Prerequisite: MCR3U or MCF3M
This course broadens students’ understanding of mathematics as it relates to managing data. Students will apply methods for organizing and analysing large amounts of information; solve problems involving probability and statistics; and carry out a culminating investigation that integrates statistical concepts and skills. Students will also refine their use of the mathematical processes necessary for success in senior mathematics. Students planning to enter university programs in business, the social sciences, and the humanities will find this course of particular interest.
Many students find the material in this course to be interesting, and often fun, but take note: virtually all questions are “word problems” and answers are highly dependent on how questions are worded. You will need to read each question carefully to determine what it is asking, then decide which of several techniques is appropriate to obtain a correct solution. Strong analytical and critical-thinking skills are a must to succeed.
The textbook for this course is McGraw-Hill Ryerson’s Mathematics of Data Management 12.
Some notes about using Gnumeric for statistical analysis are here.
Probability Project Samples
- Statistical Measures
- Visualizing Data with Graphs
- Mean, Median and Mode
- Weighted Means and Grouped Data
- Quartiles and Percentiles
- Standard Deviation and z-Scores
- Spreadsheet: 500 Marks
- Spreadsheet: 1000 Cars
- Spreadsheet: After-School Program
- Spreadsheet: Greenhouse Gas Emissions
- Spreadsheet: Earthquake Data
- Link to Statistics Add-On
- Statistical Analysis
- Counting and Permutations
- Sets and Combinations
- Pascal’s Method and Binomial Theorem
- Probability Distributions
- Normal Probability Distribution
- Lesson: Properties of the Normal Distribution
- Worksheet: Properties of the Normal Distribution
- Lesson: Probabilities Using the Normal Distribution
- Worksheet: Probabilities Using the Normal Distribution
- Handout: Z-Table for the Normal Distribution
- Lesson: The Normal Distribution and Discrete Data
- Lesson: Normal Approximation to the Binomnial Distribution