State University of New York at Morrisville

Dr. Walid H. Shayya
School of Agriculture and Natural Resouces



Course Outline for AGSC 137

Agricultural Statistics

 

Spring 2019

Blackboard Access of Course Material On-line (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 encompasses an introduction to statistical methods using examples and applications to which students can easily relate.  The course also focuses on teaching students basic statistical analysis using the MS Excel spreadsheet program and other pertinent computer tools.  Students enrolled in AGSC 137, which is part of the laptop curriculum, will be introduced to these important concepts through lectures and exercises that 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 measures of central tendency and dispersion used in summarizing agricultural data followed by an introduction 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).

Prerequisite: MAGN 101 or equivalent
3 credits* (3 lecture hours), spring semester

* Credits do NOT count if a student successfully completes MATH 123.


EXPECTED COURSE OUTCOMES:

At the successful completion of AGSC 137, the student is expected to have developed the skill to:

  1. Analyze and evaluate problems that involve descriptive statistics, probability distributions, random sampling, confidence intervals, hypothesis testing, correlation analysis, and linear regression as related to agricultural science.

  2. Understand the types of problems in agriculture that can be analyzed statistically.

  3. Utilize statistical concepts to interpret agricultural data.

  4. Use MS Excel effectively to analyze data and solve problems.


OFFICE HOURS:

The instructor has the following designated office hours per week:

If necessary, students are also encouraged to make appointments to see the instructor at other times.


CONTACT HOURS AND CLASS SCHEDULE:

AGSC 137 is a three-credit hour course.  It includes three contact hours per week for 14 weeks.  One section of the course is offered during the 2019 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. 2014. Using MS Excel 2013 to Analyze Data: An Introductory Tutorial (on-line tutorial). 

Shayya, W.H. 2011. Using MS Excel 2010 to Analyze Data: An Introductory Tutorial (on-line tutorial). 

Shayya, W.H. 2008. Using MS Excel 2007 to Analyze Data: An Introductory Tutorial (on-line tutorial).

Shayya, W.H. 2003. Using MS Excel 2003 to Analyze Data: An Introductory Tutorial (on-line tutorial). 


STUDENTS WITH DISABILITY:

Any student who feels s/he may need an accommodation based on the impact of a disability should contact the Disability Services (DS) office immediately to register for services and receive a Notification of Disabilities form. Once you have this form, we will meet privately, to discuss your specific needs.   Although you may register for services at any time, please attempt to make arrangements within the first two weeks of the semester so all appropriate academic accommodations can be set. 


CLASS POLICIES:


GRADING/EVALUATION OF STUDENT:

Evaluation is a shared responsibility between the teacher and the student. The purpose of the evaluation is 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 utlize the acquired knowledge and how s/he can use this 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%).


OUTLINE OF TOPICS:

Lecture
(Week)

Date

Lecture Topic*

Textbook/On-line Resources Homework Assignment
1 (1) Jan. 21 - Introduction to AGSC137    
2 (1) Jan. 23 - Introduction to agricultural statistics
- Variables and summation
Chapter 1 #1, Problem Set on Summations
3 (2) Jan. 28 - Different forms of presenting data Chapter 2  
4 (2) Jan. 30 - Measuring central tendency of ungrouped data
- Measures of dispersion of ungrouped data
Chapter 2 #2, Problem Set on Summarizing Data 
5 (3) Feb. 4 - Data properties of importance
- Review of descriptive statistics for ungrouped data
Chapter 2  
6 (3) Feb. 6 - Introduction to MS Excel Handout #3, Excel On-line Introductory Tutorial
7 (4) Feb. 11 - 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
On-Line Handout on Common Excel Functions  
8 (4) Feb. 13 - Hands-on exercise on summarizing the second ungrouped data set using MS Excel Handouts #4, Problem Set on Summarizing Data Using MS Excel 
9 (5) Feb. 18 - Measuring central tendency of grouped data using MS Excel Handouts  
10 (5) Feb. 20 - Measures of dispersion of grouped data using MS Excel Handouts #5, Problem Set on Summarizing Grouped Data Using MS Excel 
11 (6) Feb. 25 - Finalize discussion on summarizing data
- First exam study guide

Handouts
 
12 (6) Feb. 27

Progress Examination 1

13 (7) March 4 - First exam review
- Data sets and set operations

Handouts
 
14 (7) March 6 - Probability
- Rules of probability

Sample Problems
#6, Problem Set on Probability
Week 8 - Spring Break (No classes)
15 (9) March 18 - Probability distributions Chapters 3 and 4
Sample Problems
 
16 (9) March 20 - Probability distribution functions in MS Excel Handouts #7, Problem Set on Probability Distributions
17 (10) March 25 - Sampling and sampling distributions
- The Central Limit Theorem
Chapter 5 #8, Problem Set on Sampling Distributions
18 (10) March 27 - Confidence interval for the mean Chapter 6 #9, Problem Set on Confidence Interval
19 (11) April 1 - Confidence intervals for proportions
- Confidence intervals problems
Chapter 6  
20 (11) April 3 - Hypothesis testing
- The case of null hypothesis
Chapter 7 #10, Problem Set on Hypothesis Testing of Single Mean
21 (12) April 8 - Single sample hypothesis testing
- Second exam study guide
Chapter 7  
22 (12) April 10

Progress Examination 2

23 (13) April 15 - Hypothesis testing of two sample means Chapter 8  
24 (13) April 17 - Problems on Hypothesis Testing of two samples
- Analysis of variance and beyond
Chapter 8
Chapters 9-11
#11, Problem Set on Hypothesis Testing of Two Sample Means
25 (14) April 22 - Correlation analysis
- Simple linear regression
Chapter 12  
26 (14) April 24 - Using MS Excel to solve correlation and linear regression problems On-line Handout on Correlation and Regression Analysis Using Excel
27 (15) April 29 - Multiple linear regression
- Using MS Excel to solve multiple linear regression problems
Handouts #12, Problem Set on Correlation Analysis and Linear Regression
28 (15) May 1 - 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.