CC Math 7: Chapter 10

Probability & statistics

Video Tutorials

(10.1) Outcomes and Sample Spaces

(10.1) Outcomes and Events

Basic Probability (Watch before starting Lesson 10.2)

Probability (Watch as you go through Lesson 10.2 )

(10.2) Simple Probability

(10.2) Probability Practice Examples

(10.3) Experimental Probability

Note that what you found in 10.2 was Theoretical Probability

(10.3) Experimental Probability

(10.3) Theoretical Probability

(10.4) Compound Events

(10.4) Tree Diagrams for Representing Sample Spaces (Possible Outcomes)

(10.4) Diagramming Compound Sample Spaces

(10.4) Fundamental Counting Principle

(Use this to find the total number of possible outcomes in an experiment with more than one event)

(10.4) Compound Probability of Equally Likely (Independent) Events

(10.5) The Multiplication Rule of Probability

(10.5) Probability of Independent and Dependent Events

(10.5) Independent and Dependent Events

Example Problem: Compound Probability of Independent Events (10.5)

Example Problem: Compound Probability of Dependent Events (10.5)

(10.5) Independent and Dependent Events: Examples with Marbles

Probability Review (10.1 to 10.5)

(10.6) Identifying the Population and the Sample

(10.6) Random and Biased Sampling

(10.6) Generating a Random Sample (brief, but leads into the next video I posted here...)

(10.6) This is not from our textbook (we don't have a section 11.1), but it does still provide a decent overview of Samples and Bias.

10.6) Making a Prediction (inference) from a Sample

This is basically the same as making a prediction using Experimental Probability. The sample data is the experimental data.

(10.6) Making a Prediction (inference) from a Sample

(10.6) Making Predictions from a Sample (some more examples)

How to Read a Box and Whisker Plot Purple math.mp4

(10.7) Comparing (Interpreting) Box-and-Whisker Plots

(10.7) Comparing (Interpreting) Box-and-Whisker Plots for two data sets

(10.7) Comparing and Making Inferences about 2 Populations using Measures of Variation

(10.7) Analyzing and Comparing Dot Plots

SECTION 10.7 makes use of statistical measures of center (mean, median, mode) and measures of variation (range, interquartile range, mean absolute deviation) that were taught in Math 6.

I have posted some review videos here and you can find more here.

Review: Measures of Center (Mean, Median & Mode)

Bonus Video: What measures of central tendency are actually telling us/used for

Quartiles split up a data set into four equal parts (think "quarters") using medians. Remember that a median just splits the data in half.

Each quartile consists of 25% (one-fourth) of the sorted values in the data set. So if there are 20 values in the whole data set, there will be five values in each quartile: 25% of the numbers (the first five) will be in the first quartile (Q1), the next 25% (the next five numbers) will be in the second quartile (Q2), the next 25% will be in the third quartile (Q3), and the last 25% will be in the fourth quartile.

So, quartiles are about making equal groups out of the data. They don't tell you how far apart the values in each group are (spread, distribution).