Wiki@IndraStra

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A user with 1,008 edits. Account created on 29 July 2026.
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21 August 2026

20 August 2026

  • 13:0813:08, 20 August 2026 diff hist +8,902 N Technological singularity Created page with "The '''technological singularity''', also known as the '''singularity''', is a hypothetical future point at which technological development becomes so rapid and transformative that the resulting changes become difficult or impossible for humans to predict or control. The concept is particularly associated with the possibility that artificial intelligence (AI) systems could reach or exceed human-level capabilities and subsequently contribute t..." current
  • 13:0613:06, 20 August 2026 diff hist +7,276 N Margin call Created page with "A '''Margin call''' is a demand from a broker or other financial intermediary requiring an investor to provide additional funds or eligible collateral when the value of a margin account falls below the required maintenance margin. Margin calls can arise when the market value of securities purchased with borrowed funds declines, when the value of collateral falls, or when applicable margin requirements increase. ==Margin account== When..." current
  • 13:0313:03, 20 August 2026 diff hist +7,791 N Heteroskedasticity-consistent standard errors Created page with "'''Heteroskedasticity-consistent standard errors''' ('''HC standard errors''') are standard-error estimators used in regression analysis to provide inference that is robust to heteroskedasticity of the regression errors. In an ordinary least squares (OLS) regression: <math> \mathbf{y} = \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\epsilon} </math> the conventional OLS covariance matrix assume..." current
  • 13:0213:02, 20 August 2026 diff hist +6,727 N Heteroskedasticity Created page with "'''Heteroskedasticity''' is a condition in regression analysis in which the variance of the error term is not constant across observations. In a regression model: <math> Y_i = \beta_0 + \beta_1X_{1,i} + \cdots + \beta_kX_{k,i} + \epsilon_i </math> homoskedasticity requires the conditional variance of the error term to be constant: <math> \operatorname{Var}(\epsilon_i\mid\mathbf{X}) = \sigma^2 </math> Under heteroskedasticity, the conditio..." current
  • 13:0013:00, 20 August 2026 diff hist +8,496 N Alpha (finance) Created page with "'''Alpha''' is a measure of an investment's performance relative to the return that would be expected from a specified benchmark, market model, or set of risk factors. In a regression-based framework, alpha is commonly represented by the intercept of a return regression. For a simple market model: <math> R_i = \alpha_i + \beta_iR_m + \epsilon_i </math> where: * <math>R_i</math> = return of asset or portfolio <math>i</math>; * <math>\alpha_i</math..." current
  • 13:0013:00, 20 August 2026 diff hist +10,878 N Multiple linear regression Created page with "'''Multiple linear regression''' is a statistical method used to model the relationship between a dependent variable and two or more explanatory variables. It extends simple linear regression by allowing multiple explanatory variables to be included in the same model. A general multiple linear regression model is: <math> Y_i = \beta_0 + \beta_1X_{1,i} + \beta_2X_{2,i} + \cdots + \beta_kX_{k,i} + \epsilon_i </math> where: * <math>Y_i</math> =..." current
  • 12:5912:59, 20 August 2026 diff hist +11,295 N Regression analysis Created page with "'''Regression analysis''' is a set of statistical methods used to examine the relationship between a dependent variable and one or more explanatory variables. It is used to estimate relationships, quantify associations, test hypotheses, and generate predictions. In its simplest form, regression analysis models a dependent variable <math>Y</math> as a function of an explanatory variable <math>X</math>: <math> Y_i = \beta_0 + \beta_1X_i + \epsilon_i </math> where:..." current
  • 12:5812:58, 20 August 2026 diff hist +8,862 N Ordinary least squares Created page with "'''Ordinary least squares''' ('''OLS''') is a method for estimating the parameters of a linear regression model by minimizing the sum of the squared differences between observed and fitted values. For a simple linear regression model: <math> Y_i = \beta_0 + \beta_1X_i + \epsilon_i </math> the fitted value is: <math> \hat{Y}_i = \hat{\beta}_0 + \hat{\beta}_1X_i </math> and the residual is: <math> e_i = Y_i-\hat{Y}_i </math> The OLS estimat..." current Tag: Visual edit: Switched
  • 12:5712:57, 20 August 2026 diff hist +6,898 N Coefficient of determination Created page with "'''Coefficient of determination''' is a statistical measure that indicates the proportion of the variation in a dependent variable that is explained by a statistical model. It is commonly denoted by <math>R^2</math> and is widely used in linear regression. For a regression model with an intercept, the coefficient of determination can be expressed as: <math> R^2 = 1- \frac{\mathrm{SS}_{\mathrm{res}}} {\mathrm{SS}_{\mathrm{tot}}} </math> where:..." current
  • 12:5712:57, 20 August 2026 diff hist +8,207 N Linear regression Created page with "'''Linear regression''' is a statistical method used to model the relationship between a dependent variable and one or more explanatory variables by estimating a linear function. In the simplest case, linear regression relates one dependent variable <math>Y</math> to one explanatory variable <math>X</math>. A simple linear regression model is: <math> Y_i = \beta_0 + \beta_1X_i + \epsilon_i </math> where: * <math>Y_i</math> = observed value of the dependent varia..." current
  • 12:5612:56, 20 August 2026 diff hist −37 Cryptocurrency →Stablecoins current Tag: Visual edit
  • 12:5612:56, 20 August 2026 diff hist −36 Insolvency and Bankruptcy Code, 2016 →Objectives current Tag: Visual edit
  • 12:5512:55, 20 August 2026 diff hist −43 Fiscal policy →Mechanism current Tag: Visual edit
  • 12:5512:55, 20 August 2026 diff hist −41 Depreciation →Calculation current Tag: Visual edit
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