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MDM4U
Summer 2012
Data Management
Prerequisites: MCR3U (Functions) or MCF3M (Functions and Applications)
Recommended Mark: Level 3- (70%)
Policy Document: The Ontario Curriculum, Grades 11 and 12: Mathematics (2007)
Course Description
This course introduces students to the fields of statistics, probability and combinatorics. Students develop critical thinking
skills as they apply mathematics to a variety of contexts and explore the use of data in the media. Students will learn and
apply methods for organizing and analyzing large amounts of information, drawing conclusions from data and recognizing
sources of bias. They will solve problems using probability, combinatorics and statistics, and carry out a culminating
project that integrates statistical concepts and skills with a topic of their choice.
Topics
i.
ii.
iii.
iv.
v.
vi.
Organizing Data
Sampling and Data Collection
Data Analysis
Probability and Combinatorics
Probability Distributions
Culminating Project
Mathematical Processes
The mathematical processes are a set of interconnected thinking skills that support lifelong learning in mathematics.
Students develop and apply these skills in all math courses as they work to achieve the expectations outlined within each
course. These skills are developed through problem-solving experiences that incorporate a variety of approaches, including
investigation. The mathematical processes are:
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Problem Solving
Reasoning and Proving
Reflecting
Selecting Tools and Computational Strategies
Connecting
Representing
Communicating
Learning Skills
Learning skills are student habits and behaviours that enable them to learn effectively and achieve their potential. They are
critical to success in all subject areas. Initiative, independent work, organization, self-regulation, collaboration and
responsibility will be assessed throughout the course, and communicated on the report card.
Student Absences
Students are responsible for all work missed regardless of the reason for the absence. If you are away, you WILL miss
something important! Work must be completed before returning to school in order to remain connected to the development
of the concepts.
Students who expect to miss school due to family vacations must notify the Principal in writing, in advance. Vacations
cannot be recognized as legitimate reasons for exemption from formal evaluation. Refer to Math Department policy on
Missed / Late Assessments for more detailed information.
Textbook
Your textbook is Mathematics of Data Management (Nelson). You must return it in the condition that you receive it or you
will be charged a fee for damages.
MDM4U
Summer 2012
Evaluation
The final mark consists of two components: term work (70%) and summative evaluation (30%).
Term Work
During the term, students will be evaluated against the overall expectations of the course, with respect to the categories of
Knowledge and Understanding, Application, Communication, and Thinking as specified in the achievement chart of
the Ministry of Education curriculum documents. Evaluation should be viewed as an opportunity to demonstrate
achievement of course expectations. Evaluation will be varied, and will include mastery tests (10% of final mark), unit tests
and performance assessments. It may also include other assignments, projects, investigations and classroom activities.
Summative Evaluation
The summative evaluation occurs near the end of the course, and has two components: a final examination (15%) and the
culminating project (15%). Attendance is mandatory for both of these evaluations.
Mathematics of Data Management: Overall Expectations
(from: The Ontario Curriculum, Grades 11 and 12: Mathematics)
By the end of this course, students will:
A: Counting and Probability
1. solve problems involving the probability of an event or a combination of events for discrete sample spaces
2. solve problems involving the application of permutations and combinations to determine the probability of an
event
B: Probability Distributions
1. demonstrate an understanding of discrete probability distributions, represent them numerically, graphically, and
algebraically, determine expected values, and solve related problems from a variety of applications
2. demonstrate an understanding of continuous probability distributions, make connections to discrete probability
distributions, determine standard deviations, describe key features of the normal distribution, and solve related
problems from a variety of applications
C: Organization of Data for Analysis
1. demonstrate an understanding of the role of data in statistical studies and the variability inherent in data, and
distinguish different types of data
2. describe the characteristics of a good sample, some sampling techniques, and principles of primary data collection,
and collect and organize data to solve a problem
D: Statistical Analysis
1. analyse, interpret, and draw conclusions from one-variable data using numerical and graphical summaries
2. analyse, interpret, and draw conclusions from two-variable data using numerical, graphical and algebraic
summaries
3. demonstrate an understanding of the applications of data management used by the media and the advertising
industry and in various occupations
E: Culminating Data Management Investigation
1. design and carry out a culminating investigation that requires the integration and application of the knowledge and
skills related to the expectations of this course
2. communicate the findings of a culminating investigation and provide constructive critiques of the investigations of
others
MDM4U
Summer 2012
Evaluation Framework
The breakdown of marks is described in the table below:
Evaluation Focus
Marks
Overall Expectations
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Knowledge and Understanding
Application
Thinking
Communication
40%
Mastery
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Knowledge and Understanding
10%
Thinking, Inquiry & Problem Solving
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Thinking
10%
Communication
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Communication
10%
Summative Performance Task
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Thinking
Communication
15%
Final Examination
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Knowledge and Understanding
Application
Communication
15%
TERM
SUMMATIVE
Achievement Chart Categories
The Overall Expectation mark (40%) is broken down further according to strand and expectation in
terms of the nature of the expectation and described in the table below:
Strand
Counting and
Probability
Probability
Distributions
Organization of data for
analysis
Strand Weight
Statistical Analysis
11
11
11
7
Overall Expectation
A1
A2
B1
B2
C1
C2
D1
D2
D3
Expectation Weight
6
5
5
6
4
3
4
4
3
For more detailed information about the overall expectations, refer to the course outline or ask your
teacher.