gamma distribution

Continuous data
In previous lectures we have considered discrete
datasets and discrete probability distributions. In practice
many datasets that we collect from experiments
consist of continuous measurements.
So we need to study probability models for continuousContinuous data
In previous lectures we have considered discrete
datasets and discrete probability distributions. In practice
many datasets that we collect from experiments
consist of continuous measurements.
So we need to study probability models for continuous
data.Continuous data
In previous lectures we have considered discrete
datasets and discrete probability distributions. In practice
many datasets that we collect from experiments
consist of continuous measurements.
So we need to study probability models for continuous
data.Continuous probability distributions
When we considered the Binomial and Poisson distributions
we saw that the probability distributions were
characterized by a formula for the probability of each
possible discrete value.
All of the probabilities together sum up to 1.
We can visualize the density by plotting the probabilities
against the discrete values.
data.Continuous data
In previous lectures we have considered discrete
datasets and discrete probability distributions. In practice
many datasets that we collect from experiments
consist of continuous measurements.
So we need to study probability models for continuousContinuous data
In previous lectures we have considered discrete
datasets and discrete probability distributions. In practice
many datasets that we collect from experiments
consist of continuous measurements.
So we need to study probability models for continuous
data.Continuous data
In previous lectures we have considered discrete
datasets and discrete probability distributions. In practice
many datasets that we collect from experiments
consist of continuous measurements.
So we need to study probability models for continuous
data.Continuous probability distributions
When we considered the Binomial and Poisson distributions
we saw that the probability distributions were
characterized by a formula for the probability of each
possible discrete value.
All of the probabilities together sum up to 1.
We can visualize the density by plotting the probabilities
against the discrete values.
data.