Course Content

The course content, available here, is further enriched by in-class presentations and activities. Each week, we’ll engage with the material through:

  • Reading assignments introducing key concepts for the unit/topic.
  • Followed by lectures, in-class activities, investigations or lab sessions exploring the week’s content.
  • Submitting weekly assignments through Canvas.

Weekly Layout

Discover the Weekly Layout — a comprehensive guide to your week’s agenda. Access

lecture slides ,
videos ,
reading materials ,
investigations
weekly assignments ,
group assignments and
due dates ,

all conveniently organized for your academic journey.

Week 0 (Sep 18)

BEFORE CLASS DURING CLASS AFTER CLASS
  Nothing, just come to the class!   Covering the website & syllabus
  A Discussion on Life Sci. and Statistics

  The best stats you’ve ever seen
  Using data to better understand agriculture.
  Using statistics to treat chronic illnesses.

  NO SUBMISSION FOR TODAY!

Week 1 (Sep 22 - Sep 25)

BEFORE CLASS DURING CLASS AFTER CLASS

  CH 1: Introduction
  Section 1.2: Types of Evidence
  Section 2.1: Introduction to Variables

  FROM Samuels et al. (2016) 		  Lecture Slides - Introduction to Variables and Types of Evidence

  NO SUBMISSION FOR TODAY!

  CH 1: Introduction
  Section 1.2: Types of Evidence
  Section 2.1: Introduction to Variables

  FROM Samuels et al. (2016) 		  Investigation 1

  Investigation 1


DUE DATE: Thu, Sep 25

  CH 1: Introduction

  Section 1.3: Random Sampling

  FROM Samuels et al. (2016) 		   Lecture Slides - Sampling in Statistics
  Guided Activity

  Weekly Assignment

  Exercise 1.2.2
  Exercise 1.2.4
  Exercise 1.2.6
  Exercise 1.3.3 and
  Exercise 2.1.1

DUE DATE: Mon, Sep 29
  Introduction to R and RStudio – Lab 1
  Install R
  Install RStudio
  First Steps in R
   Lab Slides - Introduction to R & RStudio
  Upload the PDF file that we created today!

DUE DATE: Mon, Sep 29

Week 2 (Sep 29 - Oct 02)

BEFORE CLASS DURING CLASS AFTER CLASS

CH 2: Description of Samples and Populations

  Section 2.2: Frequency Distributions
  Section 2.3: Descriptive Statistics

  FROM Samuels et al. (2016)
   Lecture Slides - Description of Samples and Populations
  Rossman & Chance Applet

  Weekly Assignment

  Exercise 2.2.6
  Exercise 2.3.3
  Exercise 2.3.10

DUE DATE: Mon, Oct, 06

CH 2: Description of Samples and Populations

  Section 2.3: Descriptive Statistics
  Section 2.6: Measures of Dispersion

  FROM Samuels et al. (2016) 		  Investigation 2

  Investigation 2


DUE DATE: Thu, Oct 02
  Nothing, just come to the class!
  Lecture Slides - Introduction to Chance Models
  Guided Activity

  Review the class notes

CH 2: Description of Samples and Populations

  Section 2.3: Descriptive Statistics
  Section 2.6: Measures of Dispersion

  FROM Samuels et al. (2016) 		  Lab Slides - Descriptive Statistics - I

  Lab Assignment 1


DUE DATE: Mon, Oct, 06

Week 3 (Oct 6 - Oct 9)

BEFORE CLASS DURING CLASS AFTER CLASS

CHAPTER 3 and CHAPTER 4

  Section 3.1: Probability and the Life Sciences
  Section 3.2: Introduction to Probability
  Section 3.4: Density Curves
  Chapter 4 pp. 132- 137

  FROM Samuels et al. (2016)
   Lecture Slides - Introduction to Hypothesis Testing
  Rossman & Chance Applet

No Assignment for today!

CHAPTER 3 and CHAPTER 4

  Section 3.1: Probability and the Life Sciences
  Section 3.2: Introduction to Probability
  Section 3.4: Density Curves
  Chapter 4 pp. 132- 137

  FROM Samuels et al. (2016) 		  Investigation 3

  Investigation 3


DUE DATE: Thu, Oct 09

Nothing, just come to the class!

Print/Download the handout.
  Sampling Distribution Handout (Word)   Sampling Distribution Handout (PDF)

  Weekly Assignment

  Course Survey

DUE DATE: Mon, Oct 13

CH 2: Description of Samples and Populations

  Section 2.2: Frequency Distributions
  Section 2.3: Descriptive Statistics

  FROM Samuels et al. (2016)
   Lab Slides - Descriptive Statistics - II

  Lab Assignment 2


DUE DATE: Mon, Oct, 13

Week 4 (Oct 13 - Oct 16)

BEFORE CLASS DURING CLASS AFTER CLASS

Introduction to Confidence Intervals


Nothing, just come to the class!


  Lecture Slides - More on CLT and Introduction to Confidence Intervals


  Rossman & Chance Applet

  Weekly Assignment

  See Canvas

DUE DATE: Mon, Oct 20
  CH 4: The Normal Distribution
  Section 4.1: Introduction
  Section 4.2: The Normal Curves
  Section 4.3: Areas under a Normal Curve
  FROM Samuels et al. (2016) 		  Lecture Slides - More on CI and Normal Curves

   No assignment for today!!

CH 6: Confidence Intervals

  Section 6.1: Statistical Estimation
  Section 6.2: Standard Error of the Mean
  Section 6.3: Confidence Interval for µ
  Section 6.4: Planning a Study to Estimate µ

  FROM Samuels et al. (2016)
  Lecture Slides - Confidence Interval for a Single Mean 		    No Assignment for today!
  Midterm 1 (In-class)

Week 5 (Oct 20 - Oct 23)

BEFORE CLASS DURING CLASS AFTER CLASS
  Ch 6: Confidence Interval
  Section 6.3: CI for µ
  Section 6.4: Planning a Study to Estimate µ
  Section 6.5: Conditions for Validity of Estimation Methods
  FROM Samuels et al. (2016) 		  Lecture Slides - More on CI & Association and Confounding  

  Weekly Assignment

  Exercise 6.4.1

DUE DATE: Mon, Oct 27

Review your Hypothesis Testing for One Mean notes!

  Investigation 4 - Sleepless Night

  Investigation 4


DUE DATE: Thu, Oct 23
  Review of Week 3 & 4

  Lecture Slides - Review of Hypothesis Testing & CI  
  No Assignment for Today!
  Ch 6: Inference for Categorical Data
  Sec. 6.2 Difference of two proportions 

  FROM Diez et al. (2022)
  Lecture Slides - Difference of Two Proportions  
No assignment for today!

Week 6 (Oct 27 - Oct 30)

BEFORE CLASS DURING CLASS AFTER CLASS
  Ch 6: Inference for Categorical Data
  Sec. 6.2 Difference of two proportions 

  FROM Diez et al. (2022)

  Lecture Handout - Difference of Two Proportions (Google Doc)

  Lecture Handout - Difference of Two Proportions (PDF)

No Assignment for today!

Review these previous lecture slides

  Lecture Slides - Association and Confounding  

  Lecture Slides - Difference of Two Proportions   		  Investigation 5 - Do COVID vaccines work?

  Investigation 5


DUE DATE: Thu, Oct 30

CH 6: Confidence Intervals

  Section 6.6: Comparing two means
  Section 6.7: Confidence Interval for µ1 - µ2

  FROM Samuels et al. (2016)
  Lecture Slides - Comparing Two Means

  Weekly Assignment

  Course Survey

DUE DATE: Mon, Nov 03

  Ch 7: Inference for Numerical Data
  7.1.5 One sample t-tests 
  FROM Diez et al. (2022)


CH 6: Confidence Intervals

  Section 6.6: Comparing two means
  Section 6.7: Confidence Interval for µ1 - µ2

  FROM Samuels et al. (2016)
   Lab Slides - Applications of t-tests

  Lab Assignment 3


DUE DATE: Mon, Nov, 03

Week 7 (Nov 3 - Nov 6)

BEFORE CLASS DURING CLASS AFTER CLASS

  Ch 8: Comparison of Paired Samples
  Section 8.1: Introduction
  Section 8.2: The Paired-Sample t Test and Confidence Interval
  Section 8.3: The Paired Design
  FROM Samuels et al. (2016)
   Lecture Slides - The Paired Design  

  Weekly Assignment

Week 6 & 7 Review Assignment (CANVAS)

DUE DATE: Mon, Nov 10

  Ch 8: Comparison of Paired Samples
  Section 8.1: Introduction
  Section 8.2: The Paired-Sample t Test and Confidence Interval
  Section 8.3: The Paired Design
  FROM Samuels et al. (2016)
   Investigation 6 - Improving Running Speed

  Investigation 6


DUE DATE: Thu, Nov 06
  Comprehensive Review of Weeks 1-7
  Bring your midterm papers if you had (I’ll distribute them if you did not get yours) 		  Review midterm questions and beyond     No Assignment for today!

  Ch 8: Comparison of Paired Samples
  Section 8.1: Introduction
  Section 8.2: The Paired-Sample t Test and Confidence Interval
  Section 8.3: The Paired Design
  FROM Samuels et al. (2016) 		  Lab Slides - Paired Sample t-test  

  Lab Assignment 4


DUE DATE: Mon, Nov, 10

Week 8 (Nov 10 - Nov 13)

BEFORE CLASS DURING CLASS AFTER CLASS
 
No class meeting but Dr. Demirci will be available in her office from 9:10 AM to 12:50 PM for anyone who would like extra help.


 
Cal Poly Holiday (No class meeting)


  Ch 6: Inference for Categorical Data
  Sect. 6.3 Testing for goodness of fit using chi-square 
  Sect. 6.4 Testing for independence in two-way tables 
  FROM Diez et al. (2022)
   Lecture Slides - Chi Square Test  
No assignment for today!
  Ch 6: Inference for Categorical Data
  Sect. 6.3 Testing for goodness of fit using chi-square 
  Sect. 6.4 Testing for independence in two-way tables 
  FROM Diez et al. (2022)
   Investigation 7 - Comparing Multiple Proportions   Investigation 7

DUE DATE: Mon, Nov 17

Week 9 (Nov 17 - Nov 20)

BEFORE CLASS DURING CLASS AFTER CLASS
  Ch 11: Comparing the Means of Many Independent Samples
  Sec. 11.1 Introduction  
  Sec. 11.2 The Basic One-Way ANOVA  
  Sec. 11.4 The Global F Test  
  Sec. 11.5 Applicability of Methods  
  FROM Samuels et al. (2016)
  Lecture Slides - Introduction to One Way ANOVA  
  Week 9 Individual Assignment (on CANVAS)
DUE DATE: Sun, Nov 24
  Ch 11: Comparing the Means of Many Independent Samples
  Sec. 11.1 Introduction  
  Sec. 11.2 The Basic One-Way ANOVA  
  Sec. 11.4 The Global F Test  
  Sec. 11.5 Applicability of Methods  
  FROM Samuels et al. (2016)
  Investigation 8 - Comparing Multiple Means 

  Investigation 8

DUE DATE: Thu, Nov 20
  Ch 11: Comparing the Means of Many Independent Samples
  Sec. 11.6 One-Way Randomized Blocks Design  

  FROM Samuels et al. (2016)
  Lecture Slides - One Way Randomized Blocks Design  
No assignment!
  Ch 11: Comparing the Means of Many Independent Samples
  Sec. 11.1 Introduction  
  Sec. 11.2 The Basic One-Way ANOVA  
  Sec. 11.4 The Global F Test  
  Sec. 11.5 Applicability of Methods  
  FROM Samuels et al. (2016)
  Lab Dataset - Aldrin Concentration  
  Lab Slides - One-Way ANOVA  

  Lab Assignment 5

DUE DATE: Mon, Nov 24

Week 10 (Dec 1 -Dec 4)

BEFORE CLASS DURING CLASS AFTER CLASS
  Ch 12: Linear Regression and Correlation
  Sec. 12.1 Introduction  
  Sec. 12.2 The Correlation Coefficient  

  FROM Samuels et al. (2016)
  Lecture Slides - Correlation  
No assignment for today
  Ch 12: Linear Regression and Correlation
  Sec. 12.3 The Fitted Regression Line  
  Sec. 12.4 Parametric Interpretation of Regression: The Linear Model  
  Sec. 12.5 Statistical Inference Concerning \(\beta_1\)  

  FROM Samuels et al. (2016) 		  Lecture Slides - Bivariate Regression  

  Week 10 Individual Assignment (on CANVAS)
DUE DATE: Fri, Dec 05
  Ch 12: Linear Regression and Correlation
  Sec. 12.1 Introduction  
  Sec. 12.2 The Correlation Coefficient  
  Sec. 12.3 The Fitted Regression Line  
  Sec. 12.4 Parametric Interpretation of Regression: The Linear Model  
  Sec. 12.5 Statistical Inference Concerning \(\beta_1\)  

  FROM Samuels et al. (2016)
  Lab Slides - Correlation & Bivariate Regression  

  Lab Assignment 6

DUE DATE: Fri, Dec 5
ALL CHAPTERS!   Ungraded Quiz - Google Form

  Review your class notes