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OpenBB/assets/js/b7914986.0147b79f.js
github-actions[bot] c8e14f3cc6 deploy: 917b2bb44c
2022-12-15 21:02:29 +00:00

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JavaScript

"use strict";(self.webpackChunkdocs=self.webpackChunkdocs||[]).push([[35399],{3905:(e,t,n)=>{n.d(t,{Zo:()=>c,kt:()=>d});var r=n(67294);function a(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function l(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?o(Object(n),!0).forEach((function(t){a(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):o(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function i(e,t){if(null==e)return{};var n,r,a=function(e,t){if(null==e)return{};var n,r,a={},o=Object.keys(e);for(r=0;r<o.length;r++)n=o[r],t.indexOf(n)>=0||(a[n]=e[n]);return a}(e,t);if(Object.getOwnPropertySymbols){var o=Object.getOwnPropertySymbols(e);for(r=0;r<o.length;r++)n=o[r],t.indexOf(n)>=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(a[n]=e[n])}return a}var s=r.createContext({}),p=function(e){var t=r.useContext(s),n=t;return e&&(n="function"==typeof e?e(t):l(l({},t),e)),n},c=function(e){var t=p(e.components);return r.createElement(s.Provider,{value:t},e.children)},m={inlineCode:"code",wrapper:function(e){var t=e.children;return r.createElement(r.Fragment,{},t)}},u=r.forwardRef((function(e,t){var n=e.components,a=e.mdxType,o=e.originalType,s=e.parentName,c=i(e,["components","mdxType","originalType","parentName"]),u=p(n),d=a,f=u["".concat(s,".").concat(d)]||u[d]||m[d]||o;return n?r.createElement(f,l(l({ref:t},c),{},{components:n})):r.createElement(f,l({ref:t},c))}));function d(e,t){var n=arguments,a=t&&t.mdxType;if("string"==typeof e||a){var o=n.length,l=new Array(o);l[0]=u;var i={};for(var s in t)hasOwnProperty.call(t,s)&&(i[s]=t[s]);i.originalType=e,i.mdxType="string"==typeof e?e:a,l[1]=i;for(var p=2;p<o;p++)l[p]=n[p];return r.createElement.apply(null,l)}return r.createElement.apply(null,n)}u.displayName="MDXCreateElement"},77406:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>s,contentTitle:()=>l,default:()=>m,frontMatter:()=>o,metadata:()=>i,toc:()=>p});var r=n(87462),a=(n(67294),n(3905));const o={title:"ols",description:"OpenBB SDK Function"},l="ols",i={unversionedId:"reference/econometrics/ols",id:"reference/econometrics/ols",title:"ols",description:"OpenBB SDK Function",source:"@site/content/sdk/reference/econometrics/ols.md",sourceDirName:"reference/econometrics",slug:"/reference/econometrics/ols",permalink:"/sdk/reference/econometrics/ols",draft:!1,editUrl:"https://github.com/OpenBB-finance/OpenBBTerminal/edit/main/website/content/sdk/reference/econometrics/ols.md",tags:[],version:"current",frontMatter:{title:"ols",description:"OpenBB SDK Function"},sidebar:"tutorialSidebar",previous:{title:"norm",permalink:"/sdk/reference/econometrics/norm"},next:{title:"options",permalink:"/sdk/reference/econometrics/options"}},s={},p=[{value:"Parameters",id:"parameters",level:2},{value:"Returns",id:"returns",level:2},{value:"Examples",id:"examples",level:2}],c={toc:p};function m(e){let{components:t,...n}=e;return(0,a.kt)("wrapper",(0,r.Z)({},c,n,{components:t,mdxType:"MDXLayout"}),(0,a.kt)("h1",{id:"ols"},"ols"),(0,a.kt)("p",null,"Performs an OLS regression on timeseries data. 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Variable: lwage R-squared (uncentered): 0.920\nModel: OLS Adj. R-squared (uncentered): 0.919\nMethod: Least Squares F-statistic: 1.659e+04\nDate: Thu, 10 Nov 2022 Prob (F-statistic): 0.00\nTime: 15:28:11 Log-Likelihood: -3091.3\nNo. Observations: 4360 AIC: 6189.\nDf Residuals: 4357 BIC: 6208.\nDf Model: 3\nCovariance Type: nonrobust\n==============================================================================\n coef std err t P>|t| [0.025 0.975]\n------------------------------------------------------------------------------\neduc 0.0986 0.002 39.879 0.000 0.094 0.103\nexper 0.1018 0.009 10.737 0.000 0.083 0.120\nexpersq -0.0034 0.001 -4.894 0.000 -0.005 -0.002\n==============================================================================\nOmnibus: 1249.642 Durbin-Watson: 0.954\nProb(Omnibus): 0.000 Jarque-Bera (JB): 9627.436\nSkew: -1.152 Prob(JB): 0.00\nKurtosis: 9.905 Cond. 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