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 |
| 0.356 | 0.558 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.77 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.40e-05 |
| Time: | 05:05:44 | Log-Likelihood: | -100.41 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.6170 | 84.879 | 1.079 | 0.294 | -86.036 269.270 |
| C(dose)[T.1] | -33.2285 | 99.318 | -0.335 | 0.742 | -241.102 174.646 |
| expression | -6.7227 | 15.215 | -0.442 | 0.664 | -38.568 25.122 |
| expression:C(dose)[T.1] | 15.5630 | 17.782 | 0.875 | 0.392 | -21.656 52.782 |
| Omnibus: | 0.451 | Durbin-Watson: | 1.860 |
| Prob(Omnibus): | 0.798 | Jarque-Bera (JB): | 0.269 |
| Skew: | 0.249 | Prob(JB): | 0.874 |
| Kurtosis: | 2.817 | Cond. No. | 189. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.00 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.38e-05 |
| Time: | 05:05:44 | Log-Likelihood: | -100.86 |
| 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 | 28.2200 | 43.979 | 0.642 | 0.528 | -63.520 119.960 |
| C(dose)[T.1] | 53.3556 | 8.693 | 6.138 | 0.000 | 35.223 71.489 |
| expression | 4.6704 | 7.829 | 0.597 | 0.558 | -11.661 21.002 |
| Omnibus: | 0.160 | Durbin-Watson: | 1.920 |
| Prob(Omnibus): | 0.923 | Jarque-Bera (JB): | 0.349 |
| Skew: | 0.139 | Prob(JB): | 0.840 |
| Kurtosis: | 2.464 | Cond. No. | 58.6 |
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: | 05:05:44 | 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: | 0.006 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.1202 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.732 |
| Time: | 05:05:44 | Log-Likelihood: | -113.04 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.6928 | 72.532 | 0.754 | 0.459 | -96.145 205.531 |
| expression | 4.4987 | 12.975 | 0.347 | 0.732 | -22.484 31.481 |
| Omnibus: | 2.703 | Durbin-Watson: | 2.552 |
| Prob(Omnibus): | 0.259 | Jarque-Bera (JB): | 1.402 |
| Skew: | 0.262 | Prob(JB): | 0.496 |
| Kurtosis: | 1.910 | Cond. No. | 58.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.418 | 0.089 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.573 |
| Model: | OLS | Adj. R-squared: | 0.457 |
| Method: | Least Squares | F-statistic: | 4.920 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0209 |
| Time: | 05:05:44 | Log-Likelihood: | -68.918 |
| No. Observations: | 15 | AIC: | 145.8 |
| Df Residuals: | 11 | BIC: | 148.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 204.0302 | 79.041 | 2.581 | 0.026 | 30.062 377.999 |
| C(dose)[T.1] | -6.8011 | 217.138 | -0.031 | 0.976 | -484.719 471.116 |
| expression | -18.6289 | 10.682 | -1.744 | 0.109 | -42.141 4.883 |
| expression:C(dose)[T.1] | 7.0666 | 30.882 | 0.229 | 0.823 | -60.905 75.038 |
| Omnibus: | 3.373 | Durbin-Watson: | 1.280 |
| Prob(Omnibus): | 0.185 | Jarque-Bera (JB): | 1.843 |
| Skew: | -0.857 | Prob(JB): | 0.398 |
| Kurtosis: | 3.089 | Cond. No. | 255. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.571 |
| Model: | OLS | Adj. R-squared: | 0.499 |
| Method: | Least Squares | F-statistic: | 7.985 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00624 |
| Time: | 05:05:44 | Log-Likelihood: | -68.953 |
| No. Observations: | 15 | AIC: | 143.9 |
| Df Residuals: | 12 | BIC: | 146.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 197.8301 | 71.260 | 2.776 | 0.017 | 42.569 353.092 |
| C(dose)[T.1] | 42.7678 | 14.315 | 2.988 | 0.011 | 11.579 73.957 |
| expression | -17.7834 | 9.619 | -1.849 | 0.089 | -38.742 3.175 |
| Omnibus: | 3.044 | Durbin-Watson: | 1.221 |
| Prob(Omnibus): | 0.218 | Jarque-Bera (JB): | 1.737 |
| Skew: | -0.833 | Prob(JB): | 0.420 |
| Kurtosis: | 2.970 | Cond. No. | 75.6 |
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: | 05:05:44 | 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: | 0.252 |
| Model: | OLS | Adj. R-squared: | 0.194 |
| Method: | Least Squares | F-statistic: | 4.376 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0566 |
| Time: | 05:05:44 | Log-Likelihood: | -73.124 |
| No. Observations: | 15 | AIC: | 150.2 |
| Df Residuals: | 13 | BIC: | 151.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 270.4850 | 84.983 | 3.183 | 0.007 | 86.890 454.080 |
| expression | -24.7646 | 11.839 | -2.092 | 0.057 | -50.340 0.811 |
| Omnibus: | 4.544 | Durbin-Watson: | 2.114 |
| Prob(Omnibus): | 0.103 | Jarque-Bera (JB): | 1.759 |
| Skew: | 0.462 | Prob(JB): | 0.415 |
| Kurtosis: | 1.601 | Cond. No. | 70.7 |