AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots
CP73
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.728 | 0.404 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.693 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 14.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.12e-05 |
| Time: | 05:21:02 | Log-Likelihood: | -99.523 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 19 | BIC: | 211.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 119.1895 | 485.113 | 0.246 | 0.809 | -896.164 1134.543 |
| C(dose)[T.1] | -1119.2251 | 830.143 | -1.348 | 0.193 | -2856.734 618.284 |
| expression | -5.6449 | 42.139 | -0.134 | 0.895 | -93.843 82.553 |
| expression:C(dose)[T.1] | 98.8335 | 70.625 | 1.399 | 0.178 | -48.987 246.654 |
| Omnibus: | 0.618 | Durbin-Watson: | 1.696 |
| Prob(Omnibus): | 0.734 | Jarque-Bera (JB): | 0.166 |
| Skew: | -0.207 | Prob(JB): | 0.920 |
| Kurtosis: | 3.035 | Cond. No. | 2.84e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.98e-05 |
| Time: | 05:21:03 | Log-Likelihood: | -100.65 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -285.8347 | 398.536 | -0.717 | 0.482 | -1117.165 545.496 |
| C(dose)[T.1] | 42.2899 | 15.550 | 2.720 | 0.013 | 9.853 74.727 |
| expression | 29.5397 | 34.617 | 0.853 | 0.404 | -42.670 101.750 |
| Omnibus: | 0.018 | Durbin-Watson: | 2.122 |
| Prob(Omnibus): | 0.991 | Jarque-Bera (JB): | 0.191 |
| Skew: | 0.053 | Prob(JB): | 0.909 |
| Kurtosis: | 2.566 | Cond. No. | 1.09e+03 |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 38.84 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 05:21:03 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 21 | BIC: | 208.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.2083 | 5.919 | 9.159 | 0.000 | 41.900 66.517 |
| C(dose)[T.1] | 53.3371 | 8.558 | 6.232 | 0.000 | 35.539 71.135 |
| Omnibus: | 0.322 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.060 | Prob(JB): | 0.785 |
| Kurtosis: | 2.299 | Cond. No. | 2.57 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.536 |
| Model: | OLS | Adj. R-squared: | 0.514 |
| Method: | Least Squares | F-statistic: | 24.27 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.12e-05 |
| Time: | 05:21:03 | Log-Likelihood: | -104.27 |
| No. Observations: | 23 | AIC: | 212.5 |
| Df Residuals: | 21 | BIC: | 214.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1181.8657 | 256.106 | -4.615 | 0.000 | -1714.468 -649.264 |
| expression | 107.9174 | 21.904 | 4.927 | 0.000 | 62.366 153.469 |
| Omnibus: | 0.284 | Durbin-Watson: | 2.413 |
| Prob(Omnibus): | 0.867 | Jarque-Bera (JB): | 0.461 |
| Skew: | -0.001 | Prob(JB): | 0.794 |
| Kurtosis: | 2.307 | Cond. No. | 614. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.131 | 0.308 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.516 |
| Model: | OLS | Adj. R-squared: | 0.384 |
| Method: | Least Squares | F-statistic: | 3.912 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0399 |
| Time: | 05:21:03 | Log-Likelihood: | -69.854 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1434.1477 | 1229.279 | -1.167 | 0.268 | -4139.773 1271.478 |
| C(dose)[T.1] | 1275.4850 | 1832.592 | 0.696 | 0.501 | -2758.023 5308.993 |
| expression | 115.7083 | 94.722 | 1.222 | 0.247 | -92.773 324.189 |
| expression:C(dose)[T.1] | -94.6682 | 140.579 | -0.673 | 0.515 | -404.079 214.743 |
| Omnibus: | 2.060 | Durbin-Watson: | 0.682 |
| Prob(Omnibus): | 0.357 | Jarque-Bera (JB): | 1.479 |
| Skew: | -0.732 | Prob(JB): | 0.477 |
| Kurtosis: | 2.527 | Cond. No. | 4.07e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.412 |
| Method: | Least Squares | F-statistic: | 5.911 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0163 |
| Time: | 05:21:03 | Log-Likelihood: | -70.157 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -876.3869 | 887.438 | -0.988 | 0.343 | -2809.949 1057.175 |
| C(dose)[T.1] | 41.4375 | 16.722 | 2.478 | 0.029 | 5.004 77.871 |
| expression | 72.7284 | 68.379 | 1.064 | 0.308 | -76.256 221.713 |
| Omnibus: | 2.016 | Durbin-Watson: | 0.655 |
| Prob(Omnibus): | 0.365 | Jarque-Bera (JB): | 1.506 |
| Skew: | -0.620 | Prob(JB): | 0.471 |
| Kurtosis: | 2.067 | Cond. No. | 1.55e+03 |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.406 |
| Method: | Least Squares | F-statistic: | 10.58 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 05:21:03 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 13 | BIC: | 147.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.4286 | 11.044 | 6.106 | 0.000 | 43.570 91.287 |
| C(dose)[T.1] | 49.1964 | 15.122 | 3.253 | 0.006 | 16.527 81.866 |
| Omnibus: | 2.713 | Durbin-Watson: | 0.810 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.843 | Prob(JB): | 0.393 |
| Kurtosis: | 2.619 | Cond. No. | 2.70 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.238 |
| Model: | OLS | Adj. R-squared: | 0.180 |
| Method: | Least Squares | F-statistic: | 4.071 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0648 |
| Time: | 05:21:03 | Log-Likelihood: | -73.257 |
| No. Observations: | 15 | AIC: | 150.5 |
| Df Residuals: | 13 | BIC: | 151.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -1817.8211 | 947.410 | -1.919 | 0.077 | -3864.576 228.933 |
| expression | 146.6522 | 72.684 | 2.018 | 0.065 | -10.371 303.675 |
| Omnibus: | 1.976 | Durbin-Watson: | 1.350 |
| Prob(Omnibus): | 0.372 | Jarque-Bera (JB): | 1.294 |
| Skew: | 0.476 | Prob(JB): | 0.524 |
| Kurtosis: | 1.922 | Cond. No. | 1.40e+03 |