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 |
| 0.407 | 0.531 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.610 |
| Method: | Least Squares | F-statistic: | 12.45 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 9.86e-05 |
| Time: | 23:00:01 | Log-Likelihood: | -100.60 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -95.5299 | 207.370 | -0.461 | 0.650 | -529.561 338.501 |
| C(dose)[T.1] | 842.2859 | 1293.368 | 0.651 | 0.523 | -1864.765 3549.337 |
| expression | 13.0607 | 18.080 | 0.722 | 0.479 | -24.781 50.902 |
| expression:C(dose)[T.1] | -66.3314 | 107.916 | -0.615 | 0.546 | -292.203 159.540 |
| Omnibus: | 0.145 | Durbin-Watson: | 1.707 |
| Prob(Omnibus): | 0.930 | Jarque-Bera (JB): | 0.364 |
| Skew: | -0.033 | Prob(JB): | 0.834 |
| Kurtosis: | 2.388 | Cond. No. | 3.97e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.08 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.32e-05 |
| Time: | 23:00:01 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -74.1850 | 201.237 | -0.369 | 0.716 | -493.958 345.588 |
| C(dose)[T.1] | 47.3513 | 12.779 | 3.705 | 0.001 | 20.694 74.009 |
| expression | 11.1989 | 17.545 | 0.638 | 0.531 | -25.399 47.797 |
| Omnibus: | 0.450 | Durbin-Watson: | 1.799 |
| Prob(Omnibus): | 0.799 | Jarque-Bera (JB): | 0.552 |
| Skew: | 0.019 | Prob(JB): | 0.759 |
| Kurtosis: | 2.242 | Cond. No. | 549. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:00:02 | 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: | 0.420 |
| Model: | OLS | Adj. R-squared: | 0.392 |
| Method: | Least Squares | F-statistic: | 15.20 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000826 |
| Time: | 23:00:02 | Log-Likelihood: | -106.84 |
| No. Observations: | 23 | AIC: | 217.7 |
| Df Residuals: | 21 | BIC: | 220.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -610.6408 | 177.130 | -3.447 | 0.002 | -979.003 -242.279 |
| expression | 58.9021 | 15.106 | 3.899 | 0.001 | 27.488 90.316 |
| Omnibus: | 3.069 | Durbin-Watson: | 1.780 |
| Prob(Omnibus): | 0.216 | Jarque-Bera (JB): | 1.651 |
| Skew: | 0.363 | Prob(JB): | 0.438 |
| Kurtosis: | 1.907 | Cond. No. | 381. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 6.377 | 0.027 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.564 |
| Method: | Least Squares | F-statistic: | 7.028 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00659 |
| Time: | 23:00:02 | Log-Likelihood: | -67.272 |
| No. Observations: | 15 | AIC: | 142.5 |
| Df Residuals: | 11 | BIC: | 145.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -534.5476 | 625.446 | -0.855 | 0.411 | -1911.144 842.049 |
| C(dose)[T.1] | -545.2025 | 799.870 | -0.682 | 0.510 | -2305.705 1215.300 |
| expression | 49.1469 | 51.057 | 0.963 | 0.356 | -63.229 161.523 |
| expression:C(dose)[T.1] | 48.3292 | 65.243 | 0.741 | 0.474 | -95.270 191.929 |
| Omnibus: | 1.669 | Durbin-Watson: | 0.995 |
| Prob(Omnibus): | 0.434 | Jarque-Bera (JB): | 1.240 |
| Skew: | -0.509 | Prob(JB): | 0.538 |
| Kurtosis: | 2.028 | Cond. No. | 2.13e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.640 |
| Model: | OLS | Adj. R-squared: | 0.580 |
| Method: | Least Squares | F-statistic: | 10.67 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00217 |
| Time: | 23:00:02 | Log-Likelihood: | -67.637 |
| No. Observations: | 15 | AIC: | 141.3 |
| Df Residuals: | 12 | BIC: | 143.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -897.0703 | 382.059 | -2.348 | 0.037 | -1729.505 -64.635 |
| C(dose)[T.1] | 47.2263 | 12.743 | 3.706 | 0.003 | 19.462 74.991 |
| expression | 78.7442 | 31.183 | 2.525 | 0.027 | 10.802 146.686 |
| Omnibus: | 3.289 | Durbin-Watson: | 1.090 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 1.281 |
| Skew: | -0.247 | Prob(JB): | 0.527 |
| Kurtosis: | 1.657 | Cond. No. | 744. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:00:02 | 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: | 0.228 |
| Model: | OLS | Adj. R-squared: | 0.169 |
| Method: | Least Squares | F-statistic: | 3.840 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0718 |
| Time: | 23:00:02 | Log-Likelihood: | -73.359 |
| No. Observations: | 15 | AIC: | 150.7 |
| Df Residuals: | 13 | BIC: | 152.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -958.6444 | 537.046 | -1.785 | 0.098 | -2118.861 201.572 |
| expression | 85.8200 | 43.792 | 1.960 | 0.072 | -8.787 180.427 |
| Omnibus: | 1.445 | Durbin-Watson: | 2.035 |
| Prob(Omnibus): | 0.485 | Jarque-Bera (JB): | 0.827 |
| Skew: | 0.049 | Prob(JB): | 0.661 |
| Kurtosis: | 1.854 | Cond. No. | 743. |