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 |
| 0.000 | 0.985 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 13.03 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.43e-05 |
| Time: | 06:25:22 | Log-Likelihood: | -100.25 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 157.9403 | 162.847 | 0.970 | 0.344 | -182.903 498.784 |
| C(dose)[T.1] | -277.0833 | 281.182 | -0.985 | 0.337 | -865.604 311.437 |
| expression | -14.3664 | 22.538 | -0.637 | 0.531 | -61.539 32.807 |
| expression:C(dose)[T.1] | 47.7743 | 40.600 | 1.177 | 0.254 | -37.201 132.750 |
| Omnibus: | 4.113 | Durbin-Watson: | 2.001 |
| Prob(Omnibus): | 0.128 | Jarque-Bera (JB): | 1.593 |
| Skew: | 0.201 | Prob(JB): | 0.451 |
| Kurtosis: | 1.775 | Cond. No. | 554. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 06:25:22 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.6345 | 136.788 | 0.377 | 0.710 | -233.700 336.969 |
| C(dose)[T.1] | 53.4922 | 12.029 | 4.447 | 0.000 | 28.401 78.583 |
| expression | 0.3565 | 18.926 | 0.019 | 0.985 | -39.122 39.835 |
| Omnibus: | 0.339 | Durbin-Watson: | 1.883 |
| Prob(Omnibus): | 0.844 | Jarque-Bera (JB): | 0.495 |
| Skew: | 0.062 | Prob(JB): | 0.781 |
| Kurtosis: | 2.292 | Cond. No. | 224. |
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: | 06:25:22 | 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: | 0.302 |
| Model: | OLS | Adj. R-squared: | 0.269 |
| Method: | Least Squares | F-statistic: | 9.088 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00660 |
| Time: | 06:25:22 | Log-Likelihood: | -108.97 |
| No. Observations: | 23 | AIC: | 221.9 |
| Df Residuals: | 21 | BIC: | 224.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 481.1683 | 133.305 | 3.610 | 0.002 | 203.944 758.392 |
| expression | -57.2485 | 18.990 | -3.015 | 0.007 | -96.741 -17.756 |
| Omnibus: | 0.333 | Durbin-Watson: | 2.731 |
| Prob(Omnibus): | 0.847 | Jarque-Bera (JB): | 0.497 |
| Skew: | 0.147 | Prob(JB): | 0.780 |
| Kurtosis: | 2.342 | Cond. No. | 158. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.050 | 0.326 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 3.671 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0472 |
| Time: | 06:25:22 | Log-Likelihood: | -70.097 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -199.4089 | 268.423 | -0.743 | 0.473 | -790.203 391.386 |
| C(dose)[T.1] | 201.9158 | 403.515 | 0.500 | 0.627 | -686.215 1090.046 |
| expression | 37.3763 | 37.564 | 0.995 | 0.341 | -45.302 120.055 |
| expression:C(dose)[T.1] | -21.9603 | 55.367 | -0.397 | 0.699 | -143.822 99.902 |
| Omnibus: | 3.122 | Durbin-Watson: | 0.626 |
| Prob(Omnibus): | 0.210 | Jarque-Bera (JB): | 1.939 |
| Skew: | -0.877 | Prob(JB): | 0.379 |
| Kurtosis: | 2.842 | Cond. No. | 501. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 5.837 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0170 |
| Time: | 06:25:22 | Log-Likelihood: | -70.204 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -127.2422 | 190.291 | -0.669 | 0.516 | -541.850 287.365 |
| C(dose)[T.1] | 42.0153 | 16.641 | 2.525 | 0.027 | 5.758 78.272 |
| expression | 27.2678 | 26.610 | 1.025 | 0.326 | -30.709 85.245 |
| Omnibus: | 3.454 | Durbin-Watson: | 0.644 |
| Prob(Omnibus): | 0.178 | Jarque-Bera (JB): | 2.017 |
| Skew: | -0.898 | Prob(JB): | 0.365 |
| Kurtosis: | 2.989 | Cond. No. | 188. |
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: | 06:25:22 | 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: | 0.224 |
| Model: | OLS | Adj. R-squared: | 0.164 |
| Method: | Least Squares | F-statistic: | 3.749 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0749 |
| Time: | 06:25:22 | Log-Likelihood: | -73.399 |
| No. Observations: | 15 | AIC: | 150.8 |
| Df Residuals: | 13 | BIC: | 152.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -310.8008 | 209.072 | -1.487 | 0.161 | -762.474 140.873 |
| expression | 55.5613 | 28.694 | 1.936 | 0.075 | -6.428 117.550 |
| Omnibus: | 0.651 | Durbin-Watson: | 1.447 |
| Prob(Omnibus): | 0.722 | Jarque-Bera (JB): | 0.616 |
| Skew: | 0.143 | Prob(JB): | 0.735 |
| Kurtosis: | 2.049 | Cond. No. | 173. |