# Average

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Related to Central Tendency: standard deviation, measures of dispersion

## Average

An arithmetic mean return of selected stocks intended to represent the behavior of the market or some component of it. One good example is the widely quoted Dow Jones Industrial Average, which adds the current prices of the 30 DJIA stocks, and divides the results by a predetermined number, the divisor.

## Average (across-day) measures

An estimation of price that uses the average or representative price of a large number of trades.

## Average

A simple way to calculate the relative price of an index of stocks that involves adding the prices of all the stocks in the index and dividing by the total number of stocks. Market averages may be weighted, for example, for price or market capitalization. Movements in the market average of an index are considered a way to observe trends in the health of the companies represented in it. Some market averages are taken as an indicator of health in the broader economy; prominent examples of this include the Dow Jones Industrial Average and S&P 500 indices.

See averages.

## Average.

A stock market average is a mathematical way of reporting the composite change in prices of the stocks that the average includes.

Each average is designed to reflect the general movement of the broad market or a certain segment of the market and often serves as a benchmark for the performance of individual stocks in its sphere.

A true average adds the prices of the stocks it covers and divides that amount by the number of stocks.

However, many averages are weighted, which usually means they count stocks with the largest market capitalizations more heavily than they do others. Weighting reflects the impact that the stocks of the biggest companies have on the markets and on the economy in general.

The Dow Jones Industrial Average (DJIA), which tracks the performance of 30 large-company stocks, is the most widely followed market average in the United States.

References in periodicals archive ?
Table 3 Changes in the Width of the Central Tendency and Changes in Uncertainty Standard deviation 80th - 20th percentile Dependent variable (1) (2) Width of central tendency 0.047 (***) 0.09 (0.02) (0.10) Constant -0.008 0.01 (0.004) (0.02) Observations 20 20 [R.sup.2] 0.48 0.11 (*) Significant at the 10 percent level (**) Significant at the 5 percent level (***) Significant at the 1 percent level Note: Robust standard errors are in parentheses.
In addition to the central tendency of the group as a whole, we are also interested in looking at how the individuals varied away from (or dispersed around) the central tendency.
Statistical hypothesis: the null hypothesis is that the central tendency of the EB scores is the same in students with parents of different educational levels [[H.sub.0]: E(primary) = E(secondary) = E(university) = E(postgraduate)], the alternative hypothesis is that the central tendency of the scores is different [[H.sub.1]: E(primary) [not equal to] E(secondary) [not equal to] E(university) [not equal to] E(postgraduate)].
The first two measures of central tendency, the mean and the median, work well with data that represent some quantity, such as time expended and work units produced.
Monthly expected inflation also generally varies more than the central tendency, particularly in recent years.
The constants and properties we observe and can so accurately measure and (sometimes) predict are the result of the central tendency of an astronomical number of quanta.
Therefore, many researchers report the median RT as a central tendency parameter, because it is less susceptible to departures from normality (i.e., robust).
Rather, it continues to release the forecast information as a range of forecasts, both the full range between the high and the low and a central tendency that omits the extreme values.
Assume prediction is possible (clockwork universe); gather data and relationships and see what you learn (inductive thinking); identify central tendencies (law of large numbers); rely on logic, math, and science (science as a predictive discipline); identify areas to be evaluated for change impacts (future-oriented mind-set); identify key trends and forces (change drivers); and pursue central tendency causal impacts as far as possible while assuming other things unchanged (disciplined web of implications).
Following past practice, we will publish the central tendency and the range of the projections for each variable and each year.
Cohen takes an accessible, conversation approach when introducing the conceptual foundations and basic of statistic procedures, a practice he continues with material on frequency tables, graphs, distributions, measures of central tendency and variability, standardized scores and the normal distribution, hypothesis testing with one or two samples, internal estimation and the t distribution, the t test for two independent sample means, statistical power and effect size, linear correlation and regression, the matched t test, one-way independent ANOVA and two-way ANOVA, multiple regression and its connection to ANOVA, nonparametric statistics, chi-square tests and statistical tests for ordinal data.
Recommendations include: Use intuitive metrics, not pretentious full-color curves; generate, report, and demand all data and metrics; the best metrics are F50 (central tendency), F.1 or F.01 (early failures), and beta (a measure of breadth); never use the "first fail" as a metric of anything.

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