a phenotype with the greatest frequency in a trait is enviromental conditions. (G00D !-U<K)
A sampling distribution refers to the distribution from which data relating to a population follows. Information about the sampling distribution plus other information about the population can be inferred by appropriate analysis of samples taken from a distribution.
The normal distribution is very important in statistical analysis. A considerable amount of data follows a normal distribution: the weight and length of items mass-produced usually follow a normal distribution ; and if average demand for a product is high, then demand usually follows a normal distribution. It is possible to show that when the sample is large, the sample mean follows a normal distribution. This result is important in the construction of confidence intervals and in significance testing. In quality control procedures for a mean chart, the construction of the warning and action lines is based on the normal distribution.
Height, weight, IQ,
with mean and standard deviation . Once standardized, , the test statistic follows Standard Normal Probability Distribution.
Given U_i~χ_(ν_i)^2, (U_1/ν_1)/(U_2/ν_2 ) follows which distribution? F_(ν_1,ν_2 ) F Probability Distribution with ν degree of freedom Given T=Z/√(U/ν), Z~N(0,1) and U~χ_ν^2, T^2 follows an F-Distribution F_(1,ν) F Probability Distribution with one degree of freedom in the numerator and ν in the denominator
A sampling distribution refers to the distribution from which data relating to a population follows. Information about the sampling distribution plus other information about the population can be inferred by appropriate analysis of samples taken from a distribution.
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The normal distribution is very important in statistical analysis. A considerable amount of data follows a normal distribution: the weight and length of items mass-produced usually follow a normal distribution ; and if average demand for a product is high, then demand usually follows a normal distribution. It is possible to show that when the sample is large, the sample mean follows a normal distribution. This result is important in the construction of confidence intervals and in significance testing. In quality control procedures for a mean chart, the construction of the warning and action lines is based on the normal distribution.
Height, weight, IQ,
with mean and standard deviation . Once standardized, , the test statistic follows Standard Normal Probability Distribution.
Sony india follows a one level distribution strategy i.e. manufacturer to retailers to customers.
Given T=Z/√(U/ν), Z~N(0,1) and U~χ_ν^2, T follows the Student t-Distribution t_ν Student t-Distribution
Given T=Z/√(U/ν), Z~N(0,1) and U~χ_ν^2, T follows the Student t-Distribution t_ν Student t-Distribution
Given U_i~χ_(ν_i)^2, (U_1/ν_1)/(U_2/ν_2 ) follows which distribution? F_(ν_1,ν_2 ) F Probability Distribution with ν degree of freedom Given T=Z/√(U/ν), Z~N(0,1) and U~χ_ν^2, T^2 follows an F-Distribution F_(1,ν) F Probability Distribution with one degree of freedom in the numerator and ν in the denominator
Statistical theory predicts the phenotype follows Pascal's triangle coefficients.
Statistical theory predicts the phenotype follows Pascal's triangle coefficients.
We prefer mostly normal distribution, because most of the data around us follows normal distribution example height, weight etc. will follow normal. We can check it by plotting the graph then we can see the bell curve on the histogram. The most importantly by CLT(central limit theorem) and law of large numbers, we can say that as n is large the data follows normal distribution.