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Courses may be offered in one of the following modalities:

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  • Hybrid/blended courses (30–79 percent of coursework is delivered online.)
  • Online courses (100 percent of coursework is delivered online, either synchronously on a designated day and time or asynchronously as a deadline-driven course.)
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If you are enrolled in courses delivered in traditional or hybrid modalities, you will be expected to attend face-to-face instruction as scheduled.


Statistics For Natural Sciences (MTH-114)


Semester: Spring 2025
Number: 0144-114-001
Instructor: Neclisha Davis
Days: Tuesday Thursday 4:15 pm - 5:30 pm
Note: Traditional In-Person Class
Location: Garden City - Science Building 227
Credits: 3
Status: This Course is Filled to Capacity
Course Materials: View Text Books
Related Syllabi: Adam Wittenstein for Spring 2010*
Adam Wittenstein for Spring 2011*
Adam Wittenstein for Spring 2012*
Adam Wittenstein for Spring 2013*
Adam Wittenstein for Spring 2016*
Adam Wittenstein for Spring 2017*
Adam Wittenstein for Spring 2023*

*Attention Students: Please note that the syllabi available for your view on these pages are for example only. The instructors and requirements for each course are subject to change each semester. If you enroll in a particular course, your instructor and course outline may differ from what is presented here.

Description:

Develops tools for making decisions when faced with data. Learn techniques for analyzing and displaying data, and performing statistical tests, using illustrative examples drawn from the sciences. Make extensive use of statistical software in integrated labs and lectures as an aid to reason. (Learning Goals:Q;Distribution Reqs:Mathematics,Computing & Logic)

Learning Goals:   Students will learn to use professional statistical software which can analyze data. Students will be able to distinguish between when to perform different statistical reasoning tests. Students will be able to analyze a graphical representation of statistical data and draw appropriate conclusions. Students will learn to compute a z-score or a t-score, a confidence interval, and conduct a hypothesis test. Given the appropriate data, students will be able to calculate a numerical summary measure (i.e. mean, median, variance, range, IQR, etc.)

*The learning goals displayed here are those for one section of this course as offered in a recent semester, and are provided for the purpose of information only. The exact learning goals for each course section in a specific semester will be stated on the syllabus distributed at the start of the semester, and may differ in wording and emphasis from those shown here.

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