysis and interpretation.

Participation: Students are expected to regularly participate in classes and, if not in class, in out-of-class communication with the professor. Lack of participation and irregular attendance will be especially noticed if the student is struggling in class. The professor will warmly reward struggling students who seek help through email and questions.

Final Course Score

A = 90-100%
B = 80-89%
C = 70-79%
D = 60-69%
F = < 60%

Reading List

8 Mar: Looking at Data

Statistical Inference

***Chp. 1 – Kokoska
*Right-to-Work Law,
*Two major labor bills reappear at the Capitol, Jason Clayworth, Des Moines Register Blog, February 4, 2010.


***Chp. 2 – Kokoska
**Basics of a Basic Graph –

Summarizing Data

***Chp. 3 – Kokoska
**KhanAcademy lecture on means (and notation)
**Anatomy of a Standard Deviation
**KhanAcademy lecture on variance
**KhanAcademy lecture on standard deviation

22 Mar: Probability

***Chp. 4 – Kokoska

24 Mar: Random Variables

***Chp. 5 – Kokoska
***KhanAcademy lecture on Normal Distribution Curve

29 Mar: Correlation

***Chp. 6 – Kokoska

31 Mar: Continuous Probability Functions

***Chp. 6 – Kokoska
***KhanAcademy lecture on Normal Distribution Curve
**Mapping percentile rank to normal distribution, Wikipedia

7 Apr: Class will not meet

12 Apr: Class will not meet

14 Apr: Sampling

***Chp. 7 – Kokoska

19 Apr: Confidence Intervals

***Chp. 8 – Kokoska

21 Apr: Hypothesis Testing

***Chp. 9 – Kokoska

29 Apr: Confidence Intervals and Testing Based on Two Samples

***Chp. 10 – Kokoska

31 Apr: Correlation & Linear Regression

***Chp. 12 – Kokoska

5 May: Correlation & Linear Regression (con’t)

***Chp. 12 – Kokoska

7 May: In-class Final (open book)

Missed Exams and Assignments

Assignments will be due at the beginning of class every Tuesday and tests will be given on the days denoted below. Late assignments will be penalized 40 percent. Students must notify the professor of an upcoming absence. Students will be allowed to make up exams ONLY when the professor received prior notification for the inability to complete the exams. In extreme cases where prior notification is impossible, the student must provide written documentation—not by the student—explaining the absence. Students who miss a test for an unexcused absence will receive a zero.


Students will be expected to attend every class. Irregular attendance will be reflected in participation and company exercise scores. Those who already anticipate missing two or more classes are encouraged to enroll at another time.

Academic Integrity

Grand View University is dedicated to the development of the whole person and is committed to truth, excellence, and ethical values. Personal integrity and academic honesty in all aspects of the University experience are the responsibility of each faculty member, staff member, and student.

A student has an obligation to do work that is his or her own and reflects his or her learning and quest for academic knowledge. Dishonesty and cheating are not acceptable behaviors. Examples include helping others during exams, writing papers for others, falsifying data/records, copying other students’ work, taking work directly from the Internet or any printed source and claiming it as one’s own, and downloading/purchasing papers on-line. Students who cheat, could risk severe penalties, which may include failure of the assignment, failure of the course, or expulsion from the University.

“As a member of the Grand View University community, and in accordance with the mission of the University and its Lutheran identity, I agree to appreciate and respect the dignity and worth of each individual. I will honor and promote a community of open interaction, personal integrity, active and intellectual engagement, and academic honesty with students, faculty, and staff.”

Accelerated Courses

Grand View offers courses in accelerated or alternative delivery formats. They cover the same subject content and require the same or comparable assignments that are associated with a traditional fourteen week course.


Grand View University prohibits unlawful discrimination and encourages full participation by all students within the university community. When a student requires any instructional or other accommodation to optimize participation and/or performance in this course, it is the responsibility of the student to contact both the instructor and the Director of Academic Enrichment and Disability Coordinator and apply for any requested accommodation. The director is Dr. Kristine Owens and she can be reached at 515/263-2971.

Class Attendance

The Federal Government requires that students receiving financial aid attend classes. Students, who are identified by the instructor as not attending classes, will be reported to the Registrar’s Office. Students who fail to return to classes may lose all or a portion of their financial aid.

Classroom Conduct

Students should conduct themselves as responsible members of the University community respecting the rights of others. Any student behavior interfering with the professor’s ability to teach and/or the student’s ability to learn constitutes a violation of the Code of Student Conduct found in the Grand View Catalog. The professor may ask the student to leave the classroom and that student will be subject to disciplinary sanctions.

University E-Mail Account

It is essential that all students check their Grand View University e-mail account or set their account to forward to a preferred e-mail address.

Students may set-up an e-mail auto forward from the myView web site. Click on the “Manage and Update Personal Information” link and then select “set myView Mail Forwarding Address” under the “Links for You” section.

Appeal of Final Undergraduate Course Grade or Faculty Member’s Final Academic Disciplinary Action

Students who wish to appeal a final course grade or other academic disciplinary action of an instructor must complete at least section I.A. of the Academic Appeal Form on-line within fourteen calendar days after the published due date for the final grade submission of the academic term in which the issue of disagreement occurred. Visit site below to complete first part of the form. https://secure/

This form must be submitted electronically to the Office of the Provost. Nursing Students appealing a grade in a nursing course must follow the Nursing Division procedures.


  1. Homework #1 – Due 3/22
  2. Homework #2 – Due 3/31
  3. Homework #3 – Due 4/21


  1. Choose two variables from the school data set. Formulate an hypothesis between two variables and write a formal (e.g,. H_0) statement.
  2. Conduct a hypothesis test of the hypothesis test formulated above. Construct a 95% confidence interval for each variable.
  3. Conduct a linear regression:
  • Determine the dependent variable and independent variable.
  • Conduct a regression and copy/paste the results in a Word document.
  • How much variation is explained by the independent variable?
  • Is the slope of the coefficient statistically significant?
  • Interpret the slope in plain language.
  • Place the steps 1, 2, and 3 in a Word document.

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