Linear models including one- and two-way ANOVA with post-hoc multiple comparisons and transformations, simple and multiple linear regression including transformations and indicator variables, and logistic regression; and basic principles of sample and experimental design. The theoretical constructs of all topics will be developed and applied to real-life or realistic situations in the life and natural sciences. Prerequisite: MTH107. 4 Credits.

Learning Outcomes

At the end of this class you will be able to …

  1. explain why statistics is central to scientific inquiry (& your field of interest);
  2. perform, including assumption checks, and interpret the results from one-way and two-way analysis of variances;
  3. perform appropriate multiple comparisons following a significant analysis of variance result;
  4. perform, including assumption checks, and interpret the results from simple and indicator variable regressions;
  5. perform, including assumption checks, and interpret the results from simple logistic regression;
  6. use variable transformations and interaction terms where appropriate,
  7. compare and contrast the advantages and limitations of one-factor and two-factor experimental designs; and
  8. write a concise, detailed, accurate, and interesting paper explaining the design and interpreting the results of a statistical analysis.


Dr. Derek H. Ogle
Professor of Mathematical Sciences
& Natural Resources
CSE228; 682-1300
Feedback / Suggestions
Office Hours:
Mon, Wed, Fri 900-950; Tues 1200-1300
By Appointment


Elaine ("Lainey") Gallenberg
Office Hours:
Biometry: Thurs 10a-12n (CSE236)
Gen Math: Tues 1-3p; Wed 4-6p (DEX (Mead Hall))
I am a senior majoring in Natural Resources and Biology with a minor in math. On campus, I am a member of Environmental Council and Nordic Ski Club. I work for Northland Volunteer Program, Phonathon, and am a math tutor. I live in the Finer Things Apartment theme house. In addition to Biometry, I enjoy running, skiing, birding, and music.


An overall percentage score will be computed from the items and associated weights listed in the table below. To compute your current grade, enter your percentage scores in the boxes for assessments that have been completed. To project your future grade, also include percentages for future assessments. Put highest two quiz grades in “Q1” and “Q2” and lowest two quiz grades in “Q5” and “Q6”. [It is your responsibility to track your scores on the individual assessments.]

Q1 Q2 Q3 Q4 Q5 Q6 HW Portfolio
Weight 13%13%10%10% 7%7%10%30%
Overall Percentage: Projected Grade:

Lowest possible final letter grades will be assigned by comparing your overall percentage score (rounded to a whole number) to the values shown below:

A 92-100A- 90-91
B+ 87-89B 82-86B- 80-81
C+ 77-79C 70-76
D+ 67-69D 60-66F 0-59


A one-hour quiz will be given approximately one week after the completion of a module. Approximate dates for the quizzes are on the Dates page. Make-up quizzes will be provided only if you have a fixed commitment of sufficient importance that was set before the beginning of the semester or a verifiable medical condition. You will not be allowed to take a missed quiz without my prior approval. The final quiz will not be given early. Your two highest quiz grades will be “worth more” and your two lowest quiz grades will be “worth less” in your final grade (see grading table).


Several homework assignments will be assigned per module. PRINTED homework assignments are due at the beginning of the class period on the assigned due date (announced in class and on the Dates page). Assignments not handed in at the beginning of the class period will be considered late. Late assignments will not be accepted. The lowest approximately 20% of homework grades will be excluded when computing your overall grade.

Each homework assignment is worth 10 points and will be graded in two parts. The first part is an assessment of completeness and formatting (each assignment should follow the expectations outlined here) according to the following rubric.

0-1 points2-4 points5 points
Very few questions answered completely.Most questions answered completely. Format for homework may not have been followed.All questions answered completely. Format for homework followed exactly.

The second part, also worth 5 points, is an assessment of the quality of the work. In this part, only some (approximately half) of the questions will be assessed. Answer keys will be posted online so that you may check your work.

If there is any indication, whether I can confirm it or not, that you have not worked alone on your homework, then you will receive a 0 for that homework and will be warned of further penalties for subsequent offenses.


The portfolio project serves as the capstone experience for the course and allows you flexibility to demonstrate working knowledge of the major topics of the course. The portfolio will consist of a thorough analysis of data using four (cannot use any topic more than once) of the major topics in the course: (I) One-Way ANOVA (must have more than two groups and cannot be a subset of your Two-Way ANOVA), (II) Two-Way ANOVA, (III) Simple Linear Regression (cannot be a subset of your Indicator Variable Regression), (IV) Indicator Variable Regression, and (V) Logistic Regression. You can also propose an alternative topic for my approval.

For each topic you must gather and appropriately analyze data. The data can originate from your original research, another class (but cannot be exactly like an assignment for the other class), journal articles (you may have to recreate the data from a graphic or table – see me for help with this), reputable internet sources, or any other reliable source. The data should be real, should be able to be analyzed with one of the five topics from this class, and should be interesting (primarily to you, but also to me).

Finding appropriate data may be the hardest part of the portfolio. Thus, you must submit an initial proposal to me (see Dates page) that briefly describes the data that you will use (including your source for the data), the question to be asked or hypotheses to be tested with the data, the response and explanatory variables, the types (i.e., quantitative or categorical) of those variable(s), which topic you will use to answer the question, and why you will use that topic. The initial proposal is worth an all-or-nothing 10% of the overall grade for the portfolio. I strongly urge you to discuss your initial proposal with me before turning it in.

For the final portfolio paper, you will write a report for each data set/topic with the following sections:

  • Introduction: thoroughly describe background information needed to understand the data, why the data are important, why you are interested in the data, what the objectives of the study were (i.e., the research and statistical hypotheses), and what you expect to see and why.
  • Data Collection Methods: thoroughly describe the methodology you (or the authors) used to collect and you used to analyze the data. Specifically note your sample size, possibly by groups if appropriate.
  • Data Analysis Methods: describe which test you used to examine your hypothesis, discuss any follow-up tests (e.g., multiple comparisons), and declare your rejection level.
  • Assumptions: describe all assumption checking and, if necessary, data transformations (you must both demonstrate that a transformation is needed and that it accomplished meeting the assumptions).
  • Results: describe the results of your analysis.
    • For one-way ANOVA … provide an omnibus test, multiple comparisons results, specific interpretation of differences if they exist, and an appropriate plot that demonstrates your results. Back-transform results as appropriate.
    • For two-way ANOVA … provide an omnibus test or tests, multiple comparisons results, specif interpretation of differences if they exist, and an appropriate plot that demonstrates your results. Back-transform results as appropriate.
    • For simple linear regression … provide an omnibus test, specific interpretation of the slope, an appropriate prediction (with interpretation), and an appropriate plot that demonstrates your results. Back-transform results as appropriate.
    • For indicator variable regression … provide an omnibus test or tests, specific interpretation of differences in slopes and intercepts, specific interpretation of slopes and intercepts, predictions (with interpretations) that demonstrate your results, and an appropriate plot that demonstrates your results. Back-transform results as appropriate.
    • For logistic regression … provide an omnibus test, specific interpretation of the slope and back-transformed slope, predictions (with interpretations) of log(odds), odds, and probabilities, and an appropriate plot that demonstrates your results.
  • Discussion: describe how what you found relates to your objectives and hypotheses (and reasoning) from the introduction. Suggest where further research in this area should go from here.
  • Works cited: list of references (if any) used to support your project.

It is possible that all four analyses can be conducted from the same large data set. Thus, it is possible to have a single introduction and single data collection methods section, but separate data analysis methods, assumption checking, results, and discussion sections. Alternatively, if different data sets are used for each topic, then separate introduction, data collection methods, data analysis methods, assumption checking, results, and discussion sections will be required. The final portfolio should be typed, grammatically correct, and refer to supporting figures and tables. The final portfolio is due during the last week of classes (see Dates page).

Incomplete Grades

Under Northland College policy (scroll down to “Incomplete Grades”), an incomplete grade will be given ONLY under extreme circumstances beyond your control, such as a major illness. An incomplete grade will be given ONLY if you have successfully completed the entire course except for the final exam.

Classroom Conduct and Academic Integrity

Your conduct in class should revolve around the idea of being respectful of all others in the class. Adhering to the following behaviors should be considered the minimum requirements to being respectful of others:

  • Arrive to class on time. If you arrive late, then take the first available seat as quietly as possible. If you need to leave early, then please sit near the exit.
  • You are expected to attend the entire class period. Leaving class early defeats the purpose of the class period, does not take advantage of resources (i.e., the professor) that can significantly help your learning, and does not build statistical stamina.
  • Turn off and store out-of-sight cell phones and other electronic devices
  • Computer use is limited to class work.
  • If you choose to use your own computer, then please sit as far forward in class as possible.
  • Do not engage in side discussions while I or others are speaking to the entire class.
  • Do not sleep in class – this behavior is rude and distracting to others (especially the professor).
  • Unpack before and pack after class – opening and closing backpacks, rustling of papers or jackets is disruptive and disrespectful to others.
  • Do not use disrespectful language when addressing others.

Finally, note that the College’s Academic Integrity Statement & Policy will be followed in this course. Please make sure that you are familiar with its content.


It is the policy and practice of Northland College to create inclusive and accessible learning environments consistent with federal and state law. If you have already established accommodations with the Office of Accessibility Resources (OAR), please communicate your approved accommodations to me as soon as possible so that we can discuss your needs in this course. If you have not yet established services through OAR, but have a condition that requires accommodations (e.g., attention, learning, vision, hearing, mental, physical, or other health related concern), you should contact OAR (Ponzio 231 or 715-682-1387) or Donna Jones-Ilsley, Coordinator of Student Accessibility Resources.