Review of the important concepts of previous section; moment generation function; Gamma distribution, chi-square distribution.
A series of notes that I learn Emacs hacking. Parenthesis highlighting, web-mode for web development, js2-refactor, occur-mode, imenu-mode, expand-region, iedit-mode, org-mode export.
A series of notes that I learn Emacs hacking. Package loading mechanism, auto-indentation, hippie-expand, dired-mode, basic about org-mode.
Proof of the biased sample mean; Example 3.5.3 in the text book; Normal distribution, joint normal distribution (multivariate), Gamma distribution.
A series of notes that I learn Emacs hacking. Splitting the `init.el` file, enabling auto revert mode, package loading mechanism overview, enable abbreivation mode, about popwin package, more about major and minor mode in Eamcs, function and variable naming convention, counsel-git to find files.
A series of notes that I learn Emacs hacking. Basic key mapping and functions; Emacs start up settings; introduction to "modes", company-mode; basic of package management; introduction to org-mode.
Survival function and its example; transformation of random variables, scale and rate parameters and their examples; Joint probability density and its example; independent continuous random variables.
If the PDF is symmetric about c, show that E(X)=c. This is a homework problem for course STT802-002 Theory of Probabilities and Statistics I in MSU.
Hypergeometric distribution; Poisson Law; Brownian motion; continous random variables, exponential distribution, cumulative distribution function, uniform distribution; expectation and variance of continuous random variable.
Sample mean and sample variance, biased and unbiased estimation; covariance, Hypergeometric distribution and its example; correlation coefficients; discrete distribution, Poisson distribution, Poisson approximation for the Binomial distribution.
Expectation and its theorems, Geometry distribution, Negative Binomial distribution and their examples; Theorem of linerarity, PMF of a pair of random variables, Markov's inequality; variance and its examples, uniform distribution.
Random variable, independent random variable and their examples; Bernoulli distribution, Binomial distribution.
Some of the basic probability and statistics concepts. joint probabilities, combinatories; conditional probabilities and independence and their examples; Bayes' rule.
This is the lecture note that I took on the lecture. This post serve as the overview of this series.