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 |
| 0.032 | 0.859 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 12.68 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.81e-05 |
| Time: | 04:54:10 | Log-Likelihood: | -100.47 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -15.0620 | 81.301 | -0.185 | 0.855 | -185.228 155.104 |
| C(dose)[T.1] | 158.3244 | 105.583 | 1.500 | 0.150 | -62.663 379.312 |
| expression | 13.1595 | 15.402 | 0.854 | 0.404 | -19.077 45.397 |
| expression:C(dose)[T.1] | -20.5966 | 20.790 | -0.991 | 0.334 | -64.111 22.918 |
| Omnibus: | 1.432 | Durbin-Watson: | 1.992 |
| Prob(Omnibus): | 0.489 | Jarque-Bera (JB): | 0.939 |
| Skew: | 0.109 | Prob(JB): | 0.625 |
| Kurtosis: | 2.034 | Cond. No. | 168. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.54 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.79e-05 |
| Time: | 04:54:10 | Log-Likelihood: | -101.04 |
| 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 | 44.4422 | 54.768 | 0.811 | 0.427 | -69.802 158.686 |
| C(dose)[T.1] | 54.1930 | 9.977 | 5.432 | 0.000 | 33.381 75.005 |
| expression | 1.8553 | 10.341 | 0.179 | 0.859 | -19.715 23.425 |
| Omnibus: | 0.189 | Durbin-Watson: | 1.880 |
| Prob(Omnibus): | 0.910 | Jarque-Bera (JB): | 0.398 |
| Skew: | 0.001 | Prob(JB): | 0.820 |
| Kurtosis: | 2.356 | Cond. No. | 66.3 |
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: | 04:54:10 | 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: | 0.133 |
| Model: | OLS | Adj. R-squared: | 0.091 |
| Method: | Least Squares | F-statistic: | 3.215 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0874 |
| Time: | 04:54:10 | Log-Likelihood: | -111.47 |
| No. Observations: | 23 | AIC: | 226.9 |
| Df Residuals: | 21 | BIC: | 229.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 205.8009 | 70.643 | 2.913 | 0.008 | 58.890 352.712 |
| expression | -25.0004 | 13.944 | -1.793 | 0.087 | -53.998 3.998 |
| Omnibus: | 0.528 | Durbin-Watson: | 2.557 |
| Prob(Omnibus): | 0.768 | Jarque-Bera (JB): | 0.627 |
| Skew: | 0.199 | Prob(JB): | 0.731 |
| Kurtosis: | 2.296 | Cond. No. | 55.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.918 | 0.357 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.615 |
| Model: | OLS | Adj. R-squared: | 0.510 |
| Method: | Least Squares | F-statistic: | 5.856 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0122 |
| Time: | 04:54:11 | Log-Likelihood: | -68.142 |
| No. Observations: | 15 | AIC: | 144.3 |
| Df Residuals: | 11 | BIC: | 147.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 143.5454 | 150.292 | 0.955 | 0.360 | -187.246 474.337 |
| C(dose)[T.1] | -377.9466 | 223.867 | -1.688 | 0.119 | -870.674 114.781 |
| expression | -15.3825 | 30.305 | -0.508 | 0.622 | -82.083 51.318 |
| expression:C(dose)[T.1] | 85.4363 | 44.847 | 1.905 | 0.083 | -13.272 184.145 |
| Omnibus: | 7.498 | Durbin-Watson: | 1.378 |
| Prob(Omnibus): | 0.024 | Jarque-Bera (JB): | 4.148 |
| Skew: | -1.156 | Prob(JB): | 0.126 |
| Kurtosis: | 4.137 | Cond. No. | 221. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.488 |
| Model: | OLS | Adj. R-squared: | 0.403 |
| Method: | Least Squares | F-statistic: | 5.717 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0180 |
| Time: | 04:54:11 | Log-Likelihood: | -70.280 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -49.4963 | 122.552 | -0.404 | 0.693 | -316.514 217.521 |
| C(dose)[T.1] | 47.7183 | 15.248 | 3.129 | 0.009 | 14.495 80.942 |
| expression | 23.6295 | 24.665 | 0.958 | 0.357 | -30.111 77.370 |
| Omnibus: | 4.864 | Durbin-Watson: | 0.764 |
| Prob(Omnibus): | 0.088 | Jarque-Bera (JB): | 2.992 |
| Skew: | -1.093 | Prob(JB): | 0.224 |
| Kurtosis: | 3.093 | Cond. No. | 84.5 |
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: | 04:54:11 | 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: | 0.070 |
| Model: | OLS | Adj. R-squared: | -0.001 |
| Method: | Least Squares | F-statistic: | 0.9792 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.340 |
| Time: | 04:54:11 | Log-Likelihood: | -74.755 |
| No. Observations: | 15 | AIC: | 153.5 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -62.9540 | 158.577 | -0.397 | 0.698 | -405.538 279.630 |
| expression | 31.4397 | 31.772 | 0.990 | 0.340 | -37.199 100.078 |
| Omnibus: | 0.764 | Durbin-Watson: | 1.934 |
| Prob(Omnibus): | 0.682 | Jarque-Bera (JB): | 0.620 |
| Skew: | -0.435 | Prob(JB): | 0.733 |
| Kurtosis: | 2.514 | Cond. No. | 84.0 |