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 |
| 2.068 | 0.166 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.697 |
| Model: | OLS | Adj. R-squared: | 0.649 |
| Method: | Least Squares | F-statistic: | 14.56 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 3.66e-05 |
| Time: | 03:43:27 | Log-Likelihood: | -99.378 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 19 | BIC: | 211.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -36.1480 | 53.497 | -0.676 | 0.507 | -148.119 75.823 |
| C(dose)[T.1] | 127.9001 | 71.778 | 1.782 | 0.091 | -22.332 278.133 |
| expression | 22.6448 | 13.329 | 1.699 | 0.106 | -5.253 50.542 |
| expression:C(dose)[T.1] | -18.2186 | 18.832 | -0.967 | 0.345 | -57.635 21.198 |
| Omnibus: | 0.899 | Durbin-Watson: | 1.931 |
| Prob(Omnibus): | 0.638 | Jarque-Bera (JB): | 0.898 |
| Skew: | -0.369 | Prob(JB): | 0.638 |
| Kurtosis: | 2.375 | Cond. No. | 91.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.682 |
| Model: | OLS | Adj. R-squared: | 0.650 |
| Method: | Least Squares | F-statistic: | 21.44 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 1.06e-05 |
| Time: | 03:43:27 | Log-Likelihood: | -99.931 |
| No. Observations: | 23 | AIC: | 205.9 |
| Df Residuals: | 20 | BIC: | 209.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.2664 | 37.954 | 0.007 | 0.994 | -78.903 79.436 |
| C(dose)[T.1] | 59.0416 | 9.243 | 6.387 | 0.000 | 39.760 78.323 |
| expression | 13.5187 | 9.401 | 1.438 | 0.166 | -6.092 33.129 |
| Omnibus: | 0.971 | Durbin-Watson: | 1.780 |
| Prob(Omnibus): | 0.615 | Jarque-Bera (JB): | 0.822 |
| Skew: | -0.176 | Prob(JB): | 0.663 |
| Kurtosis: | 2.143 | Cond. No. | 37.5 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 03:43:27 | 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: | 0.033 |
| Model: | OLS | Adj. R-squared: | -0.013 |
| Method: | Least Squares | F-statistic: | 0.7191 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.406 |
| Time: | 03:43:27 | Log-Likelihood: | -112.72 |
| No. Observations: | 23 | AIC: | 229.4 |
| Df Residuals: | 21 | BIC: | 231.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.1332 | 55.193 | 2.285 | 0.033 | 11.353 240.913 |
| expression | -12.2522 | 14.448 | -0.848 | 0.406 | -42.299 17.794 |
| Omnibus: | 2.413 | Durbin-Watson: | 2.490 |
| Prob(Omnibus): | 0.299 | Jarque-Bera (JB): | 1.417 |
| Skew: | 0.319 | Prob(JB): | 0.492 |
| Kurtosis: | 1.964 | Cond. No. | 31.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.414 | 0.532 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.469 |
| Model: | OLS | Adj. R-squared: | 0.325 |
| Method: | Least Squares | F-statistic: | 3.244 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0640 |
| Time: | 03:43:27 | Log-Likelihood: | -70.547 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 151.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 99.9879 | 80.615 | 1.240 | 0.241 | -77.446 277.421 |
| C(dose)[T.1] | 75.0311 | 140.234 | 0.535 | 0.603 | -233.622 383.684 |
| expression | -6.7518 | 16.538 | -0.408 | 0.691 | -43.151 29.647 |
| expression:C(dose)[T.1] | -6.7212 | 31.112 | -0.216 | 0.833 | -75.198 61.756 |
| Omnibus: | 1.500 | Durbin-Watson: | 0.990 |
| Prob(Omnibus): | 0.472 | Jarque-Bera (JB): | 1.222 |
| Skew: | -0.580 | Prob(JB): | 0.543 |
| Kurtosis: | 2.220 | Cond. No. | 101. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.378 |
| Method: | Least Squares | F-statistic: | 5.261 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.0229 |
| Time: | 03:43:27 | Log-Likelihood: | -70.578 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 109.1459 | 65.790 | 1.659 | 0.123 | -34.197 252.489 |
| C(dose)[T.1] | 44.9733 | 16.808 | 2.676 | 0.020 | 8.351 81.595 |
| expression | -8.6508 | 13.440 | -0.644 | 0.532 | -37.934 20.632 |
| Omnibus: | 1.791 | Durbin-Watson: | 1.023 |
| Prob(Omnibus): | 0.408 | Jarque-Bera (JB): | 1.412 |
| Skew: | -0.664 | Prob(JB): | 0.494 |
| Kurtosis: | 2.298 | Cond. No. | 41.5 |
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: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 03:43:27 | 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: | 0.149 |
| Model: | OLS | Adj. R-squared: | 0.084 |
| Method: | Least Squares | F-statistic: | 2.281 |
| Date: | Fri, 31 Oct 2025 | Prob (F-statistic): | 0.155 |
| Time: | 03:43:27 | Log-Likelihood: | -74.088 |
| No. Observations: | 15 | AIC: | 152.2 |
| Df Residuals: | 13 | BIC: | 153.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 197.1687 | 69.166 | 2.851 | 0.014 | 47.744 346.593 |
| expression | -22.6880 | 15.022 | -1.510 | 0.155 | -55.140 9.764 |
| Omnibus: | 0.157 | Durbin-Watson: | 1.581 |
| Prob(Omnibus): | 0.925 | Jarque-Bera (JB): | 0.366 |
| Skew: | 0.089 | Prob(JB): | 0.833 |
| Kurtosis: | 2.256 | Cond. No. | 35.6 |