# Statistical Terms in Managerial Finance

Briefly describe: a statistical relation, time series, cross section, least squares, See, r, R-squared, t-value, multicollinearity and serial correlation.

© BrainMass Inc. brainmass.com October 9, 2019, 5:30 pm ad1c9bdddfhttps://brainmass.com/statistics/hypothesis-testing/59270

#### Solution Preview

Please see response attached. I hope this helps and take care.

RESPONSE:

Hi, Let's look at each of these concepts.

1. Briefly describe: a statistical relation, time series, cross section, least squares, See (what?), r, R-squared, t-value, multicollinearity, serial correlation

(1) A statistical relation -A statistical relation is when two variables vary together. For example, there is a statistic relation (e.g., correlation) between obesity and income level - high-income people are less likely to be obese than are lower income people.One study investigated the "Statistical Relation between Monthly Mean Precipitable Water and Surface-Level Humidity over Global Oceans." This relation is found to be applicable to all major ocean basins and can be used to monitor interannual variability. Boundary-layer thermodynamics of different air masses are suggested as an explanation of some characteristics of this relation. (6)

(2) Time series- A time series is a sequence of observations, which are ordered in time (or space). If observations are made on some phenomenon throughout time, it is most sensible to display the data in the order in which they arose, particularly since successive observations will probably be dependent. Time series are best displayed in a scatter plot. The series value X is plotted on the vertical axis and time t on the horizontal axis. Time is called the independent variable (in this case however, something over which you have little control). There are two kinds of time series data: ...

#### Solution Summary

This solution briefly describes the following term: a statistical relation, time series, cross section, least squares, r, R-squared, t-value, multicollinearity and serial correlation. Links are provided for further research.