## What are non spherical disturbances?

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The implication here is that Var(ε |X) = σ2 Ω = Σ, where Ω is a positive definite, symmetric matrix. The random disturbances under this assumption are referred to as nonspherical disturbances.

## What are spherical errors?

This implies the error term has uniform variance (homoscedasticity) and no serial dependence. If this assumption is violated, OLS is still unbiased, but inefficient. The term “spherical errors” will describe the multivariate normal distribution: if in the multivariate normal density, then the equation.

**Why do we use OLS method?**

In econometrics, Ordinary Least Squares (OLS) method is widely used to estimate the parameter of a linear regression model. OLS estimators minimize the sum of the squared errors (a difference between observed values and predicted values).

**What is blue property?**

A property which is less strict than efficiency, is the so called best, linear unbiased estimator (BLUE) property, which also uses the variance of the estimators. BLUE. A vector of estimators is BLUE if it is the minimum variance linear unbiased estimator. To show this property, we use the Gauss-Markov Theorem.

### What is blue in regression?

The Gauss Markov theorem says that, under certain conditions, the ordinary least squares (OLS) estimator of the coefficients of a linear regression model is the best linear unbiased estimator (BLUE), that is, the estimator that has the smallest variance among those that are unbiased and linear in the observed output …

### Why is OLS the best estimator?

This theorem tells that one should use OLS estimators not only because it is unbiased but also because it has minimum variance among the class of all linear and unbiased estimators.

**What is TSS ESS and RSS?**

TSS = ESS + RSS, where TSS is Total Sum of Squares, ESS is Explained Sum of Squares and RSS is Residual Sum of Suqares. The aim of Regression Analysis is explain the variation of dependent variable Y.

**Why are OLS estimators blue?**

OLS estimators are BLUE (i.e. they are linear, unbiased and have the least variance among the class of all linear and unbiased estimators). Amidst all this, one should not forget the Gauss-Markov Theorem (i.e. the estimators of OLS model are BLUE) holds only if the assumptions of OLS are satisfied.

#### Why is OLS called Blue?

#### Why is OLS estimator blue?

**Is OLS the same as linear regression?**

Yes, although ‘linear regression’ refers to any approach to model the relationship between one or more variables, OLS is the method used to find the simple linear regression of a set of data. Linear regression refers to any approach to model a LINEAR relationship between one or more variables.

**What is probable error in statistics?**

As mentioned, probable error is the coefficient of correlation that supports in finding out about the accurate values of the coefficients. It also helps in determining the reliability of the coefficient. The calculation of the correlation coefficient usually takes place from the samples.

## How to find the probable error of Rho?

Formulas for Calculating Probable Error P.E r product-moment = 0.6745 (1-r2)/√N. The second formula is applicable when we need the probable error for rho. We… 0.6745 (1-ρ2)/√N {1 + 1.086ρ2 + 0.13ρ4 + .002ρ6}. We calculate it through ρ by using the transmutation formula. The… P. E rfound from ρ =

## What is the value of probable error in a symmetric distribution?

Thus for a symmetric distribution it is equivalent to half the interquartile range, or the median absolute deviation. One such use of the term probable error in this sense is as the name for the scale parameter of the Cauchy distribution, which does not have a standard deviation.

**What is the probable error of N = 64?**

Therefore, the probable error is: 0.0486. Question: If the value of r = 0.7 and that of n = 64, then find the P. E. of the correlation of coefficient. Furthermore, find the limits for the population correlation coefficient. Solution: Here, we have to calculate the probable error.