Data management, Grade 12

Course description:
The Grade 12 course *Mathematics of Data Management (MDM4U)* offers students a comprehensive study of data analysis, probability, and statistical methods. This course enables students to gain critical insights into organizing, interpreting, and analyzing data and to make well-informed decisions using mathematical reasoning. Key areas include probability, combinatorics, and an introduction to statistics, allowing students to explore real-world applications relevant to various fields such as business, social sciences, health sciences, and more. Students will also refine their ability to critically evaluate data sources and apply the tools of data analysis in real-life contexts. This course is ideal for those considering post-secondary studies that emphasize statistical data interpretation, including mathematics, sciences, engineering, economics, and psychology.

Chapters and descriptions:

• Introduction to Data Management
  This chapter introduces the role of data in informed decision-making, covering types of data (qualitative vs. quantitative, discrete vs. continuous) and methods for collecting, organizing, and classifying information. Students build foundational skills for effective data analysis.
• Counting Principles and Probability
  Students explore the Fundamental Counting Principle, permutations, and combinations to solve counting problems. Probability concepts, including experimental vs. theoretical probability and conditional probability, are introduced, with tools like Venn diagrams and tree diagrams for visual representation.
• Probability Distributions
  This chapter covers probability distributions, focusing on binomial and normal distributions. Students learn to calculate z-scores, interpret data patterns, and apply these distributions to real-life scenarios, strengthening their data interpretation skills.
• Organization of Data for Analysis
  Students practice descriptive statistics, using measures like mean, median, and standard deviation to analyze data sets. Visualization tools (e.g., histograms, box plots, scatter plots) are introduced, along with technology for data organization and interpretation.
• Statistical Analysis
  In the final chapter, students examine correlation, causation, and linear regression to create and interpret models for bivariate data. Emphasis is placed on analyzing residuals, making predictions, and drawing conclusions from statistical information in real-world contexts.