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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: 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 Knowledge and Understanding Application Thinking Communication 40% Mastery Knowledge and Understanding 10% Thinking, Inquiry & Problem Solving Thinking 10% Communication Communication 10% Summative Performance Task Thinking Communication 15% Final Examination 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.