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 |
| 0.548 | 0.468 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.605 |
| Method: | Least Squares | F-statistic: | 12.25 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000109 |
| Time: | 04:18:07 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -175.0032 | 332.190 | -0.527 | 0.604 | -870.284 520.277 |
| C(dose)[T.1] | 156.2713 | 539.821 | 0.289 | 0.775 | -973.586 1286.129 |
| expression | 21.8784 | 31.702 | 0.690 | 0.498 | -44.475 88.232 |
| expression:C(dose)[T.1] | -10.3796 | 50.060 | -0.207 | 0.838 | -115.156 94.397 |
| Omnibus: | 0.183 | Durbin-Watson: | 2.044 |
| Prob(Omnibus): | 0.913 | Jarque-Bera (JB): | 0.392 |
| Skew: | 0.056 | Prob(JB): | 0.822 |
| Kurtosis: | 2.370 | Cond. No. | 1.64e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.27 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.16e-05 |
| Time: | 04:18:07 | Log-Likelihood: | -100.75 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -131.3914 | 250.889 | -0.524 | 0.606 | -654.738 391.955 |
| C(dose)[T.1] | 44.3876 | 14.871 | 2.985 | 0.007 | 13.368 75.407 |
| expression | 17.7157 | 23.941 | 0.740 | 0.468 | -32.224 67.655 |
| Omnibus: | 0.142 | Durbin-Watson: | 2.045 |
| Prob(Omnibus): | 0.931 | Jarque-Bera (JB): | 0.361 |
| Skew: | 0.043 | Prob(JB): | 0.835 |
| Kurtosis: | 2.393 | Cond. No. | 629. |
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: | 04:18:07 | 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: | 0.506 |
| Model: | OLS | Adj. R-squared: | 0.483 |
| Method: | Least Squares | F-statistic: | 21.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000141 |
| Time: | 04:18:07 | Log-Likelihood: | -104.99 |
| No. Observations: | 23 | AIC: | 214.0 |
| Df Residuals: | 21 | BIC: | 216.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -733.1063 | 175.248 | -4.183 | 0.000 | -1097.555 -368.657 |
| expression | 75.8358 | 16.344 | 4.640 | 0.000 | 41.847 109.824 |
| Omnibus: | 2.957 | Durbin-Watson: | 2.503 |
| Prob(Omnibus): | 0.228 | Jarque-Bera (JB): | 1.449 |
| Skew: | 0.256 | Prob(JB): | 0.485 |
| Kurtosis: | 1.882 | Cond. No. | 374. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 4.088 | 0.066 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.599 |
| Model: | OLS | Adj. R-squared: | 0.490 |
| Method: | Least Squares | F-statistic: | 5.479 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0150 |
| Time: | 04:18:07 | Log-Likelihood: | -68.445 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 11 | BIC: | 147.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -390.8909 | 366.435 | -1.067 | 0.309 | -1197.409 415.627 |
| C(dose)[T.1] | -268.0447 | 607.866 | -0.441 | 0.668 | -1605.949 1069.860 |
| expression | 43.9156 | 35.098 | 1.251 | 0.237 | -33.334 121.165 |
| expression:C(dose)[T.1] | 31.0290 | 58.545 | 0.530 | 0.607 | -97.827 159.885 |
| Omnibus: | 2.033 | Durbin-Watson: | 0.503 |
| Prob(Omnibus): | 0.362 | Jarque-Bera (JB): | 1.234 |
| Skew: | -0.420 | Prob(JB): | 0.540 |
| Kurtosis: | 1.873 | Cond. No. | 1.14e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.589 |
| Model: | OLS | Adj. R-squared: | 0.520 |
| Method: | Least Squares | F-statistic: | 8.593 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00483 |
| Time: | 04:18:07 | Log-Likelihood: | -68.634 |
| No. Observations: | 15 | AIC: | 143.3 |
| Df Residuals: | 12 | BIC: | 145.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -507.2751 | 284.425 | -1.784 | 0.100 | -1126.983 112.433 |
| C(dose)[T.1] | 54.0378 | 13.803 | 3.915 | 0.002 | 23.964 84.112 |
| expression | 55.0674 | 27.237 | 2.022 | 0.066 | -4.276 114.411 |
| Omnibus: | 2.300 | Durbin-Watson: | 0.491 |
| Prob(Omnibus): | 0.317 | Jarque-Bera (JB): | 1.557 |
| Skew: | -0.590 | Prob(JB): | 0.459 |
| Kurtosis: | 1.951 | Cond. No. | 440. |
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: | 04:18:07 | 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: | 0.064 |
| Model: | OLS | Adj. R-squared: | -0.008 |
| Method: | Least Squares | F-statistic: | 0.8842 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.364 |
| Time: | 04:18:07 | Log-Likelihood: | -74.807 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -286.2684 | 404.168 | -0.708 | 0.491 | -1159.419 586.883 |
| expression | 36.5692 | 38.890 | 0.940 | 0.364 | -47.448 120.586 |
| Omnibus: | 2.217 | Durbin-Watson: | 1.816 |
| Prob(Omnibus): | 0.330 | Jarque-Bera (JB): | 1.059 |
| Skew: | 0.207 | Prob(JB): | 0.589 |
| Kurtosis: | 1.766 | Cond. No. | 431. |