1 Reminders from 1-dimensional calculus
... and fy (x0 , y0 ). However, example 4 shows that the converse does not hold. In other words, the existence of both partial derivatives does not guarantee differentiability. Morally, that is because limits in several variables are more complicated than limits in one variable. Here is an even worse ex ...
... and fy (x0 , y0 ). However, example 4 shows that the converse does not hold. In other words, the existence of both partial derivatives does not guarantee differentiability. Morally, that is because limits in several variables are more complicated than limits in one variable. Here is an even worse ex ...
- Catalyst
... Write a Monte Carlo Simulation of this problem, by looping over T = 1000 independent trials. Show that your result agrees with the theory. Extend your simulation to estimate the probability that two or more birthdays either match or are adjacent. This is a common problem in uncoordinated access to a ...
... Write a Monte Carlo Simulation of this problem, by looping over T = 1000 independent trials. Show that your result agrees with the theory. Extend your simulation to estimate the probability that two or more birthdays either match or are adjacent. This is a common problem in uncoordinated access to a ...
Chapter 6: The Normal Model
... In a Normal Probability Plot, the actual data values are plotted on one axis and each value’s theoretical z-score is plotted on the other axis. For example, in a data set with 100 values from a normal distribution, the smallest value should have a z-score of -2.58. If a data set closely follows a no ...
... In a Normal Probability Plot, the actual data values are plotted on one axis and each value’s theoretical z-score is plotted on the other axis. For example, in a data set with 100 values from a normal distribution, the smallest value should have a z-score of -2.58. If a data set closely follows a no ...
171SB2_tut4_08
... You are required to hand-in your answers to questions 1 – 6. 1. Let X be a continuous random variable whose distribution is Uniform(0, 1). Calculate the probabilities: P(0.7 X < 0.9); P(0.35 < X < 0.55). Comment on your answers. 2. Let X be a continuous random variable whose distribution is Exp(2) ...
... You are required to hand-in your answers to questions 1 – 6. 1. Let X be a continuous random variable whose distribution is Uniform(0, 1). Calculate the probabilities: P(0.7 X < 0.9); P(0.35 < X < 0.55). Comment on your answers. 2. Let X be a continuous random variable whose distribution is Exp(2) ...
lecture
... • Can sometimes quantize real variables to make them discrete – E.g., age, height, distance ...
... • Can sometimes quantize real variables to make them discrete – E.g., age, height, distance ...