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 |
| 0.011 | 0.917 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 13.53 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 5.86e-05 |
| Time: | 18:28:52 | Log-Likelihood: | -99.960 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 103.6543 | 58.575 | 1.770 | 0.093 | -18.944 226.253 |
| C(dose)[T.1] | -82.3240 | 98.493 | -0.836 | 0.414 | -288.473 123.825 |
| expression | -8.7638 | 10.328 | -0.849 | 0.407 | -30.382 12.854 |
| expression:C(dose)[T.1] | 25.9028 | 18.787 | 1.379 | 0.184 | -13.418 65.224 |
| Omnibus: | 0.365 | Durbin-Watson: | 1.765 |
| Prob(Omnibus): | 0.833 | Jarque-Bera (JB): | 0.393 |
| Skew: | 0.257 | Prob(JB): | 0.821 |
| Kurtosis: | 2.617 | Cond. No. | 152. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.51 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.82e-05 |
| Time: | 18:28:53 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 59.4817 | 50.129 | 1.187 | 0.249 | -45.086 164.049 |
| C(dose)[T.1] | 52.7654 | 10.294 | 5.126 | 0.000 | 31.292 74.239 |
| expression | -0.9346 | 8.820 | -0.106 | 0.917 | -19.332 17.463 |
| Omnibus: | 0.256 | Durbin-Watson: | 1.863 |
| Prob(Omnibus): | 0.880 | Jarque-Bera (JB): | 0.444 |
| Skew: | 0.078 | Prob(JB): | 0.801 |
| Kurtosis: | 2.338 | Cond. No. | 64.3 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 18:28:53 | 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: | 0.189 |
| Model: | OLS | Adj. R-squared: | 0.150 |
| Method: | Least Squares | F-statistic: | 4.878 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0384 |
| Time: | 18:28:53 | Log-Likelihood: | -110.70 |
| No. Observations: | 23 | AIC: | 225.4 |
| Df Residuals: | 21 | BIC: | 227.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 211.4569 | 60.001 | 3.524 | 0.002 | 86.679 336.235 |
| expression | -24.6265 | 11.150 | -2.209 | 0.038 | -47.814 -1.439 |
| Omnibus: | 2.027 | Durbin-Watson: | 2.063 |
| Prob(Omnibus): | 0.363 | Jarque-Bera (JB): | 1.712 |
| Skew: | 0.624 | Prob(JB): | 0.425 |
| Kurtosis: | 2.519 | Cond. No. | 51.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.108 | 0.313 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.524 |
| Model: | OLS | Adj. R-squared: | 0.394 |
| Method: | Least Squares | F-statistic: | 4.040 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0366 |
| Time: | 18:28:53 | Log-Likelihood: | -69.729 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 220.2326 | 116.453 | 1.891 | 0.085 | -36.079 476.545 |
| C(dose)[T.1] | -92.3931 | 175.835 | -0.525 | 0.610 | -479.403 294.617 |
| expression | -27.4252 | 20.805 | -1.318 | 0.214 | -73.217 18.366 |
| expression:C(dose)[T.1] | 25.4470 | 31.138 | 0.817 | 0.431 | -43.086 93.980 |
| Omnibus: | 2.763 | Durbin-Watson: | 0.740 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.528 |
| Skew: | -0.782 | Prob(JB): | 0.466 |
| Kurtosis: | 2.976 | Cond. No. | 174. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.411 |
| Method: | Least Squares | F-statistic: | 5.890 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0165 |
| Time: | 18:28:53 | Log-Likelihood: | -70.171 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.9358 | 85.751 | 1.830 | 0.092 | -29.900 343.772 |
| C(dose)[T.1] | 50.7581 | 15.133 | 3.354 | 0.006 | 17.787 83.730 |
| expression | -16.0647 | 15.264 | -1.052 | 0.313 | -49.321 17.192 |
| Omnibus: | 4.474 | Durbin-Watson: | 0.746 |
| Prob(Omnibus): | 0.107 | Jarque-Bera (JB): | 2.186 |
| Skew: | -0.895 | Prob(JB): | 0.335 |
| Kurtosis: | 3.542 | Cond. No. | 66.7 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 18:28:53 | 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: | 0.022 |
| Model: | OLS | Adj. R-squared: | -0.053 |
| Method: | Least Squares | F-statistic: | 0.2956 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.596 |
| Time: | 18:28:53 | Log-Likelihood: | -75.131 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.7780 | 114.678 | 1.358 | 0.197 | -91.970 403.526 |
| expression | -11.0449 | 20.314 | -0.544 | 0.596 | -54.931 32.841 |
| Omnibus: | 2.125 | Durbin-Watson: | 1.765 |
| Prob(Omnibus): | 0.346 | Jarque-Bera (JB): | 1.036 |
| Skew: | 0.200 | Prob(JB): | 0.596 |
| Kurtosis: | 1.776 | Cond. No. | 66.4 |