Two solstices occur on Earth every year, around June 21^st and December 21^st, often called the “June Solstice” and the “December Solstice” respectively. These solstices occur when the sun reaches its northernmost or southernmost point relative to the equator. During a solstice, the Northern Hemisphere will either experience the most sunlight of the year or the least sunlight of the year, while the Southern Hemisphere will experience the opposite phenomenon. The hemisphere with the most sunlight experiences a summer solstice, while the other hemisphere experiences a winter solstice.

Canada is located in the Northern Hemisphere and this Thursday, December 21^st, we will be experiencing a winter solstice. As the day with the least sunlight, this will be the shortest day of the year and consequently the longest night of the year.

Here in Canada, the sun will reach its minimum elevation during the winter solstice, and it will reach its maximum elevation during the Southern Hemisphere’s summer solstice on the same day. 

How high in the sky does the sun really get during these solstices? Check out our new Maple Learn document, Winter and Summer Solstice Sun Angles to find out. The answer depends on your latitude; for instance, with a latitude of approximately 43.51°, the document helps us find that the maximum midday elevation of the sun, which occurs during a summer solstice, will be 69.99°.

But how is the latitude of a location determined in the first place? See Maple Learn’s Calculating Latitude document to find out how the star Polaris, the center of the Earth, and the equator are all connected to latitude.

Latitude is one of two geographical coordinates that are paired together to specify a position on Earth, the other being longitude. See our Calculating Longitude document to explore how you can use your local time to approximate your longitude.

Armed with these coordinates, you can describe your position on the planet and find any number of interesting facts, such as your solstice sun angles from earlier, the time for sunrise and sunset, and the position of the Moon.

Happy Winter Solstice!

How much did you weigh when you were born? How tall are you? What is your current blood pressure? It is well documented that in the general population, these variables – birth weight, height, and blood pressure – are normally or approximately normally distributed. This is the case for many variables in the natural and social sciences, which makes the normal distribution a key distribution used in research and experiments. 

The Maple Learn Examples Gallery now includes a series of documents about normal distributions and related topics in the Continuous Probability Distributions subcollection.

The Normal Distribution: Overview will introduce you to the probability density function, cumulative distribution function, and the parameters of the distribution. This document also provides an opportunity for you to alter the parameters of a normal distribution and observe the resulting graphs. Try out a few real life examples to see the graphs of their distributions! For example, according to Statology, diastolic blood pressure for men is normally distributed with a mean of 80 mmHg and a standard deviation of 20 mmHg.

Next, the Normal Distribution: Empirical Rule document introduces the empirical rule, also referred to as the 68-95-99.7 rule, which describes approximately what percentage of normally distributed data lies within one, two, and three standard deviations of the distribution’s mean.

The empirical rule is frequently used to assess whether a set of data might fit a normal distribution, so Maple Learn also provides a Model Checking Exploration to help you familiarize yourself with applications of this rule. 

In this exploration, you will work through a series of questions about various statistics from the data – the mean, standard deviation, and specific intervals – before you are asked to decide if the data could have come from a normal distribution. Throughout this investigation, you will use the intuition built from exploring the Normal Distribution: Overview and Normal Distribution: Empirical Rule documents as you analyze different data sets.

Once you are confident in using the empirical rule and working with normal distributions, you can conduct your own model checking investigations in real life. Perhaps a set of quiz grades or the weights of apples available at a grocery store might follow a normal distribution – it’s up to you to find out!

On November 11^th, Canada and other Commonwealth member states will celebrate Remembrance Day, also known as Armistice Day. This holiday commemorates the armistice signed by Germany and the Entente Powers in Compiègne, France on November 11, 1918, to end the hostilities on the Western Front of World War I. The armistice came into effect at 11:00 am that morning – the “eleventh hour of the eleventh day of the eleventh month”. 

Similar to how November 11^th – which can be written as 11/11 – is a palindromic date that reads the same forward and backward, last year there was “Twosday” – February 22, 2022, also written 22/2/22. 

Palindromic dates like November 11^th that consist only of a day and a month happen every year, but how long will we have to wait until the next “Twosday”? We can use Maple Learn’s new Calendar Calculator to find out!

To use this document, simply input two dates and press ‘Calculate’ to find the amount of time between them, presented in a variety of units. For example, here are the results for the number of days left until Christmas from November 11^th of this year:

If we return to our original question, which concerns how long we’ll have to wait until the next “Twosday”, we can use this document to find our answer:

You can use this document as a countdown to find out how much time is left until your favorite holiday, your next birthday, or the time between now and any past or future date; try out the countdown document here!

Many everyday decisions are made using the results of coin flips and die rolls, or of similar probabilistic events. Though we would like to assume that a fair coin is being used to decide who takes the trash out or if our favorite soccer team takes possession of the ball first, it is impossible to know if the coin is weighted from a single trial.

Instead, we can perform an experiment like the one outlined in Hypothesis Testing: Doctored Coin. This is a walkthrough document for testing if a coin is fair, or if it has been doctored to favor a certain outcome. 

This hypothesis testing document comes from Maple Learn’s new Estimating collection, which contains several documents, authored by Michael Barnett, that help build an understanding of how to estimate the probability of an event occurring, even when the true probability is unknown.

One of the activities in this collection is the Likelihood Functions - Experiment document, which builds an intuitive understanding of likelihood functions. This document provides sets of observed data from binomial distributions and asks that you guess the probability of success associated with the random variable, giving feedback based on your answer. 

Once you’ve developed an understanding of likelihood functions, the next step in determining if a coin is biased is the Maximum Likelihood Estimate Example – Coin Flip activity. In this document, you can run as many randomized trials of coin flips as you like and see how the maximum likelihood estimate, or MLE, changes, bearing in mind that if a coin is fair, the probability of either heads or tails should be 0.5. 

Finally, in order to determine in earnest if a coin has been doctored to favor one side over the other, a hypothesis test must be performed. This is a process in which you test any data that you have against the null hypothesis that the coin is fair and determine the p-value of your data, which will help you form your conclusion.

This Hypothesis Testing: Doctored Coin document is a walkthrough of a hypothesis test for a potentially biased coin. You can run a number of trials on this coin, determine the null and alternative hypotheses of your test, and find the test statistic for your data, all using your understanding of the concepts of likelihood functions and MLEs. The document will then guide you through the process of determining your p-value and what this means for your conclusion.

So if you’re having suspicions that a coin is biased or that a die is weighted, check out Maple Learn’s Estimating collection and its activities to help with your investigation!

Probability distributions can be used to predict many things in life: how likely you are to wait more than 15 minutes at a bus stop, the probability that a certain number of credit card transactions are fraudulent, how likely it is for your favorite sports team to win at least three games in a row, and many more. 

Different situations call for different probability distributions. For instance, probability distributions can be divided into two main categories – those defined by discrete random variables and those defined by continuous random variables. Discrete probability distributions describe random variables that can only take on countable numbers of values, while continuous probability distributions are for random variables that take values from continuums, such as the real number line.

Maple Learn’s Probability Distributions section provides introductions, examples, and simulations for a variety of discrete and continuous probability distributions and how they can be used in real life. 

One of the distributions highlighted in Maple Learn’s Example Gallery is the binomial distribution. The binomial distribution is a discrete probability distribution that models the number of n Bernoulli trials that will end in a success.

This distribution is used in many real-life scenarios, including the fraudulent credit card transactions scenario mentioned earlier. All the information needed to apply this distribution is the number of trials, n, and the probability of success, p. A common usage of the binomial distribution is to find the probability that, for a recently produced batch of products, the number that are defective crosses a certain threshold; if the probability of having too many defective products is high enough, a company may decide to test each product individually rather than spot-checking, or they may decide to toss the entire batch altogether.

An interesting feature of the binomial distribution is that it can be approximated using a different type of distribution. If the number of trials, n, is large enough and the probability of success, p, is small enough, a Poisson Approximation to the Binomial Distribution can be applied to avoid potentially complex calculations. 

To see some binomial distribution calculations in action and how accurate the probabilities supplied by the distribution are, try out the Binomial Distribution Simulation document and see how the Law of Large Numbers relates to your results.