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# Mathematics of Data Management, Grade 12 ## Mathematics of Data Management, Grade 12

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.

Prerequisite: Functions, Grade 11, University Preparation or Functions and Applications, Grade 11, University/College Preparation

## Course Outline

### Module 1 Overview: Gathering Data

Guiding Question:
How is data gathered in an unbiased method to obtain a representative sample from the population?

In this module, you will look at types of variables, including qualitative and quantitative variables, used in data collection and organization. You will consider sampling techniques to gather a sample of data from a larger population. You will check for bias in sampling design and survey techniques. You will practice writing different types of survey questions.

### Module 2 Overview: Single Variable Analysis

Guiding Question:
How do measures of central tendency and variability describe a data set and its distribution?

In this module, you will look at techniques to analyze single variable data, including measures of central tendency and measures of variability. You will also consider possible graphs for data, such as histograms and box plots, in order to describe the distribution of the data set.

### Module 3 Overview: Two Variable analysis

Guiding Question:
What types of relationships can exist between two variables?

In this module, you will look at two variable analysis, including scatter plots, equations of lines of best fit and correlation. You can use these calculations to measure the strength of a linear model and to make predictions using the linear equation.

### Module 4 Overview: Counting Techniques

Guiding Question:
How can we distinguish among different types of counting problems (ex. permutations/combinations, with/without repetition)?

In this module, you will look at the fundamental counting principle and counting techniques, including permutations and combinations. You will apply these counting techniques to probability problems in the next module. You will also use Venn diagrams and Pascal’s triangle for counting techniques.

### Module 5 Overview: Probability Strategies

Guiding Question:
How do counting techniques, like permutations and combinations, help to solve probability problems?

In this module, you will look at types of probability events, including mutually exclusive versus non-mutually exclusive and independent versus dependent events. You will also practice different probability techniques, including Venn diagrams, tree diagrams, and the fundamental counting principle.

### Module 6 Overview: Probability Distributions

Guiding Question:
What types of probability distributions can model real life applications?

In this module, you will look at probability distributions. You will consider the binomial, hypergeometric and normal distributions as well as their probability and expected value formulas.