## How to Find Expected Value

Greeting math friends! Today, we are going to dive into statistics by learning how to find the expected value of a discrete random variable. To do this we will need to know all potential numeric outcomes of a “gamble,” as well as be able to repeat the gamble as many time as we want under the same conditions, without knowing what the outcome will be. But I’m getting ahead of myself, all of this will be explained below with two different examples step by step! Don’t forget to check out the video and practice questions at the end of this post to check your understanding. Happy calculating! 🙂

## What is Expected Value?

Expected Value is the weighted average of all possible outcomes of one “game” or “gamble” based on the respective probabilities of each potential outcome.

Expected Value Formula: Don’t freak out because below is the expected value formula.

In essence, we are multiplying each outcome value by the probability of the outcome occurring, and then adding all possibilities together!  Since we are summing all outcome values times their own probabilities, we can re-write the formula in summation notation:

Does the above formula look insane to you?  Don’t worry because we will go over two examples below that will hopefully clear things up! Let check them out:

## Example #1: Expected Value of Flipping a Coin

Step 1:  First let’s write out all the possible outcomes and related probabilities for flipping a fair coin and playing this game.  Making the below table, maps out our Probability Distribution of playing this game.

Step 2: Now that, we have written out all numeric outcomes and the probability of each occurring, we can fill in our formula and find the Expected Value of playing this game:

Ready for another?  Let’s see what happens in the next example when rolling a die.

## Example #2: Expected Value of Rolling a Die

Step 1:  First let’s write out all the possible outcomes and related probabilities for rolling a die. In this question, we are assuming that each side of the die takes on its numerical value, meaning rolling a 5 or a 6 is worth more than rolling a 1 or 2.  Making the below table, maps out our Probability Distribution of rolling the die.

Step 2: Now that, we have written out all numeric outcomes and the probability of each occurring, we can fill in our formula and find the Expected Value of playing this game:

Check out the practice problems below to master your expected value skills!

## Practice Questions:

(1) An unfair coin where the probability of getting heads is .4 and the probability of getting tails is .6 is flipped.  In a game where you win \$10 on heads, and lose \$10 on tails, what is the expected value of playing this game?

(2) An unfair coin where the probability of getting heads is .4 and the probability of getting tails is .6 is flipped.  In a game where you win \$30 on heads, and lose \$50 on tails, what is the expected value of playing this game?

## Solutions:

Still got questions? No problem! Don’t hesitate to comment with any questions below. Thanks for stopping by and happy calculating! 🙂

Looking for something similar to Expected Value? Check out the statistics page here!

## Olympics Statistics: Top 10 Medals by Country

Greetings and welcome back to MathSux! This week, in honor of the Tokyo Olympics, I will be breaking down some Olympic Statistics. We will look at the top 10 countries that hold the most medals and then look at the top 10 medals earned by country in relation to each country’s total population. Let’s take a look and see what we find! Also, please note that all data used for this analysis was found on the website, here. Anyone else watching the Olympics? Try downloading the data with the link above and see what type of conclusions you can find! Happy Calculating! 🙂

## Top 10 Countries: Total Olympic Medals

Below shows the top 10 total medals earned by country from the beginning of the Olympics in 1896 to present day July 2021. As we can see in the graph below, the United States is way ahead of the game with thousands more Olympic medals when compared to any other country in the entire world! I always knew the U.S. did well in the Olympics, but did not realize it was to this magnitude!

## Top 10 Countries: Total Olympic Medals Based on Population

Below is a different kind of graph. This percentage rate represents total medals earned over time from 1896 to July 2021 divided by the country’s total population. In this case, we can see that Lichtenstein has earned way more medals based on their small population size when compared to any other country in the world! This is amazing and unexpected!

Remember that all data for the above graphs were made from the following website, here. Are you surprised by the above graphs and conclusions? Try downloading the data on your own and see what you can conclude using your own Olympics Statistics skills! Happy calculating! 🙂

Looking to apply more math to the real world? Check out how to find volume of the Hudson Yards Vessal in NYC here

## NumWorks Calculator Review

Greeting math friends and welcome to MathSux! In today’s post we are going to review and take a look at how to use the graphing calculator available by the French company, NumWorks.

In this NumWorks calculator review, first impressions are that this is a serious competitor for Texas Instruments and offers more features than a typical calculator with a focus on statistics, data analysis, and even computer programming! Check out the video below to see the un-boxing, full review, and how to use this calculator step by step. Happy calculating! 🙂

## NumWorks Calculator Stand Out Features:

1) The Home Screen: Works and looks like apps on an iPhone. It is super easy to use, and includes apps such as the regular graphing calculator we’re all used to, as well as, Python, Statistics, Probability, Equation Solver, Sequences, and Regression.

2) The Equation Solver: Punch in any function and find it’s x-values and discriminant! Very cool!

3) Python: Yes, this calculator is programmable via Python! It also includes pre-made scripts that you can easily run. This is great for aspiring programmers and important for today’s economy.

4) Exam Mode: Teachers can make students put their calculators in exam mode and watch their students calculators light up in red to prove there’s no cheating funny business going on! Warning though, this will delete all of your data including the pre-made Python scripts. But you can always hit the reset button in the back to reset.

Did I mention math teacher’s can potentially get a free calculator from NumWorks? Check out the link here!

Has anyone else tried this graphing calculator from NumWorks? What were your first thoughts? Let me know in the comments and happy calculating!

For more math resources, check out this post here and happy calculating! 🙂

## How to Calculate Z-Score?: Statistics

Hi there and welcome to MathSux! In this post, we are going to explore how to calculate z-score and the normal distribution. We’ll do this by examining the normal curve and learning how to find probability finding z-score and using the mean, standard deviation, and specific data points. Fore more info and more MathSix don’t forget to check out the video and practice questions below. Happy calculating!

## What is a Normal Curve?

A normal curve is a bell shaped curve that shows the distribution of data evenly spread with respect to the mean. If you look at the normal curve below, the area under the curve shows all the possible probabilities of a certain data point occurring, notice the curve is higher towards the center mean, μ, and gets smaller as the distance from μ grows. The distance from μ is measured by the standard deviation, a unique unit of measurement that is specific to each group of data.

Mean: The mean always falls directly in the center of our normal curve. It is the average of our data, and always falls right in the middle.

Standard Deviation:  This value is used as a standard unit of measurement for the data, measuring the distance between each data point in relation to the mean throughout the entire data set. For a review on what standard deviation is and how to calculate it, check out this post here.

Now for our normal curve:

Notice half of the data is below the mean, μ, while the other half is above? The normal curve is symmetrical about the mean, μ!

## How to Calculate Z-Score?

Z Score can tell us at what percentile a certain point in the data set falls in relation to the rest of the mean by using the standard deviation as a unit of measurement.  If this sounds confusing, it’s ok! Take a look at the following formula:

We use the above formula in conjunction with  a z table which tells us the probability under the curve for a certain point.

## a) What percent of student scored below 500?

Step 1: First, let’s draw out our given information the mean=500, standard deviation=100, and the data point the question is asking for x=500 onto a normal curve. Notice that we want to find the value of the area under the curve shaded in pink.  This will tell us the percent of students that scored below 500.

Step 2: We need to find the z-score by, using the data point given to us x=500, the mean=500, and the standard deviation, sigma=100.

Step 3: Yes, we have a zero! Now we need to take our z table and line up our chart. Notice that the chart finds the probability for everything at the beginning of the normal curve and on.  This is perfect for answering our question!

Step 4: The table gives us our solution of .5000.  If we multiply .5000 times 100 it gives us the percent of students who scored below 500 at 50%.

## b) What percent of student scored above 620?

Step 1: First, let’s draw out our given information the mean=500, standard deviation=100, and the data point the question is asking for x=620 onto a normal curve. Notice that we want to find the value of the area under the curve shaded in pink.  This will tell us the percent of students that scored above 620.

Step 2: We need to find the z-score by, using the data point given to us x=620, the mean=500, and the standard deviation, sigma=100.

Step 3: Yes, we got 1.2! Now we need to take our z table and line up our chart. Notice that the chart finds the probability for everything at the beginning of the normal curve and on.  This is means to find the percent we are looking for, we need to subtract our answer from one since we want the value of probability on the right side of the curve (the z-table only provides the left side).

Step 4: The table gives us our solution of .8849.  If we subtract this value from 1 then multiply that value times 100 it gives us the percent of students who scored above 620.

## C) What is the highest score a student could receive if the students was in the 16.11th percentile?

Step 1: In this question we have to work backwards by first identifying, where on the z-score table is the number .1611 and then filling in our z score formula to find x, the missing data point (in this case test score).

Search the table for .1611:

Notice that .1611 can be found on the z-table above with z-score -0.99.  This is what we’ll use to find the unknown data point!

Step 2: We need to find the unknown test score by, using the z score we just found z=-0.99, the mean=500, and the standard deviation, sigma=100.

Step 3: Solve for x.

## Practice Questions:

The grades on a final English exam are normally distributed with a mean of 75 and a standard deviation of 10.

a) What percent of students scored below a 60?

b) What percent of students scored above an 89?

c) What is the highest possible grade that included in the 4.46th percentile?

d) What percent of students got at least a 77?

## Solutions:

a) 6.68%

b) 8.08%

c) 58

d) 42.07%

Want to make math suck just a little bit less? Subscribe to my Youtube channel for free math videos every week! 🙂

## Looking for more Statistics?

Difference between Bar Graphs and Histograms

Box and Whisker Plots

Variance and Standard Deviation

Expected Value

## Variance and Standard Deviation: Statistics

Greetings math friends! In this post, we arere going to go over the formulas for Variance and Standard Deviation. We will take this step by step by explaining the significance of the variance and standard deviation formulas in relation to a set of data. Get your calculators ready because this step by step although not hard, will take some serious number crunching! Also, don’t forget to check out the video on standard deviation and variance below to see how to check your work using a calculator. Happy calculating! 🙂

If you’re looking for related formulas, Mean Absolute Deviation (MAD) and Expected Value, scroll to the bottom of this post! And if you’re interested we’ll also touch upon the difference between population variance and sample variance later in this post.

## What is the Variance?

The variance represents the spread of data or the distance each data point is from the mean.  When we have multiple observations in our data, we want to know how far each unit of data is from the mean.  Are all the data points close together or spread far apart? What is the probability distribution? This is what the variance will help tell us!

Don’t freak out but here’s the formula for variance, notated as using the greek letter, sigma squared, σ2:

where…

xi= Value of Data Point

μ= mean

n=Total Number of Data Points]

(xi-μ)=Distance each data point is to the mean

In plain English, this translates to:

Let’s try an example to find the standard deviation and variance of the data set below.

Step 1: First, let’s find the mean, μ.

Step 2: Now that we have the mean, we are going to do each part of our formula one step at a time in the table below.

Notice we subtract each test score from the mean, μ=78. Then we square the result of each subtracted test score to get the squared deviation of each data value, then finally sum all the squared results together.

Step 3: Now that we summed all of our squared deviations, to get 730, we can fill this in as our numerator in the variance formula. We also know our denominator is equal to 5 because that is the total number of test scores in our data set.

## What is Standard Deviation?

Standard deviation is a unit of measurement that is unique to each data set and is used to measure the spread of data. The standard deviation formula happens to be very similar to the variance formula!

Below is the formula for standard deviation, notated as sigma, the greek letter, σ:

Since this is the same exact formula as variance with a square root, all we need to do is take the square root of the variance to find standard deviation:

## Sample VS. Population

What is the difference between a sample vs. a population?

A population in statistics refers to an entire data set that at times can be humanly incapable of reaching.

For example: If we wanted to know the average income of everyone who lives in New York State, it would be almost impossible to reach every working person and ask them how much they make for a living.

To make up for the impossibility of data collection, we usually only survey a sample of the entire population to get income levels of let’s say 10,000 people across New York State, a much more reasonable in terms of data collecting!

And taking this sample size from the entire working population of New York State provides us with a sample mean, a sample variance, and a sample standard deviation.

On the other hand, if we were able to ask every student in a school what their grade point average was and get an answer, this would be an example of a whole population. Using this information, we would be able to find the population mean, population variance, and population standard deviation.

Sample notation also differs from population notation, but don’t worry about these too much, because the formulas and meanings remain the same. For example, the population mean is represented by the greek letter, μ, but the sample mean is represented by x bar.

Now try calculating the standard deviation of each data set below on your own with the following practice problems!

## Other Related Formulas

The Mean Absolute Deviation otherwise known as MAD is another formula related to variance and standard deviation. In the MAD formula above, notice we are doing very similar steps, by finding the distance to the mean of each data point, only this time we are taking the aboslute value of the ditsance to the mean. Then we sum all the absolute value distances together and divide by the total number of data points.

Why do we use aboslute value in this formula? We take the absolut value, because if didn’t the distance to the means summed togther would cancel eachother out to get zero!

Where…

X = Data point value

μ = mean

N=Total number of data points

|X-μ|=absolute deviation

If we were to take the sample from our example earlier,60, 85, 95, 70, 80, in this post and find the MAD it would go something like this:

## Expected Value:

Expected Value is the weighted average of all possible outcomes of one “game” or “gamble” based on the respective probabilities of each potential outcome for a discrete random variable. A “gamble” is defined by the following rules: 1) All possible outcomes are known 2) An outcome cannot be predicted 3) All possible outcomes are of numeric value and 4) The Game can be repeated multiple times under the same conditions.

Still got questions? No problem! Don’t hesitate to comment with any questions or check out the video above. Happy calculating! 🙂

Also! If you’re looking for more statistics, check out this post on how to create and analyze box and whisker plots here!