Derive the moments of a Standard Normal Distribution
5
Mean and Variance of Truncated Exponential Density
6
Mean and Variance of Truncated Normal Density
7
Mean and Variance of a Poisson Distribution
8
Interesting Formula for the Sample Variance. U Statistics
9
Mean and Variance of a Gamma Distribution
10
Mean and Variance of a Discrete Uniform Distribution
11
Mean and Variance of a Beta Distribution
12
Mean and Variance of a Truncated Poisson Distribution
13
Mean and Variance of a Cauchy Distribution
14
Mean and Variance of a Log Normal Distribution
15
Mean and Variance of a t Distribution
16
Mean and Variance of an f Distribution
17
Mode of a Binomial Distribution
18
Mode of a Poisson Distribution
19
Correlation of a Bivariate Discrete Distribution
20
Moments of a Cauchy Distribution that are Finite
21
LOTUS - Law of the Unconcious Statistician
22
Mean, Variance, and CDF of Arcsine Distribution
23
Mean and Variance Hypergeometric Distribution
24
Mean and Variance of a Folded Normal Distribution
25
Mean and Variance of a Compound Poisson Gamma Distribution
26
Mean and Variance of a Double Exponential (Laplace) Distribution
27
Mean, Variance, Median, and Mode of a Weibull Distribution
28
Mean and Variance of a Beta Prime Distribution
29
Mean and Variance of a General Beta Distribution
30
Mean and Variance of a Chi Distribution
31
Mean and Variance of a Truncated Cauchy Distribution
32
Exponential Family: Mean and Variance
33
Exponential Family: Normal Distribution
34
Exponential Family: Bernoulli Distribution
35
Exponential Family: Binomial Distribution (fixed n)
36
Exponential Family: Poisson Distribution
37
Exponential Family: Negative Binomial Distribution (fixed r)
38
Exponential Family: Exponential Distribution
39
Exponential Family: Pareto Distribution (known minimum)
40
Exponential Family: Weibull Distribution (known k)
41
Exponential Family: Laplace Distribution (known mean)
42
Exponential Family: Chi square Distribution
43
Exponential Family: Gamma Distribution
44
Exponential Family: Log Normal Distribution
45
Exponential Family: Beta Distribution
46
Exponential Family: Multinomial Distribution (fixed n)
47
Moments of a Normal Distribution
48
Mean and Variance of a Non Central Chi square Distribution
49
Mean, Variance, and Covariance of Quadratic Forms
50
Mean, Variance, MGF, & CDF of a Gumbel Distribution
51
Mean and Variance of a Beta Negative Binomial Distribution
52
Marginal Distribution of a Poisson Gamma Mixture Distribution
53
Method of Moments Estimation for a Beta Distribution (part 1)
54
Method of Moments and MLEs for a Beta Distribution (part 2)
55
Mean, Variance, and Covariance of a Dirichlet Distribution
56
Mean and Variance of a Zero-Truncated Binomial Distribution
57
Derive the MGF of a Logistic Distribution and use it to derive the Mean and Variance
58
Find E(xlogx) for a Gamma Distribution
59
Mean and Variance of an Inverse Gamma Distribution
60
Method of Moments Estimators for an Inverse Gamma Distribution
61
Mode of an F Distribution is less than One
62
Mean and Variance Negative Binomial Distribution
63
Mode of an Inverse Gamma Distribution
64
Mean, Variance and Mode of a Half Normal Distribution
65
Mean, Variance, and Method of Moments for a Two Parameter Lindley Distribution
66
Mean, Variance, Median, and CDF of a Rayleigh Distribution
67
CDF and Hazard Function for a Two Parameter Lindley Distribution
68
Mean and Variance of a Beta Binomial Random Variable
69
Method of Moments Estimation Beta Binomial Distribution
Description:
Explore a comprehensive 9-hour course on statistical distributions, covering mean, variance, moments, and mode. Delve into various probability distributions, including Binomial, Normal, Poisson, Gamma, Beta, and more. Learn to derive moments, calculate means and variances, and understand the properties of different distributions. Study the Exponential Family, Method of Moments estimation, and specialized topics like truncated distributions and compound distributions. Gain practical skills in analyzing and interpreting statistical data through in-depth examinations of distribution characteristics and their applications in real-world scenarios.