linear regression

(noun)

In statistics, linear regression is an approach to modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X.

Related Terms

Examples of linear regression in the following topics:

  • Regression Analysis for Forecast Improvement

    • One can forecast based on linear relationships.
    • Regression Analysis is a causal / econometric forecasting method.
    • These methods include both parametric (linear or non-linear) and non-parametric techniques.
    • The predictors are linearly independent, i.e. it is not possible to express any predictor as a linear combination of the others.
    • Familiar methods, such as linear regression and ordinary least squares regression, are parametric, in that the regression function is defined in terms of a finite number of unknown parameters that are estimated from the data.

  • Answers to Chapter 19 Questions

    • The Forex Beta measures the economic exposure and is a parameter estimate of a linear regression equation.

  • The Cost of Common Equity

    • You'll recognize the first half of this equation as the simple CAPM calculation, while the second half includes SMB (small minus big market capitalization) and HML (high minus low book-to-market ratio) multiplied by coefficients (from linear regression).

  • Arbitrage Pricing Theory

    • The Arbitrage Pricing Theory (APT) is a linear relationship between systemic factors and the return of an asset.
    • It is a generalized linear function for determining the price of an asset.
    • One of the most important aspects of APT is that, like CAPM, the relationship between each factor and the return is linear.

  • Fama-French Three-Factor Model

    • The Fama–French three-factor model is a linear model designed by Eugene Fama and Kenneth French to describe stock returns.
    • Like CAPM and the Arbitrage Pricing Theory, the Fama-French three-factor model is a linear model that relates structural factors to the expected return of an asset.

  • Measuring and Protecting against Economic Exposure

    • An analyst measures the economic exposure by estimating a regression equation, shown in Equation 24.
    • Regression equation measures the association between the asset's price and the exchange rate.
    • We can estimate the regression equation easily, and we calculate ($\beta$) by using Equation 25.

  • Behavior of an Efficient Market

    • Research based on regression and scatter diagrams has strongly supported Samuelson's dictum.

  • Sensitivity Analysis

    • Sensitivity Analysis deals with finding out the amount by which we can change the input data for the output of our linear programming model to remain comparatively unchanged.

  • Macroeconomic Factors Influencing the Interest Rate

    • ., the federal fund rates in the United States), πt is the rate of inflation as measured by the GDP deflator, π*t is the desired rate of inflation, r*t is the assumed equilibrium real interest rate, yt is the logarithm of real GDP, and y*t is the logarithm of potential output, as determined by a linear trend.

  • International Fisher Effect

    • If the interest rates are low, then we can use a linear approximation that yields Equation 21.