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| − | In Lecture 21, | + | In Lecture 21, we continued our discussion of normally distributed random variables, still focusing on the one-dimensional case. In particular, we showed how to compute the expectation and the variance of a normally distributed random variable: with the two tricks we introduced, the computations turned out to be quite reasonable. We also considered the effect of a linear transformation on a normally distributed random variable: it was observed that the resulting random variable Y=aX+b is also normally distributed. The relation between the mean and variance of X and that of Y=aX+b was given. However, note that we had already seen that relation when we looked at the expectation and variance of aX+b in general. |
Latest revision as of 07:21, 27 February 2013
Lecture 21 Blog, ECE302 Spring 2013, Prof. Boutin
Monday February 25, 2013 (Week 8) - See Course Outline.
(Other blogs 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
In Lecture 21, we continued our discussion of normally distributed random variables, still focusing on the one-dimensional case. In particular, we showed how to compute the expectation and the variance of a normally distributed random variable: with the two tricks we introduced, the computations turned out to be quite reasonable. We also considered the effect of a linear transformation on a normally distributed random variable: it was observed that the resulting random variable Y=aX+b is also normally distributed. The relation between the mean and variance of X and that of Y=aX+b was given. However, note that we had already seen that relation when we looked at the expectation and variance of aX+b in general.
Action items for students (to be completed before next lecture)
- Read Section 5.1 in the textbook.
- Solve the following practice problems and consider sharing your answers for discussion and feedback. (You will hand in your solution as part of homework 5.)
Previous: Lecture 20
Next: Lecture 22
