KeyWEn.com  
 
 
 
Estimate       Article     History   Tree Map
  Encyclopedia of Keywords > Parameters > Estimate   Michael Charnine

Keywords and Sections
SKATING DATA
FORENSIC SCIENTISTS
FUNCTION
LIKELIHOOD FUNCTION
REGRESSION COEFFICIENT
REGRESSION COEFFICIENTS
MEAN SQUARED ERROR
MSE
LOAN
FAIR MARKET VALUE
MEASURE
PRECISION
POPULATION PARAMETERS
TRUE VALUE
SET
SAMPLE DATA
ERROR VARIANCE
RESIDUALS
UNBIASED ESTIMATE
UNBIASED
BIASED
UNKNOWN PARAMETERS
UNKNOWN PARAMETER
BIAS
SYSTEMATIC ERROR
COMPARABLE PROPERTIES
MARKET VALUE
COVARIANCE MATRIX
MLE
CONSERVATIVE ESTIMATE
SAMPLE VARIANCE
ESTIMATOR
POPULATION STANDARD DEVIATION
POPULATION VARIANCE
POPULATION PARAMETER
POINT ESTIMATE
APPRAISAL
APPRAISER
STANDARD DEVIATION
POPULATION MEAN
MAXIMUM LIKELIHOOD ESTIMATE
SAMPLE MEAN
STANDARD ERRORS
STANDARD ERROR
AUTOCORRELATION
CORRELATION
Review of Short Phrases and Links

    This Review contains major "Estimate"- related terms, short phrases and links grouped together in the form of Encyclopedia article.

Definitions

  1. The estimate is unbiased, which means that, on average, the estimate will equal the population value.
  2. An estimate was made of the cost of purchasing extra Gemini spacecraft for this purpose.
  3. An estimate is suitable if replacing it with the unknown parameter does not cause major damage in next computations.
  4. The estimate is evaluated at gridlen equally spaced points in the range where the density estimate is nonzero.
  5. A point estimate is a single value given as the estimate of a population parameter that is of interest, for example the mean of some quantity.

Skating Data

  1. But it would be better if more skating data were acquired and analysed to estimate the real-world frequencies of "no-show paradoxes," "Condorcet cycles," and other pathologies. (Web site)

Forensic Scientists

  1. Forensic scientists and archaeologists use metric and nonmetric traits to estimate what the bearer of the skull looked like.

Function

  1. Suppose we are trying to estimate the parameter using an estimator (that is, some function of the observed data).
  2. Therefore, we use a regression analysis to estimate the coefficients in the function that is the best fit to the pairs of data (D, U).
  3. Optional if model provides an "estimate" function to estimate these values.

Likelihood Function

  1. We estimate the parameters in our regression equation by choosing them to maximize the likelihood function we construct.
  2. If you pick 3 or more tickets the likelihood function has a well defined mean value, which is larger than the maximum likelihood estimate.
  3. The maximum likelihood estimate would give us standard errors based on the second derivative of the likelihood function or on bootstrapping.

Regression Coefficient

  1. It provides an estimate of the standard deviation of the sampling distribution of the regression coefficient.
  2. If you look closely at each term of the variance for our example you will see that each fraction looks like the estimate of a regression coefficient.

Regression Coefficients

  1. Ordinary least squares (OLS) are then used to estimate the regression coefficients for this bootstrap sample of paired cases.
  2. Determines the regression coefficients, the generalized correlation coefficient and the standard error of estimate.

Mean Squared Error

  1. However, caution is advised as bias correction might inflate the variance and mean squared error of an estimate [ 10].
  2. Now, just fix c at a fine grid of x values in the range of your data, estimate a, b, and d, and then note what the mean squared error is.

Mse

  1. The MSE measures how far the estimator is off from what it is trying to estimate, on the average in repeated experiments.
  2. You can use simulations to estimate the Mean Square Error (MSE) of several methods, in order to choose the best one.

Loan

  1. It requires a lender to give you a good faith estimate of closing costs within three days after you apply for a loan.
  2. Also requires that the lender provide a "good faith estimate" of closing costs prior to closing of the loan.
  3. Lenders are required to give you a good-faith estimate of your closing costs within three days after you apply for a loan.

Fair Market Value

  1. The appraisal fee covers the cost of evaluating your home to estimate the fair market value.
  2. An appraisal is an estimate of the fair market value of your home.

Measure

  1. A "measure of central tendency" is either a location parameter or a statistic used to estimate a location parameter.
  2. The more students we measure the more accurate we can be and that's why we need an estimate of uncertainty (standard error).
  3. To estimate or measure the quantity of lumber in (logs or uncut trees).

Precision

  1. Standard error – The most commonly used measure of the precision of an estimate.
  2. As such, it measures the precision of the statistic as an estimate of a population or model parameter.
  3. Precision was assessed by the model R 2 and the standard error of the estimate from the regression procedures described above.

Population Parameters

  1. The goal of statistical analysis is usually to estimate population parameters, using statistics from a sample of the population.
  2. Notice that to be able to estimate the population parameters, the sample size n must be greater than one.

True Value

  1. It is well-known that the maximum likelihood estimate for the variance does not converge to the true value of the variance.
  2. Provides connection between ML estimate and true value.
  3. We want to know how likely it is for any given sample estimate to be unacceptably far from the true value.

Set

  1. This is because the mean of a set of values is the estimate that minimizes square loss versus those values.
  2. To estimate the force constant, k, a series of n measurements with different forces will produce a set of data,, where y i is a measured spring extension.
  3. Therefore, a parameter that is used to estimate the color blur area can be set according to the stop value at the imaging time.

Sample Data

  1. An estimate of the true parameter value is made using the sample data.

Error Variance

  1. The estimate the value of the error variance is a measure of variability of the y values about the estimated line.
  2. It provides an estimate of the residual or error variance of the population from which the sample was drawn.
  3. Note that you are using the maximum likelihood estimate of the error variance under the null hypothesis.

Residuals

  1. Another name for the standard error of the estimate is the standard deviation of the residuals.
  2. No. Residuals, which are errors of estimate, are measured differently in ANOVA compared to regression.
  3. In regression analysis, the best estimate of the standard deviation of the residuals about the regression line.

Unbiased Estimate

  1. The sample mean is a Fisher consistent and unbiased estimate of the population mean, but not all Fisher consistent estimates are unbiased.
  2. Degrees of freedom is used to obtain unbiased estimate for the population parameters.
  3. An appraisal is an unbiased estimate of the quality, value, and best use of a specific property.

Unbiased

  1. For example, the mean of a sample is an unbiased estimate of the mean of the population from which the sample was drawn.
  2. For data from a symmetric distribution, the trimmed mean is an unbiased estimate of the population mean.
  3. The mean of a random sample is an unbiased estimate of the mean of the population from which it was drawn.

Biased

  1. It is a biased estimate of population variance explained.
  2. Just because the error for one estimate is large, does not mean the estimator is biased.
  3. Also, just because an estimator is biased, does not preclude the error of an estimate from being zero (we may have gotten lucky).

Unknown Parameters

  1. Note that under either hypothesis, the distribution of the data is fully specified; there are no unknown parameters to estimate.
  2. Approximate model equations often relate given measurements to unknown parameters whose estimate is sought.
  3. Furthermore, we employ the Minimum Description Length rule to estimate the number of unknown parameters.

Unknown Parameter

  1. Large sample sizes n are good because the standard deviation gets smaller, which allows a more precise estimate of the unknown parameter p.

Bias

  1. From this new set of "observations" for the statistic an estimate for the bias can be calculated and an estimate for the variance of the statistic.
  2. In most cases an estimate of the standard error (or bias) of the sample will be needed.
  3. With a fast enough method, simulations can be used to estimate the bias and variance at a marker and correct the estimate of.

Systematic Error

  1. Case 2: the estimate of systematic error or bias is interpreted at a decision level near the mean of the data.
  2. Case 1: proportional error is absent, therefore the estimate of systematic error or bias is applicable throughout the range of the data.
  3. When r is low and a difference plot is used, calculate t-test statistics to provide a quantitative estimate of a systematic error.

Comparable Properties

  1. Appraisal: A professional analysis, including references to sales of comparable properties, used to estimate the value of the property.
  2. A comparative market analysis is an informal estimate of market value, based on sales of comparable properties, performed by a real estate agent or broker.

Market Value

  1. The lender requires this opinion or estimate of the market value of the house for the loan.
  2. APPRAISAL - An estimate of market value based on an analysis of a property.
  3. We use the book value of debt as a proxy for the market value, and cumulate the values, across the sector, to estimate the ratio.

Covariance Matrix

  1. In practice, we would estimate the covariance matrix based on sampled data from X and Y (i.e.
  2. Assuming the missing data are missing at random this results in an estimate for the covariance matrix which is unbiased.
  3. In other words, the covariance matrix of the Gaussian is nearly singular, reducing the number of parameters to estimate.

Mle

  1. If you specify FITMETHOD = MLE (in parentheses after the SU option) the method of maximum likelihood is used to estimate the parameters.
  2. A reasonable estimate of the standard error for the MLE can be given by, where I n is the Fisher information of the parameter.

Conservative Estimate

  1. Using this ratio, the conservative estimate of 270 000 vascular plant species resulted in an estimate of 1620 000 species of fungi.
  2. One recent conservative estimate puts the number of arthropod species in tropical forests at 6 to 9 million species (Thomas, 1990).
  3. When you factor in all of the above, a conservative estimate for the value of those Michael Phelps Olympic gold medals are: Priceless.

Sample Variance

  1. A sample variance, s 2, is an unbiased estimate of the population variance, 2, when the sum of squares is divided by n -1.
  2. The sample variance is an unbiased estimate of population variance.
  3. If μ and σ 2 are replaced by the standard formulae for sample mean and sample variance, then this is a biased estimate.

Estimator

  1. We have seen how the bootstrap could help us estimate the bias, variance and MSE of an estimator.
  2. A statistic used to estimate a parameter is called an estimator.
  3. However a statistic, when used to estimate a population parameter, is called an estimator.

Population Standard Deviation

  1. The standard deviation calculated from the sample data (s) is used as an estimate of the population standard deviation (s).
  2. A sample standard deviation is an estimate, based on a sample, of a population standard deviation.
  3. This means that the estimate of the population standard deviation of these coefficients (.119) is significantly different from zero.

Population Variance

  1. In a one-way analysis of variance (ANOVA) with g groups, there are three ways of using the data to estimate the population variance.
  2. Note: When using the sample variance to estimate the population variance, the divisor (n-1) is typically used instead of (n) to calculate the average.
  3. Its value is only of interest as an estimate for the population variance.

Population Parameter

  1. Sampling error is the difference between a population parameter and a sample statistic used to estimate it.
  2. Interval estimate: estimate of a population parameter that provides interval believed to contain the value of the parameter.
  3. The purpose of the statistical methods that have been discussed so far is to estimate a population parameter by means of a sample statistic.

Point Estimate

  1. A point estimate involves using the value of a particular sample statistic to estimate the value for a parameter of interest.
  2. This sample mean is then used as the point estimate of the population mean.
  3. For a probability distribution, the coefficient of variation is defined as the ratio of the standard deviation to the point estimate of the mean.

Appraisal

  1. Appraiser: a qualified individual who uses his or her experience and knowledge to prepare the appraisal estimate.
  2. Appraisal A report made by a qualified person setting forth an opinion or estimate of property value.
  3. Appraisal An expert judgment or estimate of the quality or value of real estate as of a given date.

Appraiser

  1. Appraisal An estimate of the value of property, made by a qualified professional called an "appraiser".
  2. Appraiser - A person qualified by education, training, and experience to estimate the value of real and personal property.
  3. Appraisal Fee: fee charged by an appraiser to estimate the market value of a property.

Standard Deviation

  1. We can just substitute the standard deviation we got from the sample as an estimate of the standard deviation of the population.
  2. If the ACF consists of positive values then the estimate of the variance (and its square root, the standard deviation) will be biased low.
  3. This is a consequence of the fact that no information is available to estimate the mean and standard deviation of the parameter of concern.

Population Mean

  1. If the value of the estimator in a particular sample is found to be 5, then 5 is the estimate of the population mean .
  2. Instead she could use an estimate of this population mean m by calculating the mean of a representative sample of customers.
  3. Sample Means Sample Means The sample mean from a group of observations is an estimate of the population mean.

Maximum Likelihood Estimate

  1. However, for many situations with quantitative covariates, the maximum likelihood estimate (MLE) is on the boundary of the parameter space.
  2. This value is the maximum likelihood estimate (MLE) of p.
  3. Specifically, it is possible to furnish estimators that improve considerably upon the maximum likelihood estimate in terms of mean squared error.

Sample Mean

  1. In the example above, we could use the sample mean as our statistic because the sample mean provides an estimate for the population mean.
  2. The usual arguments indicate that the sample variance can be used to estimate the variance of the sample mean.
  3. For Gaussian nodes, we can compute the sample mean and variance, and use linear regression to estimate the weight matrix.

Standard Errors

  1. What it is useful, however, is to estimate "real" standard errors by enlarging the model "ideal" standard errors by the model misfit encountered in the data.
  2. Computer programs have been developed to estimate the value of the model coefficients and their standard errors.
  3. Both techniques yield the same estimate for the regression coefficient; however, the standard errors differ between the two methods.

Standard Error

  1. Standard error of the mean A measure of the accuracy of the sample mean as an estimate of the population mean.
  2. Standard Error of the Mean (SEM): The precision of the estimate of a sample mean, which is very common in the literature.
  3. Notice that the true value is, by definition, unknown and this implies that the standard error of an estimate is itself an estimated value.

Autocorrelation

  1. Unity at zero-lag - Normalizes the estimate of the autocorrelation for each channel so that the zero-lag sum is identically 1.
  2. Unbiased - Generates the unbiased estimate of the autocorrelation.
  3. Biased - Generates the biased estimate of the autocorrelation.

Correlation

  1. Where regression is used to estimate the value of a dependent variable, correlation measures the degree of relationship between two variables.
  2. The polychoric correlation is another correlation applied to ordinal data that aims to estimate the correlation between theorised latent variables.
  3. An estimate of the effect of smoking on health can be made by also making use of the correlation between taxes and smoking patterns.

Categories

  1. Parameters
  2. Encyclopedia of Keywords > Thought > Value
  3. Information > Evaluation > Analysis > Variance
  4. Encyclopedia of Keywords > Society > Population
  5. Condorcet

Related Keywords

    * Accurate * Accurate Estimate * Astronomers * Borrower * Closing * Closing Costs * Confidence Interval * Cost * Costs * Data * Error * Error Estimate * Estimated * Estimates * Estimate Parameters * Estimate Value * Estimating * Good Faith Estimate * Incidence * Lender * Lenders * Magnitude * Maximum * Maximum Likelihood * Mean * Mean Estimate * Million * Model * Model Parameters * Number * Parameter * Parameters * Population * Prevalence * Probability * Proportion * Sample * Samples * Sample Estimate * Size * Squares * Statistic * Statistics * Value * Values * Variance * Variance Estimate
  1. Books about "Estimate" in Amazon.com

Book: Keywen Category Structure


  Short phrases about "Estimate"
  Originally created: April 04, 2011.
  Please send us comments and questions by this Online Form
  Please click on Move Up to move good phrases up.
0.0299 sec. a=1..