Spring 2024
Brightspace Access of
Course Material Online (for students enrolled in the course)
INSTRUCTOR:
Dr. Walid H. Shayya |
Instructor's Contact Information
|
GENERAL COURSE DESCRIPTION:
AGSC 137 involves the application of procedures and techniques for collecting, analyzing, and interpreting agricultural data. The course introduces statistical methods using examples and applications to which students can easily relate. The course also focuses on teaching students fundamental statistical analysis using the MS Excel spreadsheet program and other pertinent computer tools. Students enrolled in AGSC 137 will be introduced to these important concepts through lectures and exercises they may complete on their laptops. Emphasis will be placed on providing the student with problem-solving skills and the ability to interpret the results of basic agricultural statistical analysis. Following a brief introduction to statistics and statistical inference, students enrolled in AGSC 137 are introduced to common central tendency and dispersion measures used in summarizing agricultural data. They are then introduced to the main concepts of probability and probability distributions and their potential applications in agricultural sciences. Additional topics to be covered include random sampling, confidence intervals, hypothesis testing, correlation analysis, and linear regression (simple and multiple).
Course Format: Hybrid (Asynchronous Online and Face-to-Face)
Meeting Times/Locations: Lectures (recorded by 5:00 PM on Saturdays), Face-to-Face (Wednesdays @ 9:30-10:45 AM in Marshall 102)
Semester Start Date: January 22, 2024
Semester End Date: May 3, 2024Prerequisite: MAGN 101 or equivalent
3 credits* (3 lecture hours), spring semester* Credits do NOT count if a student successfully completes MATH 123. This course meets the SUNY General Education Requirement for Mathematics (and Quantitative Reasoning).
EXPECTED COURSE OUTCOMES:
At the successful completion of AGSC 137, the student is expected to have developed the skill to:
Analyze and interpret problems while utilizing statistical equations, tables, and graphs.
Represent statistical information numerically, symbolically, visually, and verbally.
Utilize quantitative methods to solve problems involving descriptive and inferential statistics.
Understand the breadth of statistical techniques that can be applied to analyze agricultural problems.
Apply spreadsheet programs in statistical analysis.
STUDENT HOURS:
The instructor has the following designated student hours per week during the spring semester:
Mondays: 11:00 to 11:50 AM
Tuesdays: 11:00 to 11:50 AM
Wednesdays: 11:00 to 11:50 AM
Thursdays: 11:00 to 11:50 AM
Fridays: 8:00 to 8:50 AM
If necessary, students are also encouraged to make appointments to meet the instructor at other times.
CONTACT HOURS AND CLASS SCHEDULE:
AGSC 137 is a three-credit course. It includes three contact hours per week for 14 weeks. One section of the course is offered during the 2024 Spring semester. The schedule of the offered section is as follows:
- AGSC 137 - Section 1: The two 75-minute weekly lectures are recorded and made available by 5:00 PM on Saturdays, while the face-to-face class meeting is scheduled on Wednesdays from 9:30 up to 10:45 AM in Marshall 102.
TEXTBOOK(S):
Caldwell, S. 2013. Statistics Unplugged. Wadsworth, Belmont, California (ISBN: 978-0-8400-2943-0).
Shayya, W.H. 2024. AGSC 137: Agricultural Statistics Class Manual (5th Edition). XanEdu Publishing Inc. (ISBN: 979-8-82277-700-2).
Shayya, W.H. 2020. Using MS Excel 2019 to Analyze Data: An Introductory Tutorial (online tutorial).
Shayya, W.H. 2014. Using MS Excel 2013 to Analyze Data: An Introductory Tutorial (online tutorial).
Shayya, W.H. 2011. Using MS Excel 2010 to Analyze Data: An Introductory Tutorial (online tutorial).
Shayya, W.H. 2008. Using MS Excel 2007 to Analyze Data: An Introductory Tutorial (online tutorial).
Shayya, W.H. 2003. Using MS Excel 2003 to Analyze Data: An Introductory Tutorial (online tutorial).
CLASS POLICIES:
Class Material and Reminders: The lecture topics in AGSC 137 will follow the course outline provided on this page (under Brightspace) and online at people.morrisville.edu/~shayyaw/Agsc137/AGSC137.htm (the course outline along with course expectations will be covered during the first class recording). Course material is covered in two weekly recorded lectures made available online (lectures will be recorded and made available asynchronously on Brightspace). You should expect the class recordings to be made available by 5:00 PM on Saturdays starting from January 20, 2024. Weekly reminders for the class will be prepared on Saturdays and made available on the "Announcements" page, which may be accessed through the course "Home Page" in Brightspace. Additionally, a copy of the weekly reminders will be forwarded to the campus email address of every student currently enrolled in the class.
Attendance: Each student enrolled in AGSC 137 must log on to Brightspace weekly to review the week's course material and attend to any class assignments. Quizzes and homework assignments must be completed in most weeks of the semester. Attendance will be taken based on the student completing the weekly online quiz (if assigned), submitting the weekly assignment(s), and attending the in-person class meetings. If the student fails to take a quiz and submit an assignment when due, the student will be marked absent for that week. Appropriate action will be taken when a student misses 20% or more of the course during the semester. Moreover, a student with a few or no class absences during the semester will receive favorable consideration during the grading process when the student is close to receiving the next higher letter grade. Class examinations will be held online, as announced at the start of the semester. There will be no make-up examination without a written medical excuse, family emergency, or prior permission from the instructor. Students are responsible for all materials covered, whether assigned or presented online during the recorded lectures.
Assignments: This course will include twelve homework assignments of equal weight, accounting for 20% of your final grade in the course. Class assignments must be accessed and submitted through the provided links from the “Homework Submissions” page, accessible from the "Class Assignments" page under Brightspace. Each class assignment must be completed in MS Word (or MS Excel) and then submitted using the provided assignment link under Brightspace. Assignments must be completed individually and submitted before the provided deadline (although there is usually a grace period of one day for submitting a homework assignment). Please remember that the course assignments serve a specific educational purpose by allowing students to learn and apply the covered concepts and engage in problem-solving in a statistical setting. As such, students must complete those assignments accurately, neatly, and on time. Copying another student's assignment (including retyping their answers) or allowing someone else to copy your written assignment is considered cheating. It will result in the student receiving a zero on the assignment. Late assignments may not be submitted for a grade once an assignment is corrected and returned to the class. A student missing an assignment will receive a grade of zero on that assignment. At the end of the semester, however, each student's assignment with the lowest grade will be dropped.
Examinations/Quizzes: There will be two class examinations and a comprehensive final in AGSC 137. Class examinations must be completed individually online on Brightspace. These cover the material presented during the recorded online lectures, homework assignments, and assigned readings. Starting from week#2, please note that there will be a weekly online quiz (except for the weeks when an exam is scheduled) on the material covered in the recorded weekly lectures. When present, the weekly quiz must be completed between 5:00 PM and midnight on Thursdays. A student missing a weekly quiz will receive a grade of zero. At the end of the semester, however, each student's quiz with the lowest grade will be dropped.
Honesty Policy and Discipline (Due Process): Honesty and integrity are significant elements in professional behavior and are expected of each student. Any assignment (including those in electronic media) submitted by a student must be of the student's original authorship. Representation of another's work as the student’s own shall constitute plagiarism. In any form, cheating is unacceptable in all college courses. Students having academic problems should consult their academic advisor or a college counselor. Cheating will be dealt with according to SUNY Morrisville policy. The standards of academic honesty and due process procedures for SUNY Morrisville are in the Rules, Regulations, and Expectations section of the Student Handbook.
Things to remember: The material covered in AGSC 137 will require a consistent effort from each student (understanding earlier lectures will be crucial to grasping concepts presented in subsequent lectures). Each student should plan to spend at least two hours per week for every lecture convened in class. Students are also urged to solve problems and complete the required homework assignments on time (and independently). Solving additional problems from the text and revisiting the problems solved in class will be very helpful. Completing assignments well before the due date will allow the student to ask questions should he/she encounter problems. Also, students should remember to ask questions of the instructor when they face difficulties with course material, and the instructor welcomes the opportunity to visit with students whenever needed.
GRADING/EVALUATION OF THE STUDENT:
Evaluation is a shared responsibility between the teacher and the student. The evaluation aims to demonstrate how well the professor has taught and the student has learned specific course materials, the principles, concepts, and terms relevant to the covered topics. Evaluation is also intended to assess the student's ability to utilize the acquired knowledge in problem-solving.
The breakdown of grading in this course will be as follows:
Class Work Ethic and Participation ==> 5% of the final grade
Homework Assignments ==> 20% of the final grade
Weekly Quizzes ==> 10% of the final grade
First Examination (during the second lecture of the 6th week) ==> 20% of the final grade
Second Examination (during the two lectures of the 12th week) ==> 20% of the final grade
Final Examination (comprehensive) ==> 25% of the final grade
The distribution of grades in this course will be based on the A-F College grading scheme. The letter grades correspond to the following percentage scale: A (90-100%), A- (87-89.9%), B+ (83-86.9%), B (80-82.9%), B- (77-79.9%), C+ (73-76.9%), C (70-72.9%), C- (67-69.9%), D+ (63-66.9%), D (60-62.9%), and F (<60%).
STARFISH EARLY ALERT SYSTEM:
This course participates in the Starfish Early Alert System, an early intervention system designed to enable academic success, student persistence, and graduation. When an instructor observes student behaviors or concerns that may impede academic success, the instructor may raise an alert flag that notifies the student of the matter, requests an individual contact to discuss the issue, and (in most cases) refer the student to the academic advisor. If you receive an email notification of an early alert, you must contact the instructor as soon as possible to discuss the issue. The purpose of the contact is to determine the severity of the issue, accurately assess its potential impact on your academic success, and plan actions to prevent negative consequences and enable academic success. For more information about the Early Alert system, contact your academic advisor.
OUTLINE OF TOPICS:
Lecture |
Date Recording Available |
Lecture Topic* |
Textbook/ On-line Resources |
Homework Assignment |
1 (1) | Jan. 20 | - Introduction to AGSC137 | ||
2 (1) | Jan. 20 |
-
Introduction to agricultural statistics - Variables and summation |
Chapter 1 | #1, Summations |
3 (2) | Jan. 27 | - Different forms of presenting data | Chapter 2 | |
4 (2) | Jan. 27 |
- Measuring
central tendency of ungrouped data - Measures of dispersion of ungrouped data |
Chapter 2 | #2, Summarizing Data |
5 (3) | Feb. 3 |
- Data
properties of importance - Review of descriptive statistics for ungrouped data |
Chapter 2 | |
6 (3) | Feb. 3 | - Introduction to MS Excel | Handout | #3, Excel Online Intro. Tutorial |
7 (4) | Feb. 10 |
- Hands-on
exercise on descriptive statistics for an ungrouped data set using MS
Excel - Introduction of an exercise on descriptive statistics for a second ungrouped data set |
Online Handout on Common Excel Functions | |
8 (4) | Feb. 10 | - Hands-on exercise on summarizing the second ungrouped data set using MS Excel | Handouts | #4, Summarizing Data Using MS Excel |
9 (5) | Feb. 17 | - Measuring central tendency of grouped data using MS Excel | Handouts | |
10 (5) | Feb. 17 | - Measures of dispersion of grouped data using MS Excel | Handouts | #5, Summarizing Grouped Data Using MS Excel |
11 (6) | Feb. 24 |
- Finalize
discussion on summarizing data - First exam study guide |
Handouts |
|
12 (6) | Feb. 28 |
Progress Examination 1 |
||
13 (7) | March 2 |
- First exam
review - Data sets and set operations |
Handouts |
|
14 (7) | March 2 |
-
Probability - Rules of probability |
Sample Problems |
#6, Probability |
Week 8: Spring Break (No classes) | ||||
15 (9) | March 16 | - Probability distributions |
Chapters 3
& 4 Sample Problems |
|
16 (9) | March 16 | - Probability distribution functions in MS Excel | Handouts | #7, Probability Distributions |
17 (10) | March 23 |
- Sampling
and sampling distributions - The Central Limit Theorem |
Chapter 5 | #8, Sampling Distributions |
18 (10) | March 23 | - Confidence interval for the mean | Chapter 6 | #9, Confidence Interval |
19 (11) | March 30 |
- Confidence
interval for standard deviation - Confidence interval for proportions |
Chapter 6 | |
20 (11) | March 30 |
- Hypothesis
testing - The case of null hypothesis |
Chapter 7 | #10, Hypothesis Testing of Single Mean |
21 (12) | April 6 |
- Single
sample hypothesis testing - Second exam study guide |
Chapter 7 | |
22 (12) | April 10 |
Progress Examination 2 |
||
23 (13) | April 13 | - Hypothesis testing of two sample means | Chapter 8 | |
24 (13) | April 13 |
- Problems
on Hypothesis Testing of two samples - Analysis of variance and beyond |
Chapter 8 Chapters 9-11 |
#11, Hypothesis Testing of Two Sample Means |
25 (14) | April 20 |
-
Correlation analysis - Simple linear regression |
Chapter 12 | |
26 (14) | April 20 | - Using MS Excel to solve correlation and linear regression problems | Online Handout on Correlation and Regression Analysis Using Excel | |
27 (15) | April 27 |
- Multiple
linear regression - Using MS Excel to solve multiple linear regression problems |
Handouts | #12, Correlation Analysis and Linear Regression |
28 (15) | April 27 |
- Finalize
discussion of linear regression - Final exam study guide |
Handouts | |
(16) | May ? |
Final
Examination
(comprehensive) - To Be Scheduled During the Finals Week |
*The topics
listed in the table above are tentative and may be subject to change during the
semester.
COLLEGE-WIDE POLICIES: To view the College-wide policies page, please click on this
link.