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Kruskal's Proof of the Joint Distribution of the Sample Mean and Variance
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Classroom Contents
Statistical Theory
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- 1 Statistical Theory: Sum of Squared Normal mean=mu var=1 variables
- 2 "Best" predictors of Y using a function of X.
- 3 Alternative Formula for the Expected Value
- 4 Incomplete Beta Function as the Sum of Binomial Probabilities
- 5 CI for Population Median using Order Statistics
- 6 Discrete Order Statistics with Illustration using R
- 7 Sum of Poisson Probabilities equal a Chi-square Probability
- 8 Using R to Find an Exact CI for a Poisson Parameter
- 9 The Median Minimizes Absolute Loss. 3 proofs when X is continuous.
- 10 Markov Inequality. Chebyshev Inequality. Weak Law of Large Numbers.
- 11 Proof of Binomial Theorem with specific cases of the General Binomial Theorem
- 12 Big O, Little o Notation. Examples with Cumulant and Moment Generating Functions
- 13 Proof of Holm Bonferroni Correction Method
- 14 Proof of Simes Correction Method
- 15 2 formulas between the determinant, trace and eigen values of a matrix
- 16 Properties of the Gamma Function (part 1 of 2)
- 17 Properties of the Gamma Function (part 2 of 2)
- 18 Chi square approximation to an F Distribution
- 19 Asymptotic C I for the Difference of 2 Independent Population Means
- 20 Exact C I for the difference of 2 independent normal population means
- 21 1st 4 moments of the sample mean when x is a Bernoulli random variable
- 22 A df=1 noncentral chi sq distribution as a Poisson weighted mixture of central chi sq distributions
- 23 Using R: Calculating Probability for a Bivariate Normal Random Variable
- 24 Statistical Distance
- 25 Extended Cauchy-Schwarz Inequality
- 26 Rotational Invariance
- 27 Generating Double Exponential Data from Scratch
- 28 Kruskal's Proof of the Joint Distribution of the Sample Mean and Variance
- 29 Derive the CDF of an Inverse Gamma Distribution
