Download presentation
Presentation is loading. Please wait.
1
Vital Statistics Probability and Statistics for Economics and Business
William H. Sandholm University of Wisconsin-Madison Brett A. Saraniti Northwestern University Chapter 6 Continuous Random Variables and Distributions
2
Figure 6.1: A fair spinner.
3
Figure 6.2: Density functions.
4
Figure 6.3: The density function for the fair spinner.
5
Figure 6.4: spinner.xlsx/500_spins
6
Figure 6.5: spinner.xlsx/5000_spins
7
Figure 6.6: best_of_two.xlsx
8
Figure 6.7: The density of the random variable Y.
9
Figure 6.8: Interpreting f (x).
10
Figure 6.9: A uniform density.
11
Figure 6.10: Uniform probabilities.
12
Figure 6.11: Shifting and scaling of uniform random variables.
13
Figure 6.12: Some normal densities.
14
Figure 6.13: The standard normal density function.
15
Figure 6.14: P(Z ∈ [−b,−a]) = P(Z ∈ [a, b])
![Figure 6.14: P(Z ∈ [−b,−a]) = P(Z ∈ [a, b])](http://slideplayer.com/slide/16560351/96/images/15/Figure+6.14%3A+P%28Z+%E2%88%88+%5B%E2%88%92b%2C%E2%88%92a%5D%29+%3D+P%28Z+%E2%88%88+%5Ba%2C+b%5D%29.jpg "Figure 6.14: P(Z ∈ [−b,−a]) = P(Z ∈ [a, b])")
16
Figure 6.15: Some standard normal probabilities.
17
Figure 6.16: distributions.xlsx/standard_normal
18
Figure 6.17: Some N(600, 10,000) probabilities.
19
Figure 6.18: distributions.xlsx/normal
20
Table 6.1: The standard normal distribution table.
21
Figure 6.19: Calculating P(Z ∈ [−2,−1]).
![Figure 6.19: Calculating P(Z ∈ [−2,−1]).](http://slideplayer.com/slide/16560351/96/images/21/Figure+6.19%3A+Calculating+P%28Z+%E2%88%88+%5B%E2%88%922%2C%E2%88%921%5D%29..jpg "Figure 6.19: Calculating P(Z ∈ [−2,−1]).")
22
Figure 6.20: Calculating P(Z ≥ −1.34).
23
Figure 6.21: Calculating P(X ≥ 15) when X ∼ N(8, 16)
24
Figure 6.22: uniform_sum.xlsx
25
Figure 6.23: normal_sum.xlsx
26
Figure 6.24: Brownian_motion.xlsx
27
Figure 6.25: geometric_Brownian_motion.xlsx
Similar presentations
![E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.](/12/3428304/big_thumb.jpg "E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.>")
