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What do both variance and the standard deviaton tell you about distribution?
How widely spread out, or tightly concentrated about the mean the observations are.
What are the advantages of weighted average?
More importance can be attached to observations which are either of greater importance, accuracy (lesser variance).
In stats What is the goal of data re expression when it comes to regression?
The goal of data re-expression in regression is to transform the response variable or predictors to improve the model's fit and meet the assumptions of linear regression. This can involve techniques such as logarithmic, square root, or polynomial transformations to stabilize variance, linearize relationships, or address issues like non-normality of residuals. By re-expressing the data, statisticians aim to enhance the interpretability and predictive power of the regression model.
What if the variance equaled zero?
If the variance equals zero, it indicates that all the values in the dataset are identical, meaning there is no variability or spread among the data points. This uniformity suggests that every data point is the same as the mean, leading to no dispersion. In practical terms, a variance of zero can imply a lack of diversity or change within the dataset.
What does n-1 indicate in a calculation for variance?
The n-1 indicates that the calculation is being expanded from a sample of a population to the entire population. Bessel's correction(the use of n − 1 instead of n in the formula) is where n is the number of observations in a sample: it corrects the bias in the estimation of the population variance, and some (but not all) of the bias in the estimation of the population standard deviation. That is, when estimating the population variance and standard deviation from a sample when the population mean is unknown, the sample variance is a biased estimator of the population variance, and systematically underestimates it.
What is the meaning of analysis of covariance?
A mix of linear regression and analysis of variance. analysis of covariance is responsible for intergroup variance when analysis of variance is performed.
What is heteroscadacity?
In regression analysis , heteroscedasticity means a situation in which the variance of the dependent variable varies across the data. Heteroscedasticity complicates analysis because many methods in regression analysis are based on an assumption of equal variance.
What is varience?
In statistics, variance measures how far apart a set of numbers is spread out. If the numbers are identical, the variance is zero. Variance can never be negative.
What is the smallest value a variance can be?
0. When all the observations have the same value.
What is the F stat in regression analysis?
The F stat tests the equality of variances. It uses statistical tables for reference and is calculated with F = Variance 1 (max)/variance 2(min).
Basic assumption in analysis of variance?
1- observations are from normally distributed populations. 2- observations are from populations with equal variances.
What has the author Stanton A Glantz written?
Stanton A. Glantz has written: 'Clinician's Pocket Drug Reference 2002' 'Book and Windows Software' 'Primer Biostatistics' 'Primer of Applied Regression & Analysis of Variance' -- subject(s): Analysis of variance, Biometry, Regression analysis 'Primer of Biostatistics, IBM' 'Tobacco War'
The standard deviation of a sample of 10000 observations equals 121 The variance of the sample equals?
It equals 14641.
What do both variance and the standard deviaton tell you about distribution?
How widely spread out, or tightly concentrated about the mean the observations are.
What has the author David J Saville written?
David J. Saville has written: 'Statistical methods' -- subject(s): Regression analysis, Geometry, Analysis of variance
What are the advantages of weighted average?
More importance can be attached to observations which are either of greater importance, accuracy (lesser variance).
What are 5 number data that have mean of 20 value of variance 0?
Variance = 0 means they are all the same. So the question is simplified to: what 5 identical values have a mean of 20. Since they are identical, their mean value is the same as themselves. So the answer, trivially, is [20, 20, 20, 20, 20].
