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    <p>I don't know anything about the ols module you're using. But if you try the following with s<a href="http://statsmodels.sourceforge.net/" rel="nofollow">cikits.statsmodels</a>, it should work:</p> <pre><code>import numpy as np import scikits.statsmodels.api as sm a = np.array([[.001,.05,-.003,.014,.035,-.01,.032,-.0013,.0224,.005],[-.011,.012,.0013,.014,-.0015,.019,-.032,.013,-.04,-.05608], [.0021,.02,-.023,.0024,.025,-.081,.032,-.0513,.00014,-.00015],[.001,.02,-.003,.014,.035,-.001,.032,-.003,.0224,-.005], [.0021,-.002,-.023,.0024,.025,.01,.032,-.0513,.00014,-.00015],[-.0311,.012,.0013,.014,-.0015,.019,-.032,.013,-.014,-.008], [.001,.02,-.0203,.014,.035,-.001,.00032,-.0013,.0224,.05],[.0021,-.022,-.0213,.0024,.025,.081,.032,.05313,.00014,-.00015], [-.01331,.012,.0013,.014,.01015,.019,-.032,.013,-.014,-.012208],[.01021,-.022,-.023,.0024,.025,.081,.032,.0513,.00014,-.020015]]) y = a[:, 0] x = a[:, 1:] results = sm.OLS(y, x).fit() print results.summary() </code></pre> <p>The output:</p> <pre><code> Summary of Regression Results ======================================= | Dependent Variable: ['y']| | Model: OLS| | Method: Least Squares| | # obs: 10.0| | Df residuals: 1.0| | Df model: 8.0| ============================================================================== | coefficient std. error t-statistic prob. | ------------------------------------------------------------------------------ | x0 0.2557 0.6622 0.3862 0.7654 | | x1 0.03054 1.453 0.0210 0.9866 | | x2 -3.392 2.444 -1.3877 0.3975 | | x3 1.445 1.474 0.9808 0.5062 | | x4 0.03559 0.2610 0.1363 0.9137 | | x5 -0.7412 0.8754 -0.8467 0.5527 | | x6 0.02289 0.2466 0.0928 0.9411 | | x7 0.5754 1.413 0.4074 0.7537 | | x8 -0.4827 0.7569 -0.6378 0.6386 | ============================================================================== | Models stats Residual stats | ------------------------------------------------------------------------------ | R-squared: 0.8832 Durbin-Watson: 2.578 | | Adjusted R-squared: -0.05163 Omnibus: 0.5325 | | F-statistic: 0.9448 Prob(Omnibus): 0.7663 | | Prob (F-statistic): 0.6663 JB: 0.1630 | | Log likelihood: 41.45 Prob(JB): 0.9217 | | AIC criterion: -64.91 Skew: 0.4037 | | BIC criterion: -62.18 Kurtosis: 2.405 | ------------------------------------------------------------------------------ </code></pre>
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    1. COThis works. Thanks. Quick note: in my example, I set y = the first number in each sublist and x1 was second number in each sublist, etc. I think you have it set up so that y = the entire first sublist.
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    2. COIn your example, why does the regression fail when I try to regress against only the first x variable using this code: "results = sm.OLS(y, x[0]).fit()"?
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    3. COOh, thanks for pointing that out. I think the update I just made fixes that.
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