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 |
| 1.511 | 0.233 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.686 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.86 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 5.03e-05 |
| Time: | 22:52:13 | Log-Likelihood: | -99.770 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.6543 | 218.009 | 0.494 | 0.627 | -348.644 563.952 |
| C(dose)[T.1] | 321.6323 | 307.606 | 1.046 | 0.309 | -322.193 965.458 |
| expression | -5.9917 | 24.432 | -0.245 | 0.809 | -57.128 45.144 |
| expression:C(dose)[T.1] | -30.2522 | 34.555 | -0.875 | 0.392 | -102.576 42.072 |
| Omnibus: | 0.905 | Durbin-Watson: | 1.616 |
| Prob(Omnibus): | 0.636 | Jarque-Bera (JB): | 0.895 |
| Skew: | 0.335 | Prob(JB): | 0.639 |
| Kurtosis: | 2.303 | Cond. No. | 846. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.641 |
| Method: | Least Squares | F-statistic: | 20.65 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.37e-05 |
| Time: | 22:52:13 | Log-Likelihood: | -100.23 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 20 | BIC: | 209.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 242.5534 | 153.323 | 1.582 | 0.129 | -77.273 562.379 |
| C(dose)[T.1] | 52.4324 | 8.488 | 6.177 | 0.000 | 34.726 70.138 |
| expression | -21.1150 | 17.176 | -1.229 | 0.233 | -56.944 14.714 |
| Omnibus: | 1.073 | Durbin-Watson: | 1.642 |
| Prob(Omnibus): | 0.585 | Jarque-Bera (JB): | 0.832 |
| Skew: | 0.127 | Prob(JB): | 0.660 |
| Kurtosis: | 2.103 | Cond. No. | 328. |
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: | 22:52:13 | 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: | 0.051 |
| Model: | OLS | Adj. R-squared: | 0.006 |
| Method: | Least Squares | F-statistic: | 1.133 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.299 |
| Time: | 22:52:13 | Log-Likelihood: | -112.50 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 349.5015 | 253.519 | 1.379 | 0.183 | -177.721 876.724 |
| expression | -30.3147 | 28.476 | -1.065 | 0.299 | -89.534 28.905 |
| Omnibus: | 2.086 | Durbin-Watson: | 2.501 |
| Prob(Omnibus): | 0.352 | Jarque-Bera (JB): | 1.178 |
| Skew: | 0.195 | Prob(JB): | 0.555 |
| Kurtosis: | 1.962 | Cond. No. | 325. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.484 | 0.500 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.514 |
| Model: | OLS | Adj. R-squared: | 0.382 |
| Method: | Least Squares | F-statistic: | 3.882 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0408 |
| Time: | 22:52:13 | Log-Likelihood: | -69.884 |
| No. Observations: | 15 | AIC: | 147.8 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 173.6148 | 287.819 | 0.603 | 0.559 | -459.871 807.101 |
| C(dose)[T.1] | -322.1779 | 367.626 | -0.876 | 0.400 | -1131.318 486.962 |
| expression | -12.4689 | 33.771 | -0.369 | 0.719 | -86.799 61.861 |
| expression:C(dose)[T.1] | 42.5693 | 42.579 | 1.000 | 0.339 | -51.147 136.286 |
| Omnibus: | 0.911 | Durbin-Watson: | 1.253 |
| Prob(Omnibus): | 0.634 | Jarque-Bera (JB): | 0.807 |
| Skew: | -0.469 | Prob(JB): | 0.668 |
| Kurtosis: | 2.358 | Cond. No. | 592. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.470 |
| Model: | OLS | Adj. R-squared: | 0.382 |
| Method: | Least Squares | F-statistic: | 5.324 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0221 |
| Time: | 22:52:14 | Log-Likelihood: | -70.536 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -54.4374 | 175.518 | -0.310 | 0.762 | -436.859 327.984 |
| C(dose)[T.1] | 44.9887 | 16.574 | 2.714 | 0.019 | 8.877 81.101 |
| expression | 14.3101 | 20.568 | 0.696 | 0.500 | -30.503 59.123 |
| Omnibus: | 1.767 | Durbin-Watson: | 0.834 |
| Prob(Omnibus): | 0.413 | Jarque-Bera (JB): | 1.360 |
| Skew: | -0.673 | Prob(JB): | 0.507 |
| Kurtosis: | 2.395 | Cond. No. | 201. |
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: | 22:52:14 | 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: | 0.145 |
| Model: | OLS | Adj. R-squared: | 0.079 |
| Method: | Least Squares | F-statistic: | 2.202 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.162 |
| Time: | 22:52:14 | Log-Likelihood: | -74.127 |
| No. Observations: | 15 | AIC: | 152.3 |
| Df Residuals: | 13 | BIC: | 153.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -207.1187 | 202.936 | -1.021 | 0.326 | -645.536 231.298 |
| expression | 34.6811 | 23.374 | 1.484 | 0.162 | -15.815 85.177 |
| Omnibus: | 0.054 | Durbin-Watson: | 1.728 |
| Prob(Omnibus): | 0.973 | Jarque-Bera (JB): | 0.142 |
| Skew: | -0.093 | Prob(JB): | 0.932 |
| Kurtosis: | 2.561 | Cond. No. | 190. |