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Needs OLS to be run in advance with independent and dependent variables"),(0,a.kt)("h3",{id:"usage"},"Usage"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python",metastring:"wordwrap",wordwrap:!0},"dwat [-p]\n")),(0,a.kt)("hr",null),(0,a.kt)("h2",{id:"parameters"},"Parameters"),(0,a.kt)("table",null,(0,a.kt)("thead",{parentName:"table"},(0,a.kt)("tr",{parentName:"thead"},(0,a.kt)("th",{parentName:"tr",align:null},"Name"),(0,a.kt)("th",{parentName:"tr",align:null},"Description"),(0,a.kt)("th",{parentName:"tr",align:null},"Default"),(0,a.kt)("th",{parentName:"tr",align:null},"Optional"),(0,a.kt)("th",{parentName:"tr",align:null},"Choices"))),(0,a.kt)("tbody",{parentName:"table"},(0,a.kt)("tr",{parentName:"tbody"},(0,a.kt)("td",{parentName:"tr",align:null},"plot"),(0,a.kt)("td",{parentName:"tr",align:null},"Plot the residuals"),(0,a.kt)("td",{parentName:"tr",align:null},"False"),(0,a.kt)("td",{parentName:"tr",align:null},"True"),(0,a.kt)("td",{parentName:"tr",align:null},"None")))),(0,a.kt)("hr",null),(0,a.kt)("h2",{id:"examples"},"Examples"),(0,a.kt)("pre",null,(0,a.kt)("code",{parentName:"pre",className:"language-python"},"2022 Feb 24, 05:59 (\ud83e\udd8b) /econometrics/ $ ols -d adj_close-msft -i adj_close-aapl -i adj_close-googl -i adj_close-tsla\n OLS Regression Results\n==============================================================================\nDep. Variable: adj_close_msft R-squared: 0.977\nModel: OLS Adj. R-squared: 0.977\nMethod: Least Squares F-statistic: 1.068e+04\nDate: Thu, 24 Feb 2022 Prob (F-statistic): 0.00\nTime: 12:00:01 Log-Likelihood: -2830.6\nNo. Observations: 759 AIC: 5669.\nDf Residuals: 755 BIC: 5688.\nDf Model: 3\nCovariance Type: nonrobust\n===================================================================================\n coef std err t P|t| [0.025 0.975]\n-----------------------------------------------------------------------------------\nIntercept 27.7984 2.166 12.832 0.000 23.546 32.051\nadj_close_aapl 0.8662 0.034 25.503 0.000 0.800 0.933\nadj_close_googl 0.0508 0.002 30.374 0.000 0.048 0.054\nadj_close_tsla -0.0007 0.004 -0.181 0.856 -0.009 0.007\n==============================================================================\nOmnibus: 41.445 Durbin-Watson: 0.044\nProb(Omnibus): 0.000 Jarque-Bera (JB): 47.398\nSkew: 0.612 Prob(JB): 5.10e-11\nKurtosis: 2.995 Cond. 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