Math Antics has amazing videos to explain concepts for Math. The videos are very clear and explicit and students love them. All of the video lessons are FREE.

There are also follow up exercises, videos and worksheets that students can use to solidify learning - but you will be required to pay $20 a year to access these. hat being said, it's super useful even without a paid account!

The videos are organized by strand, and all are free.

They cover numeracy, arithmetic, algorithms, fractions, mixed numbers, percentages, ratios, geometry, statistics, measurement, exponents, integers & algebra, algebra 1-3

Ce site vous offre de l'appui au sujet des mathématiques. Il s'agit de cliquer sur la section: centrale des maths

C'est un service pour les enseignants de mathématiques et leur élèves.

Run by the University of Regina in Canada, this site offers free resources for math teachers and their students, including a database where users can search for the answers to math questions.

Their Mathematics with a Human Face page includes information about careers in mathematics as well as profiles of mathematicians.

This amazing site from Alberta has interactive for all the strands.
These could be used by students or in small group instruction or as a whole class.

Today we’re going to talk about numeracy - that is understanding numbers. From really really big numbers to really small numbers, it's difficult to comprehend information at this scale, but these are often the types of numbers we see most in statistics. So understanding how these numbers work, how to best visualize them, and how they affect our world can help us become better decision makers - from deciding if we should really worry about Ebola to helping improve fighter jets during World War II!

Today we’re going to talk about measures of central tendency - those are the numbers that tend to hang out in the middle of our data: the mean, the median, and mode. All of these numbers can be called “averages” and they’re the numbers we tend to see most often - whether it’s in politics when talking about polling or income equality to batting averages in baseball (and cricket) and Amazon reviews. Averages are everywhere so today we’re going to discuss how these measures differ, how their relationship with one another can tell us a lot about the underlying data, and how they are sometimes used to mislead.

Students experience data collection, analysis and inquiry in this LEGO® MINDSTORMS® NXT -based activity. They measure the position of an oscillating platform using a ultrasonic sensor and perform statistical analysis to determine the mean, mode, median, percent difference and percent error for the collected data.

Students learn about the statistical analysis of measurements and error propagation, reviewing concepts of precision, accuracy and error types. This is done through calculations related to the concept of density. Students work in teams to each measure the dimensions and mass of five identical cubes, compile the measurements into small data sets, calculate statistics including the mean and standard deviation of these measurements, and use the mean values of the measurements to calculate density of the cubes. Then they use this calculated density to determine the mass of a new object made of the same material. This is done by measuring the appropriate dimensions of the new object, calculating its volume, and then calculating its mass using the density value. Next, the mass of the new object is measured by each student group and the standard deviation of the measurements is calculated. Finally, students determine the accuracy of the calculated mass by comparing it to the measured mass, determining whether the difference in the measurements is more or less than the standard deviation.

Today, we're looking at measures of spread, or dispersion, which we use to understand how well medians and means represent the data, and how reliable our conclusions are. They can help understand test scores, income inequality, spot stock bubbles, and plan gambling junkets. They're pretty useful, and now you're going to know how to calculate them!

Contained herein are projects for Grades 6 - 9 Math Outcomes for Stats and Probability. They are meant to use as extensions to the knowledge and skills of those outcomes and connect with real life and can be used in a Cross Curr with modifications. 

Today we're going to talk big picture about what Neural Networks are and how they work. Neural Networks, which are computer models that act like neurons in the human brain, are really popular right now - they're being used in everything from self-driving cars and Snapchat filters to even creating original art! As data gets bigger and bigger neural networks will likely play an increasingly important role in helping us make sense of all that data.

Today is the day we finally talk about the normal distribution! The normal distribution is incredibly important in statistics because distributions of means are normally distributed even if populations aren't. We'll get into why this is so - due to the Central Limit Theorem - but it's useful because it allows us to make comparisons between different groups even if we don't know the underlying distribution of the population being studied.

The following resource contains the assets (or resources) to accompany the Sask DLC Mathematics 8 course. Please note that this is not the content of the course, but the assets used to support and deliver it. The files are organized in a zip folder and a collection. 

The following file contains the assets (or resources) to accompany the Sask DLC Grade 9 Mathematics. Please note that this is not the content of the course, but the assets used to support and deliver it. The files are organized in a zipped folder. You can download it and extract the files. Links are also provided to other materials like videos and other suggested resources. 

Today we're going to talk about p-hacking (also called data dredging or data fishing). P-hacking is when data is analyzed to find patterns that produce statistically significant results, even if there really isn't an underlying effect, and it has become a huge problem in science since many scientific theories rely on p-values as proof of their existence! Today, we're going to talk about a few ways researchers have "hacked" their data, and give you some tips for identifying and avoiding these types of problems when you encounter stats in your own lives.

Last week we introduced p-values as a way to set a predetermined cutoff when testing if something seems unusual enough to reject our null hypothesis - that they are the same. But today we’re going to discuss some problems with the logic of p-values, how they are commonly misinterpreted, how p-values don’t give us exactly what we want to know, and how that cutoff is arbitrary - and arguably not stringent enough in some scenarios.

The number of amazing video math tutorials available to you here will blow your mind!

There are hundreds and hundreds of videos to help you and your students or child perfect their "math game".

The videos are organized into these groups: algebra, arithmetic, calculus, differential equations, discrete math, linear algebra, probability and stats, trig and a misc category.

Oxford mathematician Peter Donnelly reveals the common mistakes humans make in interpreting statistics -- and the devastating impact these errors can have on the outcome of criminal trials. Peter Donnelly is an expert in probability theory who applies statistical methods to genetic data -- spurring advances in disease treatment and insight on our evolution. He's also an expert on DNA analysis, and an advocate for sensible statistical analysis in the courtroom. A quiz, thought provoking question, and links for further study are provided to create a lesson around the 11-minute video. Educators may use the platform to easily "Flip" or create their own lesson for use with their students of any age or level.

We're going to finish up our discussion of p-values by taking a closer look at how they can get it wrong, and what we can do to minimize those errors. We'll discuss Type 1 (when we think we've detected an effect, but there actually isn't one) and Type 2 (when there was an effect we didn't see) errors and introduce statistical power - which tells us the chance of detecting an effect if there is one.

Today we’re going to finish up our unit on data visualization by taking a closer look at how dot plots, box plots, and stem and leaf plots represent data. We’ll also talk about the rules we can use to identify outliers and apply our new data viz skills by taking a closer look at how Justin Timberlake’s song lyrics have changed since he went solo.