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 |
| 1.153 | 0.296 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.89 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 4.95e-05 |
| Time: | 23:01:30 | Log-Likelihood: | -99.751 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.1804 | 242.567 | 0.141 | 0.889 | -473.518 541.879 |
| C(dose)[T.1] | -335.0458 | 362.992 | -0.923 | 0.368 | -1094.797 424.705 |
| expression | 2.2807 | 27.615 | 0.083 | 0.935 | -55.518 60.079 |
| expression:C(dose)[T.1] | 43.7950 | 41.113 | 1.065 | 0.300 | -42.255 129.845 |
| Omnibus: | 0.415 | Durbin-Watson: | 1.805 |
| Prob(Omnibus): | 0.812 | Jarque-Bera (JB): | 0.538 |
| Skew: | 0.069 | Prob(JB): | 0.764 |
| Kurtosis: | 2.264 | Cond. No. | 969. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.14 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.62e-05 |
| Time: | 23:01:30 | Log-Likelihood: | -100.42 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -139.3267 | 180.351 | -0.773 | 0.449 | -515.531 236.878 |
| C(dose)[T.1] | 51.5183 | 8.694 | 5.926 | 0.000 | 33.383 69.654 |
| expression | 22.0393 | 20.527 | 1.074 | 0.296 | -20.779 64.858 |
| Omnibus: | 1.266 | Durbin-Watson: | 1.849 |
| Prob(Omnibus): | 0.531 | Jarque-Bera (JB): | 0.881 |
| Skew: | 0.093 | Prob(JB): | 0.644 |
| Kurtosis: | 2.059 | Cond. No. | 379. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:01:30 | 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: | 0.086 |
| Model: | OLS | Adj. R-squared: | 0.042 |
| Method: | Least Squares | F-statistic: | 1.967 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.175 |
| Time: | 23:01:30 | Log-Likelihood: | -112.08 |
| No. Observations: | 23 | AIC: | 228.2 |
| Df Residuals: | 21 | BIC: | 230.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -323.7401 | 287.787 | -1.125 | 0.273 | -922.225 274.745 |
| expression | 45.7391 | 32.616 | 1.402 | 0.175 | -22.090 113.569 |
| Omnibus: | 3.427 | Durbin-Watson: | 2.507 |
| Prob(Omnibus): | 0.180 | Jarque-Bera (JB): | 1.388 |
| Skew: | 0.102 | Prob(JB): | 0.499 |
| Kurtosis: | 1.814 | Cond. No. | 373. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.220 | 0.647 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.548 |
| Model: | OLS | Adj. R-squared: | 0.425 |
| Method: | Least Squares | F-statistic: | 4.450 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0280 |
| Time: | 23:01:30 | Log-Likelihood: | -69.341 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 11 | BIC: | 149.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 637.0593 | 623.216 | 1.022 | 0.329 | -734.631 2008.750 |
| C(dose)[T.1] | -1100.0706 | 774.824 | -1.420 | 0.183 | -2805.447 605.305 |
| expression | -62.4603 | 68.326 | -0.914 | 0.380 | -212.844 87.923 |
| expression:C(dose)[T.1] | 124.0589 | 84.029 | 1.476 | 0.168 | -60.888 309.006 |
| Omnibus: | 1.005 | Durbin-Watson: | 1.395 |
| Prob(Omnibus): | 0.605 | Jarque-Bera (JB): | 0.776 |
| Skew: | -0.232 | Prob(JB): | 0.679 |
| Kurtosis: | 1.987 | Cond. No. | 1.40e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.368 |
| Method: | Least Squares | F-statistic: | 5.085 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0252 |
| Time: | 23:01:30 | Log-Likelihood: | -70.697 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -110.9770 | 380.301 | -0.292 | 0.775 | -939.582 717.628 |
| C(dose)[T.1] | 43.5229 | 19.733 | 2.206 | 0.048 | 0.528 86.518 |
| expression | 19.5623 | 41.682 | 0.469 | 0.647 | -71.254 110.379 |
| Omnibus: | 1.905 | Durbin-Watson: | 0.728 |
| Prob(Omnibus): | 0.386 | Jarque-Bera (JB): | 1.487 |
| Skew: | -0.650 | Prob(JB): | 0.475 |
| Kurtosis: | 2.170 | Cond. No. | 460. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:01:30 | 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: | 0.239 |
| Model: | OLS | Adj. R-squared: | 0.181 |
| Method: | Least Squares | F-statistic: | 4.089 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0642 |
| Time: | 23:01:30 | Log-Likelihood: | -73.249 |
| No. Observations: | 15 | AIC: | 150.5 |
| Df Residuals: | 13 | BIC: | 151.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -610.0763 | 348.125 | -1.752 | 0.103 | -1362.155 142.002 |
| expression | 75.8789 | 37.523 | 2.022 | 0.064 | -5.185 156.943 |
| Omnibus: | 0.518 | Durbin-Watson: | 1.058 |
| Prob(Omnibus): | 0.772 | Jarque-Bera (JB): | 0.248 |
| Skew: | -0.292 | Prob(JB): | 0.883 |
| Kurtosis: | 2.764 | Cond. No. | 369. |