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    1. Courses
    2. [TEA_GEA] MDM4U, Mathematics of Data Management Grade 12 by Ms. S. Hou (2022-2023)
    • Welcome!

      •  Announcements Forum
        • Textbook: http://ghcimdm4u.weebly.com/ebook.html
        • Textbook Questions Answers: http://ghcimdm4u.weebly.com/uploads/1/3/5/8/13589538/answers.pdf
        • Formula Sheet: http://ghcimdm4u.weebly.com/uploads/1/3/5/8/13589538/key_equations.pdf
        • Meeting ID: 370 181 2189
        Assessment Schedule:
        • Sampling Technique and Bias Due Feb 20 Monday @ 23:59pm EST
        • Statistics of one variable test on Feb 24 Friday 2.5 hours
        • Permutation Test on Mar 10 Friday 2 hours 
        • Combination Test on Mar 17 Friday 2 hours 10am - 12pm, 2 hours
        • Probability Test on Mar 31 Friday
        • Probability Distribution Assignment Due Apr 10 Monday @ 6pm
        • Final Project: Statistics of one and two variables Due Apr 12 Wednesday @ 12pm
        • Final Exam Apr 13 Thursday 8pm - 11pm

      •  Textbook pdf version File
      •  Course Outline File
      •  Final Project - Culminating Task File
      •  Final Project Sample 1 File
      •  Final Project Sample 2 File
    • Unit 1 - Permutations and Organized Counting

      GOALS: By the end of this unit you will be able to (1) Represent complex tasks or issues, using diagrams. (2) Solve introductory counting problems involving the additive and multiplicative counting principles. (3) Express the answers to permutation and combination problems, using standard combinatorial symbols. (4) Evaluate expressions involving factorial notation, using appropriate methods. (5) Solve problems, using techniques for counting permutations where some objects may be alike. (6)Identify patterns in Pascal’s triangle and relate the terms of Pascal’s triangle to values of P(n, r ) to the expansion of a binomial, and to the solution of related problems.(7) Communicate clearly, coherently, and precisely the solutions to counting problems.
      Unit 1 - Permutations and Organized Counting
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    • Lesson 1.1 - Organized Counting
      Lesson 1.1 - Organized Counting
    • Lesson 1.2: Pascal's Triangle & Pathways
      Lesson 1.2: Pascal's Triangle & Pathways
    • Unit 2 - Combinations and the Binomial Theorem

      GOALS: By the end of this unit, you will be able to (1)Use Venn diagrams as a tool for organizing information in counting problems.(2) Solve introductory counting problems involving the additive and multiplicative counting principles. (3) Express answers to permutation and combination problems, using standard combinatorial symbols. (4) Evaluate expressions involving factorial notation, using appropriate methods. (5) Solve problems, using techniques for counting combinations. (6) Identify patterns in Pascal’s triangle and relate the terms of Pascal’s triangle to values of n r, to the expansion of a binomial, and to the solution of related problems. (7) Communicate clearly, coherently, and precisely the solutions to counting problems.
      Unit 2 - Combinations and the Binomial Theorem
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    • Lesson 2.1: Venn Diagram
      Lesson 2.1: Venn Diagram
    • Lesson 2.2: Different Types of Combination
      Lesson 2.2: Different Types of Combination
    • Lesson 2.3: Binomial Theorem and Applications
      Lesson 2.3: Binomial Theorem and Applications
    • Unit 3 - Introduction to Probability

      GOALS: By the end of this unit you will be able to (1) Use Venn diagrams as a tool for organizing information in counting problems. (2) Solve problems, using techniques for counting permutations where some objects may be alike. (3) Solve problems, using techniques for counting combinations. (4) Solve probability problems involving combinations of simple events, using counting techniques. (5) Interpret probability statements, including statements about odds, from a variety of sources. (6) Design and carry out simulations to estimate probabilities in situations for which the calculation of the theoretical probabilities is difficult or impossible.(7) Assess the validity of some simulation results by comparing them with the theoretical probabilities, using the probability concepts developed in the course. (8) Represent complex tasks or issues, using diagrams.(9) Represent numerical data, using matrices, and demonstrate an understanding of terminology and notation related to matrices. (10) Demonstrate proficiency in matrix operations, including addition, scalar multiplication, matrix multiplication, the calculation of row sums, and the calculation of column sums, as necessary to solve problems, with and without the aid of technology. (11) Solve problems drawn from a variety of applications, using matrix methods.
      Unit 3 - Introduction to Probability
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    • Lesson 3.1: Introduction to Probability
      Lesson 3.1: Introduction to Probability
    • Lesson 3.2: Odds
      Lesson 3.2: Odds
    • Lesson 3.3: Dependent and Independent Event
      Lesson 3.3: Dependent and Independent Event
    • Lesson 3.4: Mutually Exclusive & Non-exclusive Event
      Lesson 3.4: Mutually Exclusive & Non-exclusive Event
    • Lesson 3.5: Wrap-up
      Lesson 3.5: Wrap-up
    • Unit 4 - The Probability Distributions

      GOALS: By the end of this unit you will be able to (1) Identify examples of discrete random variables. (2) Construct a discrete probability distribution function by calculating the probabilities of a discrete random variable. (3) Calculate expected values and interpret them within applications as averages over a large number of trials. (4) Determine probabilities, using the binomial distribution. Interpret probability statements, including statements about odds, from a variety of sources. (5) Identify the advantages of using simulations in contexts. (6) Design and carry out simulations to estimate probabilities in situations for which the calculation of the theoretical probabilities is difficult or impossible. (7)Assess the validity of some simulation results by comparing them with the theoretical probabilities, using the probability concepts developed in the course.
      Unit 4 - The Probability Distributions
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    • Lesson 4.1 - Uniform and Nonuniform Distribution
      Lesson 4.1 - Uniform and Nonuniform Distribution
    • Lesson 4.2 - Binomial and Geometric Distribution
      Lesson 4.2 - Binomial and Geometric Distribution
    • Lesson 4.3 - Hypergeometric Distribution
      Lesson 4.3 - Hypergeometric Distribution
    • Unit 5 - Statistics of One Variables

      This unit will focus on the analysis and presentation of one-variable data. Students will process raw data and develop the skills to summarize it in terms of central tendency, spread, and distribution. Students will analyze, interpret, and draw conclusions from one-variable data using numerical and graphical summaries and explore methods of describing a single piece of data in the context of a wider data set. Students use a variety of different software to analyze the presentation of data that has been collected and processed by others. They develop the critical thinking skills necessary to interpret and assess the validity of secondary data and conclusions drawn from it, maintaining an awareness of the possibility of bias and misrepresentation, either deliberate or accidental. Students submit the third part of their DMI where they process and analyse their individual data sets.
      Unit 5 - Statistics of One Variables
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    • Lesson 5.1: Graphical Summary
      Lesson 5.1: Graphical Summary
    • Lesson 5.2: Sampling and Bias
      Lesson 5.2: Sampling and Bias
    • Lesson 5.3: Measure of Central Tendency
      Lesson 5.3: Measure of Central Tendency
    • Lesson 5.4: Measure of Spread
      Lesson 5.4: Measure of Spread
    • Unit 6 - Statistics of Two Variables

      Two-variable statistics are the basis for many decisions personally and as a society. Although most two-variable statistical tests are beyond the scope of secondary school math, this unit will examine some of the basic topics in two-variable statistics. Two-variable statistics provide methods for detecting relationships between variables and for developing mathematics of these relationships. The visual pattern in a graph or plot can often reveal the nature of the relationship between two variables. In this unit students will analyse, interpret, and draw conclusions from two-variable data using numerical, graphical, and algebraic summaries. Students complete the last part of their DMI where they perform analysis of the relationship between the sets of their information, and use critical thinking skills to formulate a final conclusion relating to their initial hypothesis.
      Unit 6 - Statistics of Two Variables
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    • Lesson 6.1: Linear Regression
      Lesson 6.1: Linear Regression
    • Lesson 6.2: Cause and Effect
      Lesson 6.2: Cause and Effect
    • Unit 7 - The Normal Distribution

      GOALS: By the end of this unit you will be able to (1)Interpret one-variable statistics to describe the characteristics of a data set. (2)Organize and summarize data from secondary sources. (3)Identify situations that give rise to common distributions. (4)Interpret probability statements, including statements about odds, from a variety of sources. (5)Assess the validity of some simulation results by comparing them with the theoretical probabilities, using the probability concepts developed in the course. (6)Describe the position of individual observations within a data set, using z-scores and percentiles. (7)Demonstrate an understanding of the properties of the normal distribution. (8)Make probability statements about normal distributions. (9)Illustrate sampling bias and variability by comparing the characteristics of a known population with the characteristics of samples taken repeatedly from that population, using different sampling techniques. (10)Assess the validity of conclusions made on the basis of statistical studies. (11)Determine probabilities, using the binomial distribution.(2)
      Unit 7 - The Normal Distribution
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    • Lesson 7.1 - 7.2: Normal Distribution and Confidence Level
      Lesson 7.1 - 7.2: Normal Distribution and Confidence Level
    • Lesson 7.3: Use normal dis to approximate binomial distribution & Hypothesis test
      Lesson 7.3: Use normal dis to approximate binomial distribution & Hypothesis test
    • Final Project & Exam

      Final Project & Exam
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