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 |
| 0.525 | 0.477 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.726 |
| Model: | OLS | Adj. R-squared: | 0.683 |
| Method: | Least Squares | F-statistic: | 16.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.43e-05 |
| Time: | 04:51:35 | Log-Likelihood: | -98.218 |
| No. Observations: | 23 | AIC: | 204.4 |
| Df Residuals: | 19 | BIC: | 209.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 238.4384 | 201.210 | 1.185 | 0.251 | -182.699 659.576 |
| C(dose)[T.1] | -589.7660 | 295.613 | -1.995 | 0.061 | -1208.491 28.959 |
| expression | -21.2773 | 23.230 | -0.916 | 0.371 | -69.898 27.343 |
| expression:C(dose)[T.1] | 73.4352 | 33.840 | 2.170 | 0.043 | 2.607 144.263 |
| Omnibus: | 0.509 | Durbin-Watson: | 1.588 |
| Prob(Omnibus): | 0.775 | Jarque-Bera (JB): | 0.569 |
| Skew: | 0.298 | Prob(JB): | 0.752 |
| Kurtosis: | 2.511 | Cond. No. | 842. |
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: | 19.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.19e-05 |
| Time: | 04:51:35 | Log-Likelihood: | -100.76 |
| 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 | -61.1834 | 159.358 | -0.384 | 0.705 | -393.599 271.232 |
| C(dose)[T.1] | 51.4816 | 9.028 | 5.703 | 0.000 | 32.650 70.313 |
| expression | 13.3269 | 18.392 | 0.725 | 0.477 | -25.038 51.692 |
| Omnibus: | 0.388 | Durbin-Watson: | 2.112 |
| Prob(Omnibus): | 0.824 | Jarque-Bera (JB): | 0.533 |
| Skew: | 0.209 | Prob(JB): | 0.766 |
| Kurtosis: | 2.382 | Cond. No. | 326. |
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: | 04:51:35 | 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: | 0.102 |
| Model: | OLS | Adj. R-squared: | 0.059 |
| Method: | Least Squares | F-statistic: | 2.385 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.137 |
| Time: | 04:51:35 | Log-Likelihood: | -111.87 |
| No. Observations: | 23 | AIC: | 227.7 |
| Df Residuals: | 21 | BIC: | 230.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -296.1245 | 243.448 | -1.216 | 0.237 | -802.402 210.153 |
| expression | 43.0758 | 27.891 | 1.544 | 0.137 | -14.926 101.078 |
| Omnibus: | 2.259 | Durbin-Watson: | 2.805 |
| Prob(Omnibus): | 0.323 | Jarque-Bera (JB): | 1.126 |
| Skew: | 0.025 | Prob(JB): | 0.569 |
| Kurtosis: | 1.917 | Cond. No. | 315. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.320 | 0.582 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.465 |
| Model: | OLS | Adj. R-squared: | 0.320 |
| Method: | Least Squares | F-statistic: | 3.193 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0665 |
| Time: | 04:51:35 | Log-Likelihood: | -70.602 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 216.2982 | 360.601 | 0.600 | 0.561 | -577.378 1009.975 |
| C(dose)[T.1] | 259.9274 | 936.881 | 0.277 | 0.787 | -1802.133 2321.988 |
| expression | -16.4749 | 39.885 | -0.413 | 0.688 | -104.261 71.311 |
| expression:C(dose)[T.1] | -22.5128 | 101.875 | -0.221 | 0.829 | -246.739 201.713 |
| Omnibus: | 3.139 | Durbin-Watson: | 0.937 |
| Prob(Omnibus): | 0.208 | Jarque-Bera (JB): | 2.085 |
| Skew: | -0.902 | Prob(JB): | 0.352 |
| Kurtosis: | 2.719 | Cond. No. | 1.27e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 5.175 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0240 |
| Time: | 04:51:35 | Log-Likelihood: | -70.636 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 247.4795 | 318.425 | 0.777 | 0.452 | -446.308 941.267 |
| C(dose)[T.1] | 52.9282 | 16.876 | 3.136 | 0.009 | 16.159 89.698 |
| expression | -19.9256 | 35.217 | -0.566 | 0.582 | -96.656 56.805 |
| Omnibus: | 2.816 | Durbin-Watson: | 0.988 |
| Prob(Omnibus): | 0.245 | Jarque-Bera (JB): | 1.935 |
| Skew: | -0.860 | Prob(JB): | 0.380 |
| Kurtosis: | 2.629 | Cond. No. | 381. |
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: | 04:51:35 | 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: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.052 |
| Method: | Least Squares | F-statistic: | 0.3060 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.590 |
| Time: | 04:51:35 | Log-Likelihood: | -75.126 |
| 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 | -118.6574 | 383.955 | -0.309 | 0.762 | -948.142 710.828 |
| expression | 23.2403 | 42.012 | 0.553 | 0.590 | -67.521 114.002 |
| Omnibus: | 0.141 | Durbin-Watson: | 1.485 |
| Prob(Omnibus): | 0.932 | Jarque-Bera (JB): | 0.357 |
| Skew: | -0.041 | Prob(JB): | 0.837 |
| Kurtosis: | 2.249 | Cond. No. | 354. |