Shree GaneshA Statistics

Changing Color Blog Name Statistical Inference I: (Cramer-Rao Inequality, Method Of Moment, Maximum Likelihood Estimator) I. Introduction We see in unbiased estimator from two distinct unbiased estimators give infinitely many unbiased estimators of θ, among these estimators we find the best estimator for parameter θ by comparing their variance or mean square errors. But in some examples, we see that the number of estimators is possible as. For Normal distribution: If X1, X2, ........Xn. random sample from a normal distribution with mean 𝛍 and variance 𝛔², then T1 = x̄, T2 = Sample median, both are unbiased estimators for parameter 𝛍. Now we find a sufficient estimator; therefore, T1 is a sufficient estimator for 𝛍, hence it is the best estimator for parameter 𝛍. Thus, for finding the best estimator, we check if the estimator is sufficient or not. Now we are interested in finding the variance of th

  B.Sc. II Statistics Practical Paper -II       In this article we see all the practical problem Of  B.Sc. II  in Practical Paper -II Practical Number 1. Padmbhushan Vasantraodada Patil Mahavidyalaya, Kavathe Mahankal Department of Statistics Title: Model sampling from Discrete Uniform distribution                                                                                                                   Questions:  1. Draw a model sample of size 10 from the following Discrete Uniform Distribution P(X) = 1/8; x=1,2,3,...,8                = 0 otherwise. Calculate A.M. and H. M. of your sample. 2. Draw a model sample of size 15 from the following Discrete Uniform Distribution Taking values 10,15,20,25,30,35,40,45,50,55. Find mean deviation from mode of your sample. 3. Draw a model sample of size 8 from the following Discrete Uniform Distribution P(X) = 1/13; x=1,2,3,...,13                = 0 otherwise. Obtain the quartiles of your sample. 4. Draw a model sample of size 10 from the