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  2. Residual value - Wikipedia

    en.wikipedia.org/wiki/Residual_value

    The leasing company setting the residual values (RVs) will use their own historical information to insert the adjustment factors within the calculation to set the end value being the residual value. In accounting, the residual value could be defined as an estimated amount that an entity can obtain when disposing of an asset after its useful ...

  3. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    The residual is the difference between the observed value and the estimated value of the quantity of interest (for example, a sample mean). The distinction is most important in regression analysis , where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals .

  4. Lack-of-fit sum of squares - Wikipedia

    en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares

    One takes as estimates of α and β the values that minimize the sum of squares of residuals, i.e., the sum of squares of the differences between the observed y-value and the fitted y-value. To have a lack-of-fit sum of squares that differs from the residual sum of squares, one must observe more than one y -value for each of one or more of the ...

  5. What Is Residual Value When Leasing a Car? Plus How to ... - AOL

    www.aol.com/news/residual-value-leasing-car-plus...

    Discover what residual value is as it relates to leasing a vehicle, learn how to calculate it, and explore its effects to help you make more informed decisions.

  6. What Is Residual Income and How Do You Make It? - AOL

    www.aol.com/residual-income-184623017.html

    Here’s how to calculate it: Determine your net (after-tax) monthly income from all sources. List all of your necessary expenses, like rent or mortgage, car payment, minimum credit payments ...

  7. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    The general regression model with n observations and k explanators, the first of which is a constant unit vector whose coefficient is the regression intercept, is = + where y is an n × 1 vector of dependent variable observations, each column of the n × k matrix X is a vector of observations on one of the k explanators, is a k × 1 vector of true coefficients, and e is an n× 1 vector of the ...

  8. Reduced chi-squared statistic - Wikipedia

    en.wikipedia.org/wiki/Reduced_chi-squared_statistic

    In ordinary least squares, the definition simplifies to: =, =, where the numerator is the residual sum of squares (RSS). When the fit is just an ordinary mean, then χ ν 2 {\displaystyle \chi _{\nu }^{2}} equals the sample variance , the squared sample standard deviation .

  9. Deviance (statistics) - Wikipedia

    en.wikipedia.org/wiki/Deviance_(statistics)

    In statistics, deviance is a goodness-of-fit statistic for a statistical model; it is often used for statistical hypothesis testing.It is a generalization of the idea of using the sum of squares of residuals (SSR) in ordinary least squares to cases where model-fitting is achieved by maximum likelihood.