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 |
| 2.200 | 0.154 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.706 |
| Model: | OLS | Adj. R-squared: | 0.659 |
| Method: | Least Squares | F-statistic: | 15.20 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.76e-05 |
| Time: | 04:37:22 | Log-Likelihood: | -99.030 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 19 | BIC: | 210.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.7442 | 142.186 | 0.167 | 0.869 | -273.854 321.343 |
| C(dose)[T.1] | -204.1404 | 207.613 | -0.983 | 0.338 | -638.679 230.398 |
| expression | 4.2375 | 19.762 | 0.214 | 0.832 | -37.125 45.600 |
| expression:C(dose)[T.1] | 33.3695 | 27.934 | 1.195 | 0.247 | -25.098 91.837 |
| Omnibus: | 0.170 | Durbin-Watson: | 2.121 |
| Prob(Omnibus): | 0.919 | Jarque-Bera (JB): | 0.319 |
| Skew: | 0.168 | Prob(JB): | 0.853 |
| Kurtosis: | 2.531 | Cond. No. | 494. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.684 |
| Model: | OLS | Adj. R-squared: | 0.652 |
| Method: | Least Squares | F-statistic: | 21.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.98e-06 |
| Time: | 04:37:22 | Log-Likelihood: | -99.862 |
| No. Observations: | 23 | AIC: | 205.7 |
| Df Residuals: | 20 | BIC: | 209.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -96.3196 | 101.641 | -0.948 | 0.355 | -308.339 115.700 |
| C(dose)[T.1] | 43.5493 | 10.622 | 4.100 | 0.001 | 21.392 65.706 |
| expression | 20.9383 | 14.115 | 1.483 | 0.154 | -8.506 50.383 |
| Omnibus: | 0.428 | Durbin-Watson: | 2.166 |
| Prob(Omnibus): | 0.807 | Jarque-Bera (JB): | 0.181 |
| Skew: | 0.210 | Prob(JB): | 0.914 |
| Kurtosis: | 2.889 | Cond. No. | 186. |
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: | 04:37:22 | 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: | 0.418 |
| Model: | OLS | Adj. R-squared: | 0.390 |
| Method: | Least Squares | F-statistic: | 15.09 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000856 |
| Time: | 04:37:22 | Log-Likelihood: | -106.88 |
| No. Observations: | 23 | AIC: | 217.8 |
| Df Residuals: | 21 | BIC: | 220.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -341.9822 | 108.698 | -3.146 | 0.005 | -568.031 -115.933 |
| expression | 56.8889 | 14.645 | 3.885 | 0.001 | 26.433 87.345 |
| Omnibus: | 0.316 | Durbin-Watson: | 2.569 |
| Prob(Omnibus): | 0.854 | Jarque-Bera (JB): | 0.484 |
| Skew: | 0.167 | Prob(JB): | 0.785 |
| Kurtosis: | 2.373 | Cond. No. | 149. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.427 | 0.089 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.571 |
| Model: | OLS | Adj. R-squared: | 0.454 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0214 |
| Time: | 04:37:22 | Log-Likelihood: | -68.949 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 11 | BIC: | 148.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -159.8133 | 155.248 | -1.029 | 0.325 | -501.511 181.885 |
| C(dose)[T.1] | 49.3534 | 276.291 | 0.179 | 0.861 | -558.760 657.467 |
| expression | 32.6341 | 22.243 | 1.467 | 0.170 | -16.323 81.591 |
| expression:C(dose)[T.1] | 0.4506 | 40.018 | 0.011 | 0.991 | -87.628 88.530 |
| Omnibus: | 3.806 | Durbin-Watson: | 0.960 |
| Prob(Omnibus): | 0.149 | Jarque-Bera (JB): | 1.743 |
| Skew: | -0.798 | Prob(JB): | 0.418 |
| Kurtosis: | 3.491 | Cond. No. | 331. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.571 |
| Model: | OLS | Adj. R-squared: | 0.500 |
| Method: | Least Squares | F-statistic: | 7.993 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00621 |
| Time: | 04:37:22 | Log-Likelihood: | -68.949 |
| 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 | -160.7826 | 123.692 | -1.300 | 0.218 | -430.284 108.719 |
| C(dose)[T.1] | 52.4598 | 13.993 | 3.749 | 0.003 | 21.971 82.948 |
| expression | 32.7733 | 17.704 | 1.851 | 0.089 | -5.799 71.346 |
| Omnibus: | 3.807 | Durbin-Watson: | 0.963 |
| Prob(Omnibus): | 0.149 | Jarque-Bera (JB): | 1.749 |
| Skew: | -0.800 | Prob(JB): | 0.417 |
| Kurtosis: | 3.485 | Cond. No. | 127. |
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: | 04:37:22 | 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: | 0.069 |
| Model: | OLS | Adj. R-squared: | -0.003 |
| Method: | Least Squares | F-statistic: | 0.9641 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.344 |
| Time: | 04:37:22 | Log-Likelihood: | -74.764 |
| 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 | -75.0283 | 172.090 | -0.436 | 0.670 | -446.806 296.749 |
| expression | 24.4124 | 24.863 | 0.982 | 0.344 | -29.301 78.126 |
| Omnibus: | 3.292 | Durbin-Watson: | 1.633 |
| Prob(Omnibus): | 0.193 | Jarque-Bera (JB): | 1.324 |
| Skew: | 0.290 | Prob(JB): | 0.516 |
| Kurtosis: | 1.665 | Cond. No. | 124. |