Because it's More than a Subject
Following are the small and large sample properties of a Good Estimator Small Sample Properties 1. Unbiased Estimator: Biased means the difference between the parameter’s true value and the estimator’s value. When the difference becomes zero then it is called Unbiased estimator. = 2. Best Estimator: An estimator is called … read more
Chi-Square is an important non-parametric test and as such, rigid assumptions are necessary regarding the type of population we require only the degree of freedom for using this test. Non- parametric chi-square test can be used This test was developed by statistician “Karl Pearson”. As a test of goodness of … read more
The term probability has been interpreted in terms of four definitions : 1. Classical Definition of Probability: the classical definition states that if an experiment consists of ’’ outcomes that are mutually exclusive, exhaustive, and equally likely and of them ate the favorable outcomes of an event A then the … read more
Following are the assumptions underlying when we use method of Least Squares Assumption 1: Linear regression model The regression model is linear in the parameters i.e., Assumption 2: values are fixed in repeated sampling Values taken by the regressor are considered fixed in repeated samples. More technically, is assumed to … read more
The expected value, or mean, of a random variable is a measure of the central location for the random variable. The formula for the expected value of a discrete random variable X follows. The expected value of a discrete random variable X can be written as i.e., Properties of Mathematical … read more
Given the assumptions of the classical linear regression model, the least-squares estimators, in the class of unbiased linear estimators, have minimum variance, that is, they are BLUE. In other words, the Gauss-Markov theorem holds the properties of Best Linear Unbiased Estimators. Following are some of the assumptions that should be … read more