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$prove that limit n approches to infinity then, {gamma of (n + k)}/{ gamma of n } = n^k$

quick and healthy soyabean badi rolls #rolls for children tiffin #childrens favourite rolls #veg rol

Study Nutz

Study Nutz

quick and healthy soyabean badi rolls #rolls for children tiffin #childrens favourite rolls #veg rol

ghiya ki sabji

itna tasty daliya k bache roj maange #veg daliya #healthy and yummy daliya # namkeen daliya easy way

bacho ke tiffin k liye easiest and healthiest veg rolls #only 3 ingredients needed #soya badi rolls

fali aur aalu ki sbji #guwaar fali dish #faliyon ki sookhi sbzi #beans #guwaar fali sookhi sabzi

turai sabji #toriyon ki tari vali sbji #tori sabzi #chinese okra #tori dish #quick recipe 1 minute

probability theorem

definition of error in statistics and factors that causes error | error

sampling theory | objectives and limitations with examples | sample, sample size def | sample survey

law of statistical regularity

how to learn to write BCD code, ASCII -7 code , EBCDIC code in minutes

log normal distribution

Bivariate normal distribution

beta distribution of 2nd kind

mean and variance of beta distribution of 1st kind

bivariate normal distribution pdf

moment generating function of bivariate normal distribution

vandermondes identity

marginal distribution of Bivariate normal distribution

mean and variance of logistic distribution

mean and variance of f distribution

mean and variance of beta distribution of 2nd kind

mean and variance of gamma distribution

mean and variance of Laplace distribution

mean and and variance of exponential distribution

rth moment about origin of Lognormal distribution | mean, variance, moments of Lognormal distributon

lack of memory property of geometric distribution

relation between standard Cauchy distribution and students t distribution

summation of pmf of binomial distribution is equal to 1

summation of pmf of negative binomial distribution is equal to 1

proof of integration of pdf of normal distribution is equal to 1

proof of summation of pmf of hypergeometric distribution is equal to 1

proof of integral of pdf of gamma distribution is equal to 1

pdf, mean, variance of Pareto distribution | find u1', u2', u2, E(x) , E(x²) of Pareto distribution

conditional distributions of bivariate normal distribution | pdf of x for given y & pdf y for fix x

mean and variance of log normal distribution | E(x) , E(x²) , u² , u1', u2' , moments of log normal

statement and proof of Bernoulli's law of large numbers | Bernoulli law using Chebychev inequality

calculate probability density function of log normal distribution and standard log normal distributi

3 types of sampling | subjective, purposive, judgement sampling, probability sampling, mixed samplin

derivation of compound binomial distribution | compound distribution definition, probability functio

mean of sample mean, variance of sample mean, S.D, S.E | E(x bar) = u and variance ( x^—) = sigma²/n

principles of sampling | 5 important laws which form basis of sampling theory |statistical regularit

definition of sampling distribution of the statistic and standard error ( S.E ) of statistic | S.D

difference b/w parameter and statistic | symbols for parameters and statistics |definition ,examples

merits of sample method over census method | comparison /difference between sample and census method

rth moment about origin, mean, variance of two parameter Laplace distribution | ur', u1', u2', u2

mean and variance of Multinomial distribution | moments of Multinomial distribution | u1', u2', u2

pdf, characteristic and cumulant generating function ,cumulants, moments, B1, B2 of standard Laplace

Cumulant generating function, cumulants, moments about mean from cumulants of negative binomial dist

pmf, moment generating function, finding mean and variance from mgf of Multinomial distribution

recurrence formula for getting moments about mean of negative binomial distribution | ur+1 , u2 , u3

recurrence formula for finding probabilities or fitting of negative binomial distribution

Getting moments of Poisson distribution from moments of binomial distribution | u1, u2, u3, u4

find distribution of sample mean and variance of independent observations from normal population

statement and proof of weak law of large numbers

pdf , characteristic function of 2 parameter laplace | laplace with 2 parameter to standard Laplace

pdf, mean, variance, moments about origin of weibul distribution ,pdf of standard weibul distributio

power series distribution | p.m.f | mean, variance | special cases of p.s.d | m.g.f | u1', u2', u2

f distribution | p.d.f Derivation | mode | moments, mean, variance | relation b/w f and Chi-square

mean and variance of power series distribution | moments about mean and origin of power series distr

some special cases of power series distribution | binomial,Poisson,negative binomial,logarithmic dis

find moment generating function and mean, variance, moments from m.g.f of power series distribution

probability mass function of power series distribution

x1 and x2 are chi-square variates, then U= (x1+x2)/2 is beta distribution of 2nd kind and V= x1+x2

Cauchy distribution | pdf | mean,variance | additive property | characteristic function

Poisson distribution | pmf derivation,moments, mode, cummlant generating function,additive property

to find mean and variance of Cauchy distribution | moments of Cauchy distribution

find characteristic function of standard Cauchy variate and Cauchy variate having two parameters

Statment and proof of additive property of Cauchy distribution by using characteristic function

probability density function of standard Cauchy variate and cauchy variate having two parameters

hyper geometric distribution | moments, pmf, factorial moments, limiting form, recurrence relation

negative binomial distribution | mgf, moments, limiting form of negative binomial distribution

z distribution | deriving pdf ,moment generating function, cumulant generating function

geometric distribution | pmf,moments,probability generating function,mgf,mean,variance

moments of geometric distribution | mean, variance , moment about origin and about mean of geometric

moments of hyper geometric distribution | moments about origin, mean, variance of hyper geometric di

statement and proof of Chebyshev's inequality in very easy explanation