MATH20025
Statistics for Health Sciences |
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First Nations peoples have lived on this part of Turtle Island for millennia, stewarding the land, the water and all that contributes to life in this region. Today, the culture and presence of First Nations, Inuit and Metis peoples enrich the lands and people of this territory.
Over two centuries ago, the Mississauga people welcomed settlers to this territory, providing sustenance and engaging in trade and commerce. Between 1781 to 1820, eight treaties were signed with the Mississaugas of the Credit First Nation who opened their territory to settlement. Today, Sheridan campuses are located on Treaty 14, also known as the Head of the Lake Purchase of 1806 and Treaty 22 and 23 of 1820.
Treaty history is foundational, and it is our collective responsibility to honour the land, as we honour and respect those who have gone before us, those who are here and those who have yet to come. We are grateful for the opportunity to be learning, working and thriving on this land, and we commit to learn the truth and be active in the process of reconciliation.
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Section I: Administrative Information
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Total hours: 42.0
Credit Value: 3.0
Credit Value Notes: N/A
Effective: Spring/Summer 2026
Prerequisites: (MATH11267) OR (MATH10172)
Corequisites: N/A
Equivalents: N/A
Pre/Co/Equiv Notes: N/A |
Program(s):
Pre-Health Sciences Pathway
Program Coordinator(s):
Anthony Tavares
Course Leader or Contact: Sean Saunders
Version: 20260504_00
Status: Approved (APPR)
Section I Notes:
N/A
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Section II: Course Details
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Detailed Description
Students analyze quantitative data that arise in the health sciences, by learning fundamental terms and principles in statistics, methods for gathering and organizing data, and quantitative techniques to describe and model the data, and then apply the results of their analysis to form conclusions and draw inferences about the data. Students engage in interactive lectures, individual investigations, web-based resources, assignments, and tests, to reinforce the core concepts and provide career and life context for the concepts learned.
Program Context
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| Pre-Health Sciences Pathway |
Program Coordinator(s):
Anthony Tavares |
This is a required core course in the Pre-Health Sciences Pathway to Advanced Diplomas and Degrees certificate program. It is designed to give students the necessary experience in intermediate algebra and statistics to be successful in a diploma or degree program in the health sciences or a related field.
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Course Critical Performance and Learning Outcomes
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Critical Performance: |
| By the end of this course, students will have demonstrated the ability to assess data and the relationships that exist within and between different sets of data, using a variety of statistical and mathematical models.
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Learning Outcomes:
To achieve the critical performance, students will have demonstrated the ability to:
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- Summarize data and its meaning using statistical charts and tables.
- Calculate statistical measures of centre and variation for quantitative data.
- Model data arising from problems and scenarios in the health sciences and in day-to-day life, using linear regression equations.
- Analyze the relationship between two variables using linear correlation and regression analysis.
- Determine the probability of occurrence of events in the health sciences using trees, Venn diagrams, contingency tables, and probability formulas.
- Distinguish between discrete and continuous random variables.
- Evaluate probabilities and statistical measures such as the mean, standard deviation, and various percentiles, using the binomial distribution and the normal distribution.
- Apply the Central Limit Theorem to sampling distributions of sample means and sample proportions.
- Estimate the population mean and/or proportion using a confidence interval.
- Draw conclusions for a hypothesis about the value of a population parameter.
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Evaluation Plan
Students demonstrate their learning in the following ways:
| | Evaluation Plan: IN-CLASS
| | Intromath Assignments (8 x 2.5% each) | 20.0% | | | Module Tests (2 x 10% each) | 20.0% | | | Statistics Project | 15.0% | | | Midterm Exam | 20.0% | | | Final Exam | 25.0% | | Total | 100.0% |
Evaluation Notes and Academic Missed Work Procedure: TEST AND ASSIGNMENT PROTOCOL
To encourage behaviours that will help students to be successful in the workplace and to ensure that students receive credit for their individual work, the following rules apply to every course offered within the Faculty of Humanities and Social Sciences.
1. Students are responsible for staying abreast of test dates and times, as well as due dates and any special instructions for submitting assignments and projects as supplied to the class by the professor.
2. Students must write all tests at the specified times. Missed tests, in-class activities, assignments and presentations are awarded a mark of zero. If an extension or make-up opportunity is approved by the professor as outlined below, the mark of zero may be revised by subsequent performance. The penalty for late submission of written assignments is a loss of 10% per day for up to five business days (excluding weekends and statutory holidays), after which, a grade of zero is assigned. Business days include any day that the college is open for business, whether the student has scheduled classes that day or not.
3. Students, who miss a test or in-class activity or assignment or fail to submit an assignment on time due to exceptional circumstances, are required to notify their professor in advance of the class whenever possible. A make-up test may be supplied for students who provide an acceptable explanation of their absence and/or acceptable documentation explaining their absence (e.g., a medical certificate). All make-up tests are to be written at a time and place specified by the professor upon the student's return. Alternately, students may be given an opportunity to earn the associated marks by having a subsequent test count for the additional marks. Exceptional circumstances may result in a modification of due dates for assignments.
4. Unless otherwise specified, assignments and projects must be submitted at the beginning of class.
5. Students must complete every assignment as an individual effort unless, the professor specifies otherwise.
6. Inappropriate use of Artificial Intelligence and Digital Technology (as defined in the Academic Integrity Policy) is not permitted in any course in FHASS. Faculty may provide explicit guidelines on the use of Generative AI, digital technology, and machine writing tools in all courses within FHASS. Where no course-specific guidelines are provided, any use of Generative AI is strictly prohibited. Students are responsible for understanding expectations for every evaluation.
7. Since there may be instances of grade appeal or questions regarding the timely completion of assignments and/or extent of individual effort, etc., students are strongly advised to keep, and make available to their professor, if requested, a copy of all assignments and working notes until the course grade has been finalized.
8. There will be no resubmission of work unless this has been previously agreed to or suggested by the professor.
9. Students must submit all assignments in courses with practical lab and field components in order to pass the course.
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Provincial Context
The course meets the following Ministry of Colleges and Universities requirements:
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Essential Employability
Skills
Essential Employability Skills emphasized in the course:
- Critical Thinking & Problem Solving Skills - Use a variety of thinking skills to anticipate and solve problems.
- Critical Thinking & Problem Solving - Apply a systematic approach to solve problems.
- Information Management Skills - Analyze, evaluate, and apply relevant information from a variety of sources.
- Information Management - Locate, select, organize and document information using appropriate technology and information systems.
- Numeracy - Execute mathematical operations accurately.
Prior Learning Assessment and Recognition
PLAR Contact (if course is PLAR-eligible) - Office of the Registrar
Students may apply to receive credit by demonstrating achievement of the course learning outcomes through previous relevant work/life experience, service, self-study and training on the job. This course is eligible for challenge through the following method(s):
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Section III: Topical Outline
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Some details of this outline may change as a result of circumstances such as weather cancellations, College and student activities, and class timetabling.
Instruction Mode: In-Class
Professor: Multiple Professors
Resource(s):
Course material costs can be found through the Sheridan Bookstore
| | Type | Description | | Required | Textbook | Statistics for Health Sciences, Saunders, S., Kugathasan, T., & Thanasse, M., Toronto, ON: Vretta Inc., 2nd Ed., 2022 |
Applicable student group(s): Pre-Health Sciences Pathway to Advanced Diplomas and Degrees
Course Details: MODULE 1 ? DESCRIPTIVE STATISTICS Unit 1: Introduction to Statistical Methods and Data Organization
- Defining statistical terms, including ?Population? and ?Sample?
- Differentiating between descriptive and inferential statistics
- Identifying sampling methods and types of errors
- Differentiating between different types of statistical studies
- Identifying types of variables and levels of measurement
- Using Excel to summarize data with charts, tables and graphs
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapters 1 and 2
Intromath Assignment #1 ? 2.5%
Unit 2: Summarizing Data - Calculating mean, median, mode, trimmed means and weighted means
- Comparing various measures of centre and determining when to use each
- Calculating measures of centre for grouped data
- Determining shape, symmetry and skewness of a distribution
- Calculating range, quartiles, percentiles and inter-quartile range (IQR)
- Summarizing data with box-and-whisker plots
- Calculating variance and standard deviation
- Identifying outliers and their impact on a data set
- Using Excel to calculate statistical measures for large data sets
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 3
Intromath Assignment #2 ? 2.5%
Statistics Project (Part 1: Organizing and Analyzing Single Variable Data) ? 7.5%
Module 1 Test ? 10%
MODULE 2 ? PROBABILITY
Unit 3: Elementary Probability Theory
- Determining empirical probability based on past events
- Determining theoretical probability using the sample space
- Calculating probability using outcome tables, trees and Venn diagrams
- Identifying mutually exclusive events and independent events
- Determining the probability of compound events using the rules of addition and multiplication
- Calculating conditional probability using contingency tables
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 4
Intromath Assignment #3 ? 2.5%
Unit 4: Discrete Probability Distributions
- Differentiating between discrete and continuous random variables
- Constructing a probability distribution for a discrete random variable
- Calculating the mean (expected value), variance and standard deviation for a discrete probability distribution, such as the binomial distribution
- Calculating the probability distribution for a binomial random variable
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 5.1-5.2
Intromath Assignment #4 ? 2.5%
Midterm Exam ? 20%
MODULE 3: INFERENTIAL STATISTICS
Unit 5: Continuous Random Variables and the Normal Distribution
- Identifying probability distribution functions for continuous random variables
- Calculating cumulative probability for a continuous variable with a uniform distribution
- Calculating cumulative probability for a continuous variable with a standard normal distribution
- Converting values of a variable following a normal distribution to z-values
- Using Excel or a ?Standard Normal Cumulative Distribution Table? to relate probabilities and z-values for variables following a normal distribution
- Calculating probabilities and statistical measures, such as mean, standard deviation, and percentiles, for variables following a normal distribution
- Using the normal distribution to approximate the binomial distribution
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 6.1-6.3
Intromath Assignment #5 ? 2.5%
Unit 6: Sample Distributions and Confidence Intervals for Sample Means
- Comparing population distributions and sampling distributions
- Applying the Central Limit Theorem to sampling distributions of sample means
- Computing the standard error of the sample means
- Differentiating between point and range estimates for population means
- Constructing a confidence interval for the population mean given a sample mean and population standard deviation (i.e. ? is known) using the z-distribution.
- Identifying the properties and characteristics of the Student?s t-distribution
- Determining when to use the normal z-distribution or the Student?s t-distribution
- Constructing a confidence interval for the population mean given a sample mean and sample standard deviation (i.e. ? is unknown) using the t-distribution
- Estimating the sample size needed to obtain an estimate for the population mean within a given margin of error at a specified level of confidence
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 7
Intromath Assignment #6 ? 2.5%
Unit 7: Sample Distributions and Confidence Intervals for Sample Proportions
- Constructing the p-hat sampling distribution of sample proportions
- Computing the standard error of the sample proportions
- Using the normal distribution to approximate the p-hat distribution
- Differentiating between point and range estimates for population proportions
- Constructing a confidence interval for a population proportion
- Estimating the sample size needed to obtain an estimate for the population proportion within a given margin of error at a specified level of confidence
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 8
Intromath Assignment #7 ? 2.5% Module 3 Test - 10%
Unit 8: Hypothesis Testing
- Setting up a formal hypothesis test
- Establishing the null and alternative hypothesis
- Distinguishing between one- and two-tailed hypothesis tests
- Establishing an appropriate level of significance
- Identifying critical regions and computing p-values
- Testing a hypothesis about a population mean or population parameter
- Drawing an appropriate conclusion based on the hypothesis test
- Identifying possible Type I and Type II errors based on the conclusion drawn
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 9.1 - 9.3
Intromath Assignment #8 ? 2.5%
MODULE 4 ? DATA MODELLING
Unit 9: Correlation and Regression
- Constructing XY-scatterplots of bivariate data
- Calculating and interpreting the linear correlation between two sets of data using Pearson?s linear correlation coefficient (r)
- Fitting a linear regression equation to a bivariate data set
- Making predictions with the linear regression equation
- Computing residuals and constructing a residual plot
- Identifying non-linear trends and outliers in the data using a residual plot
- Calculating and interpreting the coefficient of determination (R2)
- Using Excel to model data and perform regression analysis
STATISTICS FOR HEALTH SCIENCES 2nd ED: Chapter 10.1, 10.3
Intromath Assignment #9 ? 2.5%
Statistics Project (Part 2: Organizing and Analyzing Bivariate Data) ? 7.5%
Final Exam ? 25%
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It is recommended that students read the following policies in relation to course outlines:
All Sheridan policies can be viewed on the Sheridan policy website.
In alignment with Sheridan's Academic Integrity Policy, students should consult with their professors and/or refer to evaluation instructions regarding the appropriate use, or prohibition, of generative Artificial Intelligence (AI) tools for coursework. Turnitin AI detection software may be used by faculty members to screen assignment submissions or exams for unauthorized use of artificial intelligence. Students are encouraged to engage with generative AI in teaching and learning contexts thoughtfully. Please review the Guidelines for the Responsible Use of Generative Artificial Intelligence at Sheridan College.
The information contained in this Course Outline including but not limited to faculty and program information and course description is subject to change without notice. . Nothing in this Course Outline should be viewed as a representation, offer and/or warranty. Students are responsible for reading the Important Notice and Disclaimer which applies to Programs and Courses.
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