Course: MAT 150 – Statistics for Life Sciences
Prerequisites: LAC 022 or SAS 022 or UL4 mathematics placement
Textbook: OpenIntro Statistics, 4ed, D. Diez, M. Cetinkaya-Rundel, C. Barr, Creative Commons, freely available: OpenIntro Statistics
Supplementary Text: Introduction to Modern Statistics, M. Cetinkaya-Rundel, J. Hardin, Creative Commons, free web-based: Introduction to Modern Statistics
Name: Michael Arciero
Office: Decary 343
Email: marciero@une.edu
Office Hours: By appointment. Please let me know in advance via email that you plan to come.
In this course, students apply principles of research design and statistics to analyze and interpret data and draw conclusions about experimental situations relevant to the sciences. Topics include random sampling, graphic and numeric descriptive data analysis, the normal distribution, hypothesis testing, t-tests, analysis of variance, correlation, and regression. Students will use statistical software to examine data graphically and perform statistical analyses.
At the conclusion of this course, students will be able to:
Distinguish among different types of data.
Describe a variety of random sampling methods.
Summarize data verbally, numerically, symbolically, and/or graphically to effectively communicate the results of data analysis.
Perform inferential hypothesis testing, including t-tests, analysis of variance, correlation, and regression.
Recognize some sources of bias and limitations of statistical analysis and inferences.
Homework will be assigned weekly and completed on Brightspace.
There will be two exams.
Your grade in this course will be determined by the following criteria:
| Homework | 60% |
| Exams | 40% (20% each) |
| Grade | Points Grade | Point Average (GPA) |
| A | 93 – 100% | 4.00 |
| A- | 90 – 92.9% | 3.75 |
| B+ | 87 – 89.9% | 3.50 |
| B | 83 – 86.9% | 3.00 |
| B- | 80 – 82.9% | 2.75 |
| C+ | 77 – 79.9% | 2.50 |
| C | 73 – 76.9% | 2.00 |
| C- | 70 – 72.9% | 1.75 |
| D | 60 – 69.9% | 1.00 |
| F | 00 – 59.9% | 0.00 |
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The University of New England (UNE) values academic integrity in all aspects of the educational experience. Any behavior that provides a student with an advantage or provides another student with another advantage or disadvantage may be considered academic misconduct.
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Academic dishonesty includes, but is not limited to the following:
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Generative AI (GenAI) applications (like ChatGPT) have proven to be powerful and effective tools, and students are encouraged to become familiar with and use them. However, as with any tool, students must use GenAI in ways that support learning, not replace it. Learning to use AI responsibly and ethically is an important skill in today’s society.
In their courses, students are not allowed to use advanced automated tools, such as generative AI tools, on assignments unless explicitly directed to do so. Each student is expected to complete each assignment, including labs and quizzes as applicable, without substantive assistance from others, including automated tools.
Using AI-content generators to complete assignments without proper attribution violates academic integrity. By submitting assignments in UNE courses, you pledge to affirm that they are your own work and you attribute use of any and all tools and sources.
Unauthorized Use
Unauthorized use of AI is treated as a violation of academic integrity.
Citing AI Use
If permitted, students should indicate and cite any use of AI tools.
Instructor Responsibility
Instructors should clearly reiterate, using UNE AI Use Policy, how students can use AI tools in their courses, and communicate this policy to students at the beginning of the semester.
Student Responsibility
Students must follow the academic integrity policy of the University of New England.