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Grade 12

Master Grade 12 Data Management

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Find the right Ontario Grade 12 course

Grade 12 Advanced Functions, University Preparation (MHF4U)

Grade 12 Calculus and Vectors, University Preparation (MCV4U)

Grade 12 Mathematics of Data Management, University Preparation (MDM4U)

Grade 12 Mathematics for College Technology, College Preparation (MCT4C)

Grade 12 Foundations for College Mathematics, College Preparation (MAP4C)

Grade 12 Mathematics for Work and Everyday Life, Workplace Preparation (MEL4E)

Ontario MDM4U Help: Master Data Management FastHelp

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OE_ID

Expectations

StudyPug Topic

ON.OE.12DM.A1.2

12DM.A1.2: Describe a sample space as a set that contains all possible outcomes of an experiment, and distinguish between a discrete sample space and a continuous sample space

ON.OE.12DM.A1.3

12DM.A1.3: Determine the theoretical probability of each outcome of a discrete sample space, recognize that the sum of the probabilities of the outcomes is 1, recognize that the probabilities form the probability distribution associated with the sample space, and solve related problems

ON.OE.12DM.A1.4

12DM.A1.4: Determine, through investigation using class-generated data and technology-based simulation models, the tendency of experimental probability to approach theoretical probability as the number of trials in an experiment increases

ON.OE.12DM.A1.5

12DM.A1.5: Recognize and describe an event as a set of outcomes and as a subset of a sample space, determine the complement of an event, determine whether two or more events are mutually exclusive or non-mutually exclusive, and solve related probability problems

ON.OE.12DM.A1.6

12DM.A1.6: Determine whether two events are independent or dependent and whether one event is conditional on another event, and solve related probability problems

ON.OE.12DM.A2.1

12DM.A2.1: Recognize the use of permutations and combinations as counting techniques, distinguish between situations that involve the use of permutations and those that involve the use of combinations, and make connections between, and calculate, permutations and combinations

ON.OE.12DM.A2.2

12DM.A2.2: Solve simple problems using techniques for counting permutations and combinations, where all objects are distinct, and express the solutions using standard combinatorial notation

ON.OE.12DM.A2.3

12DM.A2.3: Solve introductory counting problems involving the additive counting principle and the multiplicative counting principle

ON.OE.12DM.A2.4

12DM.A2.4: Make connections, through investigation, between combinations and Pascal's triangle

ON.OE.12DM.A2.5

12DM.A2.5: Solve probability problems using counting principles for situations involving equally likely outcomes

ON.OE.12DM.B1.1

12DM.B1.1: Recognize and identify a discrete random variable, generate a probability distribution by calculating the probabilities associated with all values of a random variable, and represent a probability distribution numerically using a table

ON.OE.12DM.B1.2

12DM.B1.2: Calculate the expected value for a given probability distribution, interpret the expected value in applications, and make connections between the expected value and the weighted mean of the values of the discrete random variable

ON.OE.12DM.B1.3

12DM.B1.3: Represent a probability distribution graphically using a probability histogram, and make connections between the frequency histogram and the probability histogram

ON.OE.12DM.B1.4

12DM.B1.4: Recognize conditions that give rise to a random variable that follows a binomial probability distribution, calculate the probability associated with each value of the random variable, represent the distribution numerically using a table and graphically using a probability histogram, and make connections to the algebraic representation

ON.OE.12DM.B1.5

12DM.B1.5: Recognize conditions that give rise to a random variable that follows a hypergeometric probability distribution, calculate the probability associated with each value of the random variable, and represent the distribution numerically using a table and graphically using a probability histogram

ON.OE.12DM.B1.6

12DM.B1.6: Compare, with technology and using numeric and graphical representations, the probability distributions of discrete random variables

ON.OE.12DM.B1.7

12DM.B1.7: Solve problems involving probability distributions, including problems arising from real-world applications

ON.OE.12DM.B2.1

12DM.B2.1: Recognize and identify a continuous random variable, and distinguish between situations that give rise to discrete frequency distributions and situations that give rise to continuous frequency distributions

ON.OE.12DM.B2.2

12DM.B2.2: Recognize standard deviation as a measure of the spread of a distribution, and determine, with and without technology, the mean and standard deviation of a sample of values of a continuous random variable

ON.OE.12DM.B2.3

12DM.B2.3: Describe challenges associated with determining a continuous frequency distribution, and recognize the need for mathematical models to represent continuous frequency distributions

ON.OE.12DM.B2.4

12DM.B2.4: Represent, using intervals, a sample of values of a continuous random variable numerically using a frequency table and graphically using a frequency histogram and a frequency polygon

ON.OE.12DM.B2.5

12DM.B2.5: Recognize that theoretical probability for a continuous random variable is determined over a range of values, that the probability that a continuous random variable takes any single value is zero, and that the probabilities of ranges of values form the probability distribution associated with the random variable

ON.OE.12DM.B2.6

12DM.B2.6: Recognize that the normal distribution is commonly used to model the frequency and probability distributions of continuous random variables, describe some properties of the normal distribution, and recognize and describe situations that can be modelled using the normal distribution

ON.OE.12DM.B2.7

12DM.B2.7: Make connections, through investigation using dynamic statistical software, between the normal distribution and the binomial and hypergeometric distributions for increasing numbers of trials of the discrete distributions

ON.OE.12DM.B2.8

12DM.B2.8: Recognize a z-score as the positive or negative number of standard deviations from the mean to a value of the continuous random variable, and solve probability problems involving normal distributions using a variety of tools and strategies

ON.OE.12DM.C1.1

12DM.C1.1: Recognize and describe the role of data in statistical studies, describe examples of applications of statistical studies, and recognize that conclusions drawn from statistical studies of the same relationship may differ

ON.OE.12DM.C1.3

12DM.C1.3: Distinguish different types of statistical data

ON.OE.12DM.C2.1

12DM.C2.1: Determine and describe principles of primary data collection and criteria that should be considered in order to collect reliable primary data

ON.OE.12DM.C2.2

12DM.C2.2: Explain the distinction between the terms population and sample, describe the characteristics of a good sample, explain why sampling is necessary, and describe and compare some sampling techniques

ON.OE.12DM.C2.3

12DM.C2.3: Describe how the use of random samples with a bias or the use of non-random samples can affect the results of a study

ON.OE.12DM.C2.4

12DM.C2.4: Describe characteristics of an effective survey, and design questionnaires or experiments for gathering data

ON.OE.12DM.C2.5

12DM.C2.5: Collect data from primary sources, through experimentation, or from secondary sources, and organize data with one or more attributes to answer a question or solve a problem

ON.OE.12DM.D1.1

12DM.D1.1: Recognize that the analysis of one-variable data involves the frequencies associated with one attribute, and determine, using technology, the relevant numerical summaries

ON.OE.12DM.D1.2

12DM.D1.2: Determine the positions of individual data points within a one-variable data set using quartiles, percentiles, and z-scores, use the normal distribution to model suitable one-variable data sets, and recognize these processes as strategies for one-variable data analysis

ON.OE.12DM.D1.3

12DM.D1.3: Generate, using technology, the relevant graphical summaries of one-variable data based on the type of data provided

ON.OE.12DM.D1.4

12DM.D1.4: Interpret, for a normally distributed population, the meaning of a statistic qualified by a statement describing the margin of error and the confidence level, and make connections between the sample size, the margin of error, and the confidence level

ON.OE.12DM.D1.5

12DM.D1.5: Interpret statistical summaries to describe the characteristics of a one-variable data set and to compare two related one-variable data sets; describe how statistical summaries can be used to misrepresent one-variable data; and make inferences, and make and justify conclusions, from statistical summaries of one-variable data

ON.OE.12DM.D2.1

12DM.D2.1: Recognize that the analysis of two-variable data involves the relationship between two attributes, recognize the correlation coefficient as a measure of the fit of the data to a linear model, and determine, using technology, the relevant numerical summaries

ON.OE.12DM.D2.2

12DM.D2.2: Recognize and distinguish different types of relationships between two variables that have a mathematical correlation

ON.OE.12DM.D2.3

12DM.D2.3: Generate, using technology, the relevant graphical summaries of two-variable data based on the type of data provided

ON.OE.12DM.D2.4

12DM.D2.4: Determine, by performing a linear regression using technology, the equation of a line that models a suitable two-variable data set, determine the fit of an individual data point to the linear model, and recognize these processes as strategies for two-variable data analysis

ON.OE.12DM.D2.5

12DM.D2.5: Interpret statistical summaries to describe the characteristics of a two-variable data set and to compare two related two-variable data sets; describe how statistical summaries can be used to misrepresent two-variable data; and make inferences, and make and justify conclusions, from statistical summaries of two-variable data

ON.OE.12DM.D3.1

12DM.D3.1: Interpret statistics presented in the media, and explain how the media, the advertising industry, and others use and misuse statistics to promote a certain point of view

ON.OE.12DM.D3.2

12DM.D3.2: Assess the validity of conclusions presented in the media by examining sources of data, methods of data collection, and possible sources of bias, and by questioning the analysis of the data and conclusions drawn from the data

ON.OE.12DM.D3.3

12DM.D3.3: Gather, interpret, and describe information about applications of data management in occupations, and about university programs that explore these applications

ON.OE.12DM.E1.1

12DM.E1.1: Pose a significant problem of interest that requires the organization and analysis of a suitable set of primary or secondary quantitative data, and conduct appropriate background research related to the topic being studied

ON.OE.12DM.E1.2

12DM.E1.2: Design a plan to study the problem

ON.OE.12DM.E1.3

12DM.E1.3: Gather data related to the study of the problem and organize the data

ON.OE.12DM.E1.4

12DM.E1.4: Interpret, analyse, and summarize data related to the study of the problem

ON.OE.12DM.E1.5

12DM.E1.5: Draw conclusions from the analysis of the data, evaluate the strength of the evidence, specify any limitations of the conclusions, and suggest follow-up problems or investigations

ON.OE.12DM.E2.1

12DM.E2.1: Compile a clear, well-organized, and detailed report of the investigation

ON.OE.12DM.E2.2

12DM.E2.2: Present a summary of the culminating investigation to an audience of their peers within a specified length of time

ON.OE.12DM.E2.3

12DM.E2.3: Answer questions about the culminating investigation and respond to critiques

ON.OE.12DM.E2.4

12DM.E2.4: Critique the mathematical work of others in a constructive manner
Complete Ontario Grade 12 Mathematics of Data Management Coverage

MDM4U Lessons

64

Video Explanations

502

Practice Problems

710

Ontario Standards

100% Aligned

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Frequently Asked Questions

Everything you need to know about mastering MDM4U with StudyPug

What does MDM4U coverage include?

Complete Grade 12 Mathematics of Data Management curriculum with video lessons on probability, counting principles, statistical distributions, data collection, and analysis. Includes practice problems, step-by-step solutions, and progress tracking.

How does the AI photo search work for probability problems?

Take a photo of any MDM4U problem, and our AI finds the exact lesson teaching that concept. It's like having a personal tutor who knows exactly what you need for university preparation.

Are the teachers certified Ontario educators?

Yes! Our teachers are Canadian certified Ontario educators who understand the MDM4U curriculum and create lessons specifically aligned with Ontario university preparation standards.

Can I use StudyPug on my phone or tablet?

Absolutely! StudyPug works seamlessly on desktop, tablet, and mobile devices. Your progress syncs automatically so you can study for MDM4U anywhere, anytime.

How will StudyPug help me prepare for university?

We teach the exact data management and statistical analysis concepts universities expect. Students report significantly improved grades, stronger problem-solving skills, and greater confidence in university-level mathematics.

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