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 |
| 1.969 | 0.176 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.692 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 14.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.28e-05 |
| Time: | 04:46:07 | Log-Likelihood: | -99.572 |
| No. Observations: | 23 | AIC: | 207.1 |
| Df Residuals: | 19 | BIC: | 211.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 39.2865 | 73.885 | 0.532 | 0.601 | -115.357 193.930 |
| C(dose)[T.1] | -26.4617 | 94.663 | -0.280 | 0.783 | -224.594 171.671 |
| expression | 3.5386 | 17.467 | 0.203 | 0.842 | -33.020 40.097 |
| expression:C(dose)[T.1] | 18.4415 | 22.174 | 0.832 | 0.416 | -27.969 64.852 |
| Omnibus: | 1.566 | Durbin-Watson: | 1.571 |
| Prob(Omnibus): | 0.457 | Jarque-Bera (JB): | 1.155 |
| Skew: | 0.308 | Prob(JB): | 0.561 |
| Kurtosis: | 2.091 | Cond. No. | 139. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.649 |
| Method: | Least Squares | F-statistic: | 21.30 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.11e-05 |
| Time: | 04:46:07 | Log-Likelihood: | -99.983 |
| No. Observations: | 23 | AIC: | 206.0 |
| Df Residuals: | 20 | BIC: | 209.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -8.9674 | 45.393 | -0.198 | 0.845 | -103.655 85.720 |
| C(dose)[T.1] | 51.9507 | 8.426 | 6.166 | 0.000 | 34.375 69.527 |
| expression | 14.9818 | 10.677 | 1.403 | 0.176 | -7.290 37.253 |
| Omnibus: | 1.011 | Durbin-Watson: | 1.438 |
| Prob(Omnibus): | 0.603 | Jarque-Bera (JB): | 0.843 |
| Skew: | 0.186 | Prob(JB): | 0.656 |
| Kurtosis: | 2.139 | Cond. No. | 49.3 |
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: | 04:46:07 | 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: | 0.073 |
| Model: | OLS | Adj. R-squared: | 0.029 |
| Method: | Least Squares | F-statistic: | 1.659 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.212 |
| Time: | 04:46:07 | Log-Likelihood: | -112.23 |
| No. Observations: | 23 | AIC: | 228.5 |
| Df Residuals: | 21 | BIC: | 230.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -17.0138 | 75.417 | -0.226 | 0.824 | -173.852 139.825 |
| expression | 22.7011 | 17.624 | 1.288 | 0.212 | -13.950 59.352 |
| Omnibus: | 1.952 | Durbin-Watson: | 2.203 |
| Prob(Omnibus): | 0.377 | Jarque-Bera (JB): | 1.142 |
| Skew: | 0.193 | Prob(JB): | 0.565 |
| Kurtosis: | 1.979 | Cond. No. | 49.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.138 | 0.716 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.311 |
| Method: | Least Squares | F-statistic: | 3.111 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0707 |
| Time: | 04:46:07 | Log-Likelihood: | -70.693 |
| No. Observations: | 15 | AIC: | 149.4 |
| Df Residuals: | 11 | BIC: | 152.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.1173 | 55.097 | 0.801 | 0.440 | -77.150 165.385 |
| C(dose)[T.1] | 92.9002 | 154.791 | 0.600 | 0.561 | -247.793 433.594 |
| expression | 5.0647 | 11.688 | 0.433 | 0.673 | -20.661 30.791 |
| expression:C(dose)[T.1] | -9.5309 | 33.680 | -0.283 | 0.782 | -83.660 64.598 |
| Omnibus: | 3.038 | Durbin-Watson: | 0.841 |
| Prob(Omnibus): | 0.219 | Jarque-Bera (JB): | 2.005 |
| Skew: | -0.885 | Prob(JB): | 0.367 |
| Kurtosis: | 2.723 | Cond. No. | 108. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.010 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0262 |
| Time: | 04:46:07 | Log-Likelihood: | -70.747 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 49.4007 | 49.811 | 0.992 | 0.341 | -59.127 157.929 |
| C(dose)[T.1] | 49.3399 | 15.654 | 3.152 | 0.008 | 15.232 83.448 |
| expression | 3.9168 | 10.533 | 0.372 | 0.716 | -19.033 26.867 |
| Omnibus: | 2.724 | Durbin-Watson: | 0.871 |
| Prob(Omnibus): | 0.256 | Jarque-Bera (JB): | 1.866 |
| Skew: | -0.844 | Prob(JB): | 0.393 |
| Kurtosis: | 2.630 | Cond. No. | 31.2 |
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: | 04:46:07 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.05131 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.824 |
| Time: | 04:46:07 | Log-Likelihood: | -75.271 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 79.4664 | 63.503 | 1.251 | 0.233 | -57.724 216.656 |
| expression | 3.0984 | 13.678 | 0.227 | 0.824 | -26.451 32.648 |
| Omnibus: | 0.563 | Durbin-Watson: | 1.680 |
| Prob(Omnibus): | 0.755 | Jarque-Bera (JB): | 0.569 |
| Skew: | 0.071 | Prob(JB): | 0.753 |
| Kurtosis: | 2.057 | Cond. No. | 30.4 |