Filming Location
127A Smithfield Road, Frederiksted, Virgin Islands
Statistics
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Description
The TV series Statistics, created by an unlisted creator and produced by an unlisted studio, first premiered on May 12, 2026 in the United Kingdom. The series spans 1 season with 44 episodes, featuring the voices of an unlisted voice cast. It follows the story of In 44 episodes, Adriene Hill teaches you Statistics! This course is based on the 2018 AP Statistics curriculum and introduces everything from basic descriptive statistics to data collection to hot topics in data analysis like Big Data and neural networks. By the end of the course, you will be able to: *Identify questions that can be answered using statistics *Describe patterns, trends, associations, and relationships in data both numerically and graphically *Justify a conclusion using evidence from data, definitions, or statistical inference *Apply statistical models to make inferences and predictions from data sets *Understand how statistics are used broadly in the world and interpret their meaning, like in newspapers or scientific studies. This is an Ai series and has received a rating of 0/10 from 0 viewers.
Where to Watch
ugc-edu.comMannu khadka
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Production
Castle Rock Entertainment
Award
21 wins & 43 nominations total
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User Review
We've talked a lot about modeling data and making inferences about it, but today we're going to look towards the future at how machine learning is being used to build models to predict future outcomes. We'll discuss three popular types of supervised machine learning models: Logistic Regression, Linear discriminant Analysis (or LDA) and K Nearest Neighbors (or KNN). For a broader overview of machine learning, check out our episode in Crash Course Computer Science! https://www.youtube.com/watch?v=z-EtmaFJieY Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled
We've talked a lot about modeling data and making inferences about it, but today we're going to look towards the future at how machine learning is being used to build models to predict future outcomes. We'll discuss three popular types of supervised machine learning models: Logistic Regression, Linear discriminant Analysis (or LDA) and K Nearest Neighbors (or KNN). For a broader overview of machine learning, check out our episode in Crash Course Computer Science! https://www.youtube.com/watch?v=z-EtmaFJieY Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled
We've talked a lot about modeling data and making inferences about it, but today we're going to look towards the future at how machine learning is being used to build models to predict future outcomes. We'll discuss three popular types of supervised machine learning models: Logistic Regression, Linear discriminant Analysis (or LDA) and K Nearest Neighbors (or KNN). For a broader overview of machine learning, check out our episode in Crash Course Computer Science! https://www.youtube.com/watch?v=z-EtmaFJieY Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark Brouwer, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Brian Thomas Gossett, Khaled
In our series finale, we're going to take a look at some of the times we've used statistics to gaze into our crystal ball, and actually got it right! We'll talk about how stores know what we want to buy (which can sometimes be a good thing), how baseball was changed forever when Paul DePodesta created a record-winning Oakland A's baseball team, and how statistics keeps us safe with the incredible strides we've made in weather forecasting. Statistics are everywhere, and even if you don't remember all the formulae and graphs we've thrown at you in this series, we hope you take with you a better appreciation of the many ways statistics impacts your life, and hopefully we've given your a more math-y perspective on how the world works. Thanks so much for watching DFTBAQ! Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for eve
In our series finale, we're going to take a look at some of the times we've used statistics to gaze into our crystal ball, and actually got it right! We'll talk about how stores know what we want to buy (which can sometimes be a good thing), how baseball was changed forever when Paul DePodesta created a record-winning Oakland A's baseball team, and how statistics keeps us safe with the incredible strides we've made in weather forecasting. Statistics are everywhere, and even if you don't remember all the formulae and graphs we've thrown at you in this series, we hope you take with you a better appreciation of the many ways statistics impacts your life, and hopefully we've given your a more math-y perspective on how the world works. Thanks so much for watching DFTBAQ! Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for eve
In our series finale, we're going to take a look at some of the times we've used statistics to gaze into our crystal ball, and actually got it right! We'll talk about how stores know what we want to buy (which can sometimes be a good thing), how baseball was changed forever when Paul DePodesta created a record-winning Oakland A's baseball team, and how statistics keeps us safe with the incredible strides we've made in weather forecasting. Statistics are everywhere, and even if you don't remember all the formulae and graphs we've thrown at you in this series, we hope you take with you a better appreciation of the many ways statistics impacts your life, and hopefully we've given your a more math-y perspective on how the world works. Thanks so much for watching DFTBAQ! Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for eve
In our series finale, we're going to take a look at some of the times we've used statistics to gaze into our crystal ball, and actually got it right! We'll talk about how stores know what we want to buy (which can sometimes be a good thing), how baseball was changed forever when Paul DePodesta created a record-winning Oakland A's baseball team, and how statistics keeps us safe with the incredible strides we've made in weather forecasting. Statistics are everywhere, and even if you don't remember all the formulae and graphs we've thrown at you in this series, we hope you take with you a better appreciation of the many ways statistics impacts your life, and hopefully we've given your a more math-y perspective on how the world works. Thanks so much for watching DFTBAQ! Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for eve
Today we’re going to talk about why many predictions fail - specifically we’ll take a look at the 2008 financial crisis, the 2016 U.S. presidential election, and earthquake prediction in general. From inaccurate or just too little data to biased models and polling errors, knowing when and why we make inaccurate predictions can help us make better ones in the future. And even knowing what we can’t predict can help us make better decisions too. Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Naman Goel, Patrick Wiener II, Nathan Catchings, Efrain R. Pedroza, Brandon Westmoreland, dorsey, Indika Siriwardena, James Hughes, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Kathrin B
Today we’re going to talk about why many predictions fail - specifically we’ll take a look at the 2008 financial crisis, the 2016 U.S. presidential election, and earthquake prediction in general. From inaccurate or just too little data to biased models and polling errors, knowing when and why we make inaccurate predictions can help us make better ones in the future. And even knowing what we can’t predict can help us make better decisions too. Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Naman Goel, Patrick Wiener II, Nathan Catchings, Efrain R. Pedroza, Brandon Westmoreland, dorsey, Indika Siriwardena, James Hughes, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Kathrin B
Today we’re going to talk about why many predictions fail - specifically we’ll take a look at the 2008 financial crisis, the 2016 U.S. presidential election, and earthquake prediction in general. From inaccurate or just too little data to biased models and polling errors, knowing when and why we make inaccurate predictions can help us make better ones in the future. And even knowing what we can’t predict can help us make better decisions too. Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Naman Goel, Patrick Wiener II, Nathan Catchings, Efrain R. Pedroza, Brandon Westmoreland, dorsey, Indika Siriwardena, James Hughes, Kenneth F Penttinen, Trevin Beattie, Satya Ridhima Parvathaneni, Erika & Alexa Saur, Glenn Elliott, Justin Zingsheim, Jessica Wode, Kathrin B
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