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Not your average video:
Interactive Problems: Solve problems alongside the video with step-by-step guidance and detailed solutions.
Exam Preparation: Complete unit reviews designed for final exam preparation with all key concepts covered systematically.
Expert Teaching: High-quality instruction from Perplex co-founder James Mullen with clear explanations, worked examples, and exam tips.
Distributions & Random Variables
Watch comprehensive video reviews for Distributions & Random Variables, designed for final exam preparation. Each video includes integrated problems you can solve alongside detailed solutions.
Not your average video:
Interactive Problems: Solve problems alongside the video with step-by-step guidance and detailed solutions.
Exam Preparation: Complete unit reviews designed for final exam preparation with all key concepts covered systematically.
Expert Teaching: High-quality instruction from Perplex co-founder James Mullen with clear explanations, worked examples, and exam tips.
Not your average video:
Interactive Problems: Solve problems alongside the video with step-by-step guidance and detailed solutions.
Exam Preparation: Complete unit reviews designed for final exam preparation with all key concepts covered systematically.
Expert Teaching: High-quality instruction from Perplex co-founder James Mullen with clear explanations, worked examples, and exam tips.
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AHL 4.14
A continuous random variable X is a variable that can take any value in a specific range, eg 0<X<1.
Since there are infinitely many possible values, the probability of any specific value is 0. Instead, you have to consider the probability that the value of X will fall within some specific range.
This probability is the area under a curve:
The function f(x) is called the probability density function. Its values are not probabilities - since P(X=x)=0 - but instead an abstract measure of how "densely packed" the probability is around each point.
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AHL 4.14
A continuous random variable X is a variable that can take any value in a specific range, eg 0<X<1.
Since there are infinitely many possible values, the probability of any specific value is 0. Instead, you have to consider the probability that the value of X will fall within some specific range.
This probability is the area under a curve:
The function f(x) is called the probability density function. Its values are not probabilities - since P(X=x)=0 - but instead an abstract measure of how "densely packed" the probability is around each point.
Powered by Desmos