“It’s hard to make predictions, especially about the future” – Various
Usually, improvement in prediction-making comes in two steps. Step 1: Make a Prediction. Step 2: Evaluate how accurate the prediction was, and learn from it. Often times, Step 2 can get overlooked as we move on to future predictions and future weeks of fantasy football. Spreadsheets can help us quickly evaluate how our predictions were, and quickly point out where we might have erred.
It’s that time of the year when Little League World Series coverage heats up on ESPN, and viewers get to see a miniaturized version of baseball played at the highest level. This year has been especially impressive with a super-team from Las Vegas that rarely makes errors, the Chicago team that beat them, and of course the sensational female pitcher Mo’ne Davis, who throws as hard as some high school pitchers. Every Little Leaguer dreams of making it to the big leagues, someday making a career of playing the game they love. But how hard is it to get there? This is a question that we will try to solve this week with the help of a spreadsheet.
Have you heard of Bayes’ Rule? Let’s use an intuitive example to understand an application of this rule. What’s the probability American Hustle wins the Golden Globes given it wins the Oscars? Continue reading →
With a Grand Slam approaching, let’s talk tennis! If we were to predict a tennis pro’s weight based on his height, where would we begin? How will our understanding of the best-fit line and spreadsheets help us make this prediction?
We’ve collected the heights and weights of tennis pros including Federer, Djokovic, Nadal, Murray, Azarenka, Sharapova, and Williams, along with another 192 top players. Let’s investigate how to 1) calculate the correlation of weights and heights, and 2) draw a best fit-line and scatter plot in Google spreadsheets to extrapolate or make predictions!
What is the probability of rolling any pair of numbers with two dice? Let’s first solve this and then confirm our calculated probability by simulating 500 dice rolls with a spreadsheet! In this post, we will focus on understanding basic probability concepts and then discover how with spreadsheets, we can actually see whether our calculated probability holds true!
We have a set of data and want to understand its characteristics. A great starting point is to measure the central or typical value and the dispersion around that value. In this post, we will focus on the latter – specifically a standard measure of spread known as the sample standard deviation!
The average value is very desirable in the world of statistics! Known as the central tendency, averages provide a way to understand the characteristics of a broad set of data. What are the different measures of central tendency? How can we calculate them? Let’s explore this below!
Is the “height success rate” for seeds grown in organic soil significantly higher than that for those grown in the non-organic soil? Let’s use a statistical test to find out! But before we delve in, let’s review the amazing Central Limit Theorem (CLT). Why so remarkable, you may ask? Continue reading →
What is the impact of organic soil on a seedling’s height? Will an organically-grown seedling be taller than one grown in non-organic soil? Armed with data, we’ll now tackle these questions by performing a statistical test in a spreadsheet!
Seeking science experiment ideas? Want to conduct an experiment? Curious about hypothesis testing? Understanding how to design and test an effective experiment is an essential skill. Let’s investigate this research question: What is the impact of organic soil on a seedling’s height?
Spreadsheets are a platform to test an experiment or conduct a research study. With spreadsheets, we can collect & organize data, compute summary statistics, and even test hypotheses!
So, let’s begin our experiment with spreadsheets, statistics, seeds, & soil! Continue reading →