Search results
Results From The WOW.Com Content Network
Econometrics is an application of statistical methods to economic data in order to give empirical content to economic relationships. [1] More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference."
Economic statistics is a topic in applied statistics and applied economics that concerns the collection, processing, compilation, dissemination, and analysis of economic data. It is closely related to business statistics and econometrics . [ 1 ]
Confidence intervals: the red line is true value for the mean in this example, the blue lines are random confidence intervals for 100 realizations. Most studies only sample part of a population, so results do not fully represent the whole population. Any estimates obtained from the sample only approximate the population value.
Mean imputation can be carried out within classes (i.e. categories such as gender), and can be expressed as ^ = ¯ where ^ is the imputed value for record and ¯ is the sample mean of respondent data within some class . This is a special case of generalized regression imputation:
The earlier term for the discipline was "political economy", but since the late 19th century, it has commonly been called "economics". [22] The term is ultimately derived from Ancient Greek οἰκονομία (oikonomia) which is a term for the "way (nomos) to run a household (oikos)", or in other words the know-how of an οἰκονομικός (oikonomikos), or "household or homestead manager".
Economic data are data describing an actual economy, past or present.These are typically found in time-series form, that is, covering more than one time period (say the monthly unemployment rate for the last five years) or in cross-sectional data in one time period (say for consumption and income levels for sample households).
Bayesian statistics are based on a different philosophical approach for proof of inference.The mathematical formula for Bayes's theorem is: [|] = [|] [] []The formula is read as the probability of the parameter (or hypothesis =h, as used in the notation on axioms) “given” the data (or empirical observation), where the horizontal bar refers to "given".
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]