Allows modeling the straight-line relationship between the dependent variable and the independent variable.
Allows modeling more than one independent variable to make predictions about the dependent variable.
Time Series Analysis
Allows forecasting, analyzing and drawing conclusions from time-oriented data.
Allows simplifying many of the computations in modern portfolio theory and risk management.
Allows finding a representative subset of the data, which contains the information of the entire set.
Time Value of Money
Allows measuring equivalence relationships between cash flows occurring on different dates.
Measure of Central Tendency
Allows measuring locations where the data are centered.
Measures of Dispersions
Allows analyzing the variability around the central tendency. If mean return addresses reward, dispersion addresses risk.
Allows testing hypotheses concerning the population values of the intercept
or slope coefficient of a regression model.
Allows testing hypotheses to detect the heteroskedasticity, which occurs when the variance of the errors differs across observations in regression.
Allows testing hypotheses to diagnose serial correlation, which occurs when the error term is correlated across observations in regression.
Allows experimenting with including or excluding different independent variables in a regression in order to detect multicollinearity (often a matter of degree).
Allows testing hypotheses concerning the Stationarity of a single time series.
Allows testing hypotheses concerning the Cointegration (two time series are cointegrated if they share a common trend).
Allows testing hypotheses whether or not a sample of data comes from a population that is normally distributed.
Allows testing hypotheses to detect outliers which are small number of observations at either extreme (small or large) of a sample.
Allows testing hypotheses concerning the value of a single population mean, the differences between two population means and paired comparison.
Allows testing hypotheses concerning the value of a single population variance and the differences between two population variances.
Allows testing hypotheses concerning the correlation calculated on the ranks of two variables, and Autocorrelation (the correlations of a time series with its own past values).
Allows to determine the rejection points of the test statistic from a variety of statistical tables.