Course Information

Course Title:	Applied Statistics in Agricultural Sciences
Course Number:	PLS 120
Lectures:	Teaching and Learning Complex 1010
Lab Sessions:	Session 1: Wednesdays — TLC 2212 Sessions 2, 3, 4: Thursdays & Fridays — TLC 2216
Lecture Records:	Access Recordings

Course Description

This course provides an introduction to basic statistical concepts and techniques. It aims to equip undergraduate students with the necessary skills to understand and analyze data, make informed decisions, and interpret statistical results. The course will cover fundamental topics such as descriptive statistics, probability, hypothesis testing, and inferential statistics.

Main Objectives

Acquire Fundamental Competencies: Students will gain essential skills in data collection, organization, analysis, visualization, and interpretation to form a strong foundation in statistical methods.
Develop Statistical Reasoning: The course aims to cultivate a comprehensive understanding of statistical thinking and reasoning, enabling students to critically evaluate and analyze data in various contexts.

Instructors

Mohsen B. Mesgaran

Instructor

276 Robbins Hall

mbmesgaran@ucdavis.edu

Office Hours: By appointment (send email to schedule)

Mohammadreza Narimani

Teaching Assistant - Lab Section A01

mnarimani@ucdavis.edu

Office Hours: Thursdays 10 AM - 12 PM (Zoom)

Join Zoom Office Hours

Course Schedule

Week	General Topic	Specific Topics
1	Introduction to Statistics	Overview of statistics and applications; Descriptive vs inferential statistics; Types of data; Levels of measurement; Data collection methods
2	Collecting, Organizing and Presenting Data	Frequency distributions and histograms; Measures of central tendency; Measures of variability; Data visualization
3	Probability	Understanding probability; Basic probability rules; Complementary events and conditional probability; Probability distributions
4	Probability Distributions	Discrete distributions (binomial, Poisson); Continuous distributions (normal); Z-scores; Applications
5	Sampling and Sampling Distributions	Sampling techniques; Sampling distribution of sample mean; Central Limit Theorem; Standard error and confidence intervals
6	Estimation	Point and interval estimation; Confidence intervals for mean and proportion; Sample size determination; Margin of error
7	Hypothesis Testing	Null and alternative hypotheses; Type I and II errors; One-sample tests; p-values and statistical significance
8	Hypothesis Testing (continued)	Two-sample tests; Paired t-tests; Chi-square test; Interpreting results
9	Analysis of Variance (ANOVA)	One-way ANOVA; F-test; Post hoc tests; Multiple comparisons; Experimental design basics
10	Correlation and Regression	Relationships between variables; Scatterplots and correlation; Simple linear regression; Interpreting coefficients

Course Grading

Evaluation Component	Points	Percentage
Mid-term Exam	100	22.2%
Weekly Assignments (10 assignments)	200 (20 each)	44.4%
Final Exam	150	33.3%
Total	450	100%

Important Information

📅 Key Dates

Mid-term Exam: October 31 (Friday), 5:10 PM
Final Exam: December 11 (Thursday), 1:00 PM
Thanksgiving Holiday: November 28 (Friday) - No lecture

📋 Course Policies

The course syllabus is flexible and may be adjusted to accommodate students' learning pace
Both mid-term and final exams will be online and open-book; however, AI tools are not permitted
Weekly assignments are to be completed individually (no group submissions)
Lab exercises will use R software (RStudio) - no prior programming knowledge required
R programming will be evaluated in lab assignments but not in exams
Final exam covers all material, with ~60% focused on post-midterm content
All course materials distributed through Canvas
In-class quizzes are ungraded and for engagement purposes
Bring computer, tablet, or smartphone to class for quizzes

Required Materials

📚 Textbook

A textbook is not required for this course. The provided course materials will be sufficient for completing assignments and successfully passing the exams.

💻 Software

R and RStudio: Free statistical software (installation help provided)
Binder: Online R environment (no installation required)

📖 Supplementary Textbooks (Optional)

Daniel, Wayne W., and Chad L. Cross. Biostatistics: a foundation for analysis in the health sciences. Wiley, 2018.
Zar, Jerrold H. Biostatistical analysis. Princeton-Hall, New Jersey (1999).
McClave, James T., and Terry T. Sincich. Statistics. Pearson Higher Ed, 2017.
Li, Jerome CR. Statistical inference. Vol. 1. Ann Arbor: Edwards Brothers, 1964.
Arora, P. N., and P. K. Malhan. Biostatistics. Himalaya Pub. House, 2010.

Course Syllabus