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 |
| 0.633 | 0.436 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.607 |
| Method: | Least Squares | F-statistic: | 12.34 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000104 |
| Time: | 04:33:49 | Log-Likelihood: | -100.67 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -114.6429 | 214.807 | -0.534 | 0.600 | -564.239 334.954 |
| C(dose)[T.1] | 154.2928 | 384.442 | 0.401 | 0.693 | -650.354 958.940 |
| expression | 16.0021 | 20.349 | 0.786 | 0.441 | -26.589 58.593 |
| expression:C(dose)[T.1] | -9.2632 | 37.618 | -0.246 | 0.808 | -87.999 69.472 |
| Omnibus: | 1.071 | Durbin-Watson: | 1.636 |
| Prob(Omnibus): | 0.585 | Jarque-Bera (JB): | 0.850 |
| Skew: | -0.162 | Prob(JB): | 0.654 |
| Kurtosis: | 2.116 | Cond. No. | 1.08e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 19.40 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.08e-05 |
| Time: | 04:33:49 | Log-Likelihood: | -100.70 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -86.0417 | 176.402 | -0.488 | 0.631 | -454.009 281.926 |
| C(dose)[T.1] | 59.6732 | 11.747 | 5.080 | 0.000 | 35.170 84.177 |
| expression | 13.2915 | 16.708 | 0.796 | 0.436 | -21.561 48.144 |
| Omnibus: | 1.088 | Durbin-Watson: | 1.639 |
| Prob(Omnibus): | 0.580 | Jarque-Bera (JB): | 0.844 |
| Skew: | -0.139 | Prob(JB): | 0.656 |
| Kurtosis: | 2.104 | Cond. No. | 427. |
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: | 04:33:50 | 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: | 0.221 |
| Model: | OLS | Adj. R-squared: | 0.184 |
| Method: | Least Squares | F-statistic: | 5.954 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0236 |
| Time: | 04:33:50 | Log-Likelihood: | -110.23 |
| No. Observations: | 23 | AIC: | 224.5 |
| Df Residuals: | 21 | BIC: | 226.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 536.6204 | 187.359 | 2.864 | 0.009 | 146.985 926.255 |
| expression | -44.2571 | 18.138 | -2.440 | 0.024 | -81.977 -6.538 |
| Omnibus: | 1.227 | Durbin-Watson: | 2.680 |
| Prob(Omnibus): | 0.541 | Jarque-Bera (JB): | 1.038 |
| Skew: | 0.316 | Prob(JB): | 0.595 |
| Kurtosis: | 2.173 | Cond. No. | 307. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.026 | 0.875 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.475 |
| Model: | OLS | Adj. R-squared: | 0.332 |
| Method: | Least Squares | F-statistic: | 3.319 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0606 |
| Time: | 04:33:50 | Log-Likelihood: | -70.466 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -191.0993 | 650.680 | -0.294 | 0.774 | -1623.237 1241.038 |
| C(dose)[T.1] | 735.3656 | 942.202 | 0.780 | 0.452 | -1338.407 2809.138 |
| expression | 26.0605 | 65.580 | 0.397 | 0.699 | -118.280 170.401 |
| expression:C(dose)[T.1] | -67.8350 | 93.439 | -0.726 | 0.483 | -273.492 137.822 |
| Omnibus: | 1.730 | Durbin-Watson: | 0.979 |
| Prob(Omnibus): | 0.421 | Jarque-Bera (JB): | 1.312 |
| Skew: | -0.552 | Prob(JB): | 0.519 |
| Kurtosis: | 2.063 | Cond. No. | 1.59e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.908 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 04:33:50 | Log-Likelihood: | -70.817 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 140.3906 | 454.340 | 0.309 | 0.763 | -849.531 1130.312 |
| C(dose)[T.1] | 51.5249 | 21.385 | 2.409 | 0.033 | 4.931 98.119 |
| expression | -7.3548 | 45.784 | -0.161 | 0.875 | -107.110 92.401 |
| Omnibus: | 2.178 | Durbin-Watson: | 0.815 |
| Prob(Omnibus): | 0.337 | Jarque-Bera (JB): | 1.619 |
| Skew: | -0.755 | Prob(JB): | 0.445 |
| Kurtosis: | 2.444 | Cond. No. | 592. |
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: | 04:33:50 | 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: | 0.184 |
| Model: | OLS | Adj. R-squared: | 0.121 |
| Method: | Least Squares | F-statistic: | 2.929 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.111 |
| Time: | 04:33:50 | Log-Likelihood: | -73.776 |
| No. Observations: | 15 | AIC: | 151.6 |
| Df Residuals: | 13 | BIC: | 153.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -586.5218 | 397.569 | -1.475 | 0.164 | -1445.417 272.374 |
| expression | 67.4178 | 39.395 | 1.711 | 0.111 | -17.690 152.526 |
| Omnibus: | 1.372 | Durbin-Watson: | 1.295 |
| Prob(Omnibus): | 0.504 | Jarque-Bera (JB): | 0.949 |
| Skew: | -0.583 | Prob(JB): | 0.622 |
| Kurtosis: | 2.600 | Cond. No. | 441. |