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Master Probability with Tree Diagrams and Tables

Grade 12 Math: Master Advanced Concepts & Skills

Role of statistical thinking in research and scientific method

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Determining probabilities using tree diagrams and tables

Classification of data

Characteristics of Quantitative Data

Classify the Level of Measurement

Identify the Level of Measurement

Sampling methods

Sampling Methods

Census and bias

Census and Bias

Classifying Bias

Common graphical representations of variation

Frequency tables and dot plots

Analyze the Frequency Table

Frequency distribution and histograms

Reading and drawing bar graphs

read and draw bar graphs

Reading Bar Graphs

Reading and drawing histograms

Reading and drawing line graphs

Reading Line Graphs

Box-and-whisker plots and scatter plots

Bivariate, scatter plots and correlation

Reading and drawing Venn diagrams

Stem and leaf plots

The Leaf and Stem of Numbers

Recognizing the Leaf and Stem of Numbers

Recognizing The Leaf And Stem of Numbers Leaf

Recognizing The Leaf And Stem of Numbers Stem

Reading Stem and Leaf Plots — Median

Reading Stem and Leaf Plots — Range

stem and leaf plots Range

Reading Stem and Leaf Plots

Reading Stem and Leaf Plots — Mean

Reading Stem and Leaf Plots — Mode

Frequency polygons

Shapes of distributions

Use of summary statistics to describe variation

Center of a data set: mean, median, mode

Spread of a data set - standard deviation & variance

Measures of relative standing - z-score, quartiles, percentiles

Probability distribution - histogram, mean, variance & standard deviation

Association between two variables

Regression analysis

Equation of the best fit line

Probability models for variation

Probability of independent events

Addition rule for "OR"

Addition Rule for "OR"

Multiplication rule for "AND"

Multiplication Rule for "AND"

Conditional probability

Probability involving permutations and combinations

Binomial distribution

Binomial Distribution

Mean and standard deviation of binomial distribution

Interpreting Mean and Standard Deviation of Binomial

Introduction to normal distribution

The Normal Distribution and the 68-95-99.7 Rule

Using "invNorm" to Solve Normal Distribution Problems

Normal distribution and continuous random variable

Z-scores and random continuous variables

Sampling distributions

Understanding Proportions and Sample Size

Central limit theorem

Central Limit Theorem

Applying the Central Limit Theorem

Law of total probability

Law of Total Probability

Probability of a randomly selected employee meeting a sales target

Probability of a randomly selected driver being involved in an accident

Probability of a randomly selected customer making a purchase

Probability of a randomly selected person being overweight

Bayes' rule

Bayes" Rule

Bayes rule

Bayes" rule

Bayes' Rule

Poisson distribution

Poisson Distribution

Geometric distribution

Negative binomial distribution

Negative Binomial Distribution

Determining the Negative Binomial Distribution

Determining the Cumulative Negative Binomial Distribution

Hypergeometric distribution

Determining the Cumulative Hypergeometric Distribution

Hypergeometric Distribution

Probability with Venn diagrams

Properties of expectation

Properties of Expectation and Variance

Properties of Expectation

Intuition and appreciation of inferential concepts

Point estimates

Confidence levels and critical values

Margin of error

Margin of Error

Making a confidence interval

Rare event rule

The Rare Event Rule for the Central Limit Theorem

Chi-Squared confidence intervals

Chi-Squared Confidence Intervals

Confidence intervals to estimate population mean

Student's t-distribution

Student"s t-distribution

Determining a Confidence Interval for a Population Mean using t-distributions

Communication of statistical findings

Null hypothesis and alternative hypothesis

Proving claims

Confidence levels, significance levels and critical values

Test statistics

Traditional hypothesis testing

Traditional Mean Hypothesis Testing

P-value hypothesis testing

Proportion Hypothesis Testing

Chi-Squared hypothesis testing

Variance Hypothesis Testing

Chi-Squared Hypothesis Testing

Mean hypothesis testing with t-distribution

Hypothesis Testing Mean Claims without Knowing <katex> \sigma </katex>

Analysis of variance (ANOVA)

Chi-square goodness of fit test

Type 1 and type 2 errors

Type 1 and Type 2 Errors

We&apos;ve got you covered with our complete help for Grade 12 math, whether it&apos;s Ontario curriculum, <a href="https://www.alberta.ca/writing-diploma-exams.aspx">Diploma exam</a> (Alberta), <a href="http://www.edu.gov.mb.ca/k12/assess/tests/index.html">Provincial test</a> (Manitoba) or <a href="https://www.saskatchewan.ca/residents/education-and-learning/departmental-exams">Departmental exam</a> (Saskatchewan).

Aligned with your class or textbook, you will get grade 12 math help on topics like Trigonometric identities, Vectors, Permutations and Combinations, Matrices, Polynomial functions and so many more. Learn the concepts with our video tutorials that show you step-by-step solutions to even the hardest grade 12 math questions. Then, strengthen your understanding with tons of grade 12 math practice. 

All our lessons are taught by experienced Grade 12 math teachers. Let&apos;s finish your homework in no time, and ACE that final.

Statistics 12

math

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Intro

Ex. 1:

Ex. 2:

Ex. 3:

- Hi, welcome to this question right here. So what happened is, I actually did the answer already. But let's just go through the question and see what happens. Right? 
So Ken over here flips a coin. And we know that there's only head and tail on the coin. Right? So basically what happened is at the very beginning you had to get a head or you had to get a tail. Okay? Then you can go to a bag the contents. Find different marbles, each with different colors. And we&apos;ve got the green, the blue, red, orange and the yellow. Okay? So what happened is after you do this, you flip the head. There's a possibly that you can get a green, blue, red, orange or yellow. Right? Same thing with the tail. Okay? 
Once you flipped the tail. You might get a green, blue, red, orange or the yellow. Right? So the total sample space you're going to have for this question is just going to be these guys over here. Total of 10. Okay? We&apos;ve got a head and green, head and blue, head and red, head and orange, head and yellow. And of course got a tail and a green, tail/blue, tail/red, tail/orange and tail and yellow. Okay? So this is the tree diagram for this sample space. Okay? 
Thanks for watching.

Tables and tree diagrams are useful tools to calculate probabilities. With them, you can list all the possible outcomes of an event (<a href='[[glossary.html|{"keyword":"Sample Space"}]]'>sample space</a>) in a clear and easy to understand way. Therefore, we can determine the probabilities of events effectively.

Using tables and tree diagrams for probability