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 |
| 0.836 | 0.371 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.10e-05 |
| Time: | 06:24:21 | Log-Likelihood: | -100.20 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.0484 | 53.373 | 2.193 | 0.041 | 5.338 228.759 |
| C(dose)[T.1] | 0.8840 | 65.013 | 0.014 | 0.989 | -135.191 136.959 |
| expression | -15.8402 | 13.369 | -1.185 | 0.251 | -43.821 12.141 |
| expression:C(dose)[T.1] | 13.2220 | 16.242 | 0.814 | 0.426 | -20.773 47.217 |
| Omnibus: | 0.250 | Durbin-Watson: | 2.047 |
| Prob(Omnibus): | 0.883 | Jarque-Bera (JB): | 0.439 |
| Skew: | 0.025 | Prob(JB): | 0.803 |
| Kurtosis: | 2.325 | Cond. No. | 89.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.88e-05 |
| Time: | 06:24:21 | Log-Likelihood: | -100.59 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 81.5116 | 30.449 | 2.677 | 0.014 | 17.997 145.027 |
| C(dose)[T.1] | 53.3371 | 8.592 | 6.208 | 0.000 | 35.414 71.260 |
| expression | -6.8824 | 7.528 | -0.914 | 0.371 | -22.585 8.820 |
| Omnibus: | 0.344 | Durbin-Watson: | 1.978 |
| Prob(Omnibus): | 0.842 | Jarque-Bera (JB): | 0.475 |
| Skew: | 0.230 | Prob(JB): | 0.789 |
| Kurtosis: | 2.468 | Cond. No. | 30.4 |
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: | 06:24:21 | 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: | 0.014 |
| Model: | OLS | Adj. R-squared: | -0.033 |
| Method: | Least Squares | F-statistic: | 0.2999 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.590 |
| Time: | 06:24:21 | Log-Likelihood: | -112.94 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.0200 | 50.371 | 2.125 | 0.046 | 2.269 211.771 |
| expression | -6.8822 | 12.568 | -0.548 | 0.590 | -33.019 19.254 |
| Omnibus: | 2.313 | Durbin-Watson: | 2.557 |
| Prob(Omnibus): | 0.315 | Jarque-Bera (JB): | 1.482 |
| Skew: | 0.373 | Prob(JB): | 0.477 |
| Kurtosis: | 2.005 | Cond. No. | 29.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.026 | 0.874 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.538 |
| Model: | OLS | Adj. R-squared: | 0.412 |
| Method: | Least Squares | F-statistic: | 4.269 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0315 |
| Time: | 06:24:21 | Log-Likelihood: | -69.509 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 110.8069 | 67.050 | 1.653 | 0.127 | -36.769 258.383 |
| C(dose)[T.1] | -100.0951 | 105.947 | -0.945 | 0.365 | -333.284 133.093 |
| expression | -9.2569 | 14.115 | -0.656 | 0.525 | -40.323 21.810 |
| expression:C(dose)[T.1] | 37.0993 | 25.631 | 1.447 | 0.176 | -19.314 93.513 |
| Omnibus: | 1.405 | Durbin-Watson: | 1.266 |
| Prob(Omnibus): | 0.495 | Jarque-Bera (JB): | 0.998 |
| Skew: | -0.594 | Prob(JB): | 0.607 |
| Kurtosis: | 2.567 | Cond. No. | 77.5 |
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: | 4.909 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 06:24:21 | 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 | 58.0846 | 58.806 | 0.988 | 0.343 | -70.043 186.212 |
| C(dose)[T.1] | 50.9552 | 19.106 | 2.667 | 0.021 | 9.327 92.584 |
| expression | 1.9940 | 12.308 | 0.162 | 0.874 | -24.822 28.810 |
| Omnibus: | 2.409 | Durbin-Watson: | 0.822 |
| Prob(Omnibus): | 0.300 | Jarque-Bera (JB): | 1.717 |
| Skew: | -0.796 | Prob(JB): | 0.424 |
| Kurtosis: | 2.539 | Cond. No. | 34.7 |
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: | 06:24:21 | 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: | 0.124 |
| Model: | OLS | Adj. R-squared: | 0.057 |
| Method: | Least Squares | F-statistic: | 1.840 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.198 |
| Time: | 06:24:22 | Log-Likelihood: | -74.307 |
| No. Observations: | 15 | AIC: | 152.6 |
| Df Residuals: | 13 | BIC: | 154.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 163.8820 | 52.636 | 3.113 | 0.008 | 50.169 277.595 |
| expression | -16.6559 | 12.280 | -1.356 | 0.198 | -43.186 9.874 |
| Omnibus: | 1.106 | Durbin-Watson: | 1.479 |
| Prob(Omnibus): | 0.575 | Jarque-Bera (JB): | 0.747 |
| Skew: | -0.087 | Prob(JB): | 0.688 |
| Kurtosis: | 1.921 | Cond. No. | 25.0 |