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What assumption will work best for a large data set with a normal distribution?
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What kind of graph would you use to show the scores?
When putting the scores in, you use the normal distribution graph, which is the best start.
What is gaussian distribution and what is its significance in least squares analysis?
From a technical perspective, alternative characterizations are possible, for example: The normal distribution is the only absolutely continuous distribution all of whose cumulants beyond the first two (i.e. other than the mean and variance) are zero. For a given mean and variance, the corresponding normal distribution is the continuous distribution with the maximum entropy. In order to make statistical tests on the results it is necessary to make assumptions about the nature of the experimental errors. A common (but not necessary) assumption is that the errors belong to a Normal distribution. The central limit theorem supports the idea that this is a good approximation in many cases. The Gauss-Markov theorem. In a linear model in which the errors have expectation zero conditional on the independent variables, are uncorrelated and have equal variances, the best linear unbiased estimator of any linear combination of the observations, is its least-squares estimator. "Best" means that the least squares estimators of the parameters have minimum variance. The assumption of equal variance is valid when the errors all belong to the same distribution. In a linear model, if the errors belong to a Normal distribution the least squares estimators are also the maximum likelihood estimators. However, if the errors are not normally distributed, a central limit theorem often nonetheless implies that the parameter estimates will be approximately normally distributed so long as the sample is reasonably large. For this reason, given the important property that the error mean is independent of the independent variables, the distribution of the error term is not an important issue in regression analysis. Specifically, it is not typically important whether the error term follows a normal distribution. In a least squares calculation with unit weights, or in linear regression, the variance on the jth parameter, denoted , is usually estimated with where the true residual variance σ2 is replaced by an estimate based on the minimised value of the sum of squares objective function S. The denominator, n-m, is the statistical degrees of freedom; see effective degrees of freedom for generalizations. Confidence limits can be found if the probability distribution of the parameters is known, or an asymptotic approximation is made, or assumed. Likewise statistical tests on the residuals can be made if the probability distribution of the residuals is known or assumed. The probability distribution of any linear combination of the dependent variables can be derived if the probability distribution of experimental errors is known or assumed. Inference is particularly straightforward if the errors are assumed to follow a normal distribution, which implies that the parameter estimates and residuals will also be normally distributed conditional on the values of the independent variables.
Distinguish between selective distribution and exclusive distribution?
Selective distribution involves a producer using a limited number of outlets in a geographical area to sell products. An advantage of this approach is that the producer can choose the most appropriate or best-performing outlets and focus effort (e.g. training) on them. Selective distribution works best when consumers are prepared to "shop around" - in other words - they have a preference for a particular brand or price and will search out the outlets that supply.Exclusive distribution is an extreme form of selective distribution in which only one wholesaler, retailer or distributor is used in a specific geographical area.
What is ten to the sevententh power?
Such large numbers are best written in scientific notation - in this case, 1017 (ten to the seventeenth power). In normal notation, that would be a one, followed by 17 zeroes.
When listing data for a frequency distribution table it is best to list the numbers in order?
Yes.
In a normal distribution which statement best describes the relationship between mean median and mode?
[object Object]
Why standard deviation is best measure of dispersion?
standard deviation is best measure of dispersion because all the data distributions are nearer to the normal distribution.
What kind of graph would you use to show the scores?
When putting the scores in, you use the normal distribution graph, which is the best start.
What is gaussian distribution and what is its significance in least squares analysis?
From a technical perspective, alternative characterizations are possible, for example: The normal distribution is the only absolutely continuous distribution all of whose cumulants beyond the first two (i.e. other than the mean and variance) are zero. For a given mean and variance, the corresponding normal distribution is the continuous distribution with the maximum entropy. In order to make statistical tests on the results it is necessary to make assumptions about the nature of the experimental errors. A common (but not necessary) assumption is that the errors belong to a Normal distribution. The central limit theorem supports the idea that this is a good approximation in many cases. The Gauss-Markov theorem. In a linear model in which the errors have expectation zero conditional on the independent variables, are uncorrelated and have equal variances, the best linear unbiased estimator of any linear combination of the observations, is its least-squares estimator. "Best" means that the least squares estimators of the parameters have minimum variance. The assumption of equal variance is valid when the errors all belong to the same distribution. In a linear model, if the errors belong to a Normal distribution the least squares estimators are also the maximum likelihood estimators. However, if the errors are not normally distributed, a central limit theorem often nonetheless implies that the parameter estimates will be approximately normally distributed so long as the sample is reasonably large. For this reason, given the important property that the error mean is independent of the independent variables, the distribution of the error term is not an important issue in regression analysis. Specifically, it is not typically important whether the error term follows a normal distribution. In a least squares calculation with unit weights, or in linear regression, the variance on the jth parameter, denoted , is usually estimated with where the true residual variance σ2 is replaced by an estimate based on the minimised value of the sum of squares objective function S. The denominator, n-m, is the statistical degrees of freedom; see effective degrees of freedom for generalizations. Confidence limits can be found if the probability distribution of the parameters is known, or an asymptotic approximation is made, or assumed. Likewise statistical tests on the residuals can be made if the probability distribution of the residuals is known or assumed. The probability distribution of any linear combination of the dependent variables can be derived if the probability distribution of experimental errors is known or assumed. Inference is particularly straightforward if the errors are assumed to follow a normal distribution, which implies that the parameter estimates and residuals will also be normally distributed conditional on the values of the independent variables.
Which accounting term best describes assumption made in applying the four inventory methods?
Cost Flow Assumption
What word best describes a risk undertaking?
assumption
Which Thailand private universities is the best?
ABAC Assumption University
In statistics what is the difference between average and normal?
Given a set of n numbers, the average is the sum of the set of numbers divided by n. An average implies that I have a set of data and I've made the calculation as above. The word "normal" is usually used as "normal distribution." Two terms often confused are "mean" and "average." When an average (a statistic) is calculated, this is the best estimate of the mean (a parameter) of the distribution. If I am referring to the properties of a distribution, I should use the term "mean" and not "average." Often, people will refer to the mean of a dataset, when they have calculated an average.
Where do i state an assumption?
It is generally best to state your assumptions right at the beginning.
What type of graph is best suited for displaying the range and frequency distribution of the ages of the members of a large family while still showing each individual data point?
bar graph
What best describes income distribution in the US?
'merica
What is best strategy for moving a Distribution Center?
Honestly...Not to...
