State University of New York at Morrisville

Dr. Walid H. Shayya
School of Agriculture, Business, and Technology



Course Outline of AGSC 137 

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, 2024

Prerequisite: 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:

  1. Analyze and interpret problems while utilizing statistical equations, tables, and graphs.

  2. Represent statistical information numerically, symbolically, visually, and verbally.

  3. Utilize quantitative methods to solve problems involving descriptive and inferential statistics.

  4. Understand the breadth of statistical techniques that can be applied to analyze agricultural problems.

  5. Apply spreadsheet programs in statistical analysis.


STUDENT HOURS:

The instructor has the following designated student hours per week during the spring semester:

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:


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:


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:

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
(Week)

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.