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 |
| 0.776 | 0.389 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.610 |
| Method: | Least Squares | F-statistic: | 12.47 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 9.76e-05 |
| Time: | 22:49:21 | Log-Likelihood: | -100.59 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 104.6474 | 97.639 | 1.072 | 0.297 | -99.713 309.008 |
| C(dose)[T.1] | 93.0639 | 158.030 | 0.589 | 0.563 | -237.696 423.824 |
| expression | -11.1287 | 21.501 | -0.518 | 0.611 | -56.130 33.873 |
| expression:C(dose)[T.1] | -8.0694 | 34.065 | -0.237 | 0.815 | -79.368 63.229 |
| Omnibus: | 0.619 | Durbin-Watson: | 1.692 |
| Prob(Omnibus): | 0.734 | Jarque-Bera (JB): | 0.642 |
| Skew: | 0.096 | Prob(JB): | 0.725 |
| Kurtosis: | 2.204 | Cond. No. | 215. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.60 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.94e-05 |
| Time: | 22:49:21 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 119.2173 | 74.020 | 1.611 | 0.123 | -35.185 273.620 |
| C(dose)[T.1] | 55.6931 | 9.010 | 6.181 | 0.000 | 36.898 74.488 |
| expression | -14.3433 | 16.279 | -0.881 | 0.389 | -48.300 19.613 |
| Omnibus: | 0.549 | Durbin-Watson: | 1.719 |
| Prob(Omnibus): | 0.760 | Jarque-Bera (JB): | 0.611 |
| Skew: | 0.104 | Prob(JB): | 0.737 |
| Kurtosis: | 2.229 | Cond. No. | 83.8 |
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: | 22:49:21 | 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: | 0.017 |
| Model: | OLS | Adj. R-squared: | -0.030 |
| Method: | Least Squares | F-statistic: | 0.3594 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.555 |
| Time: | 22:49:21 | Log-Likelihood: | -112.91 |
| No. Observations: | 23 | AIC: | 229.8 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 8.1766 | 119.546 | 0.068 | 0.946 | -240.434 256.787 |
| expression | 15.5156 | 25.880 | 0.600 | 0.555 | -38.306 69.337 |
| Omnibus: | 1.341 | Durbin-Watson: | 2.565 |
| Prob(Omnibus): | 0.511 | Jarque-Bera (JB): | 1.033 |
| Skew: | 0.268 | Prob(JB): | 0.597 |
| Kurtosis: | 2.111 | Cond. No. | 80.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.434 | 0.523 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.490 |
| Model: | OLS | Adj. R-squared: | 0.350 |
| Method: | Least Squares | F-statistic: | 3.517 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0526 |
| Time: | 22:49:21 | Log-Likelihood: | -70.256 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -38.3027 | 113.346 | -0.338 | 0.742 | -287.776 211.171 |
| C(dose)[T.1] | 154.4471 | 152.139 | 1.015 | 0.332 | -180.408 489.303 |
| expression | 20.3130 | 21.663 | 0.938 | 0.369 | -27.366 67.992 |
| expression:C(dose)[T.1] | -20.2165 | 29.655 | -0.682 | 0.510 | -85.486 45.053 |
| Omnibus: | 2.271 | Durbin-Watson: | 0.575 |
| Prob(Omnibus): | 0.321 | Jarque-Bera (JB): | 1.577 |
| Skew: | -0.767 | Prob(JB): | 0.455 |
| Kurtosis: | 2.588 | Cond. No. | 138. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.278 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0227 |
| Time: | 22:49:21 | Log-Likelihood: | -70.567 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.8493 | 76.109 | 0.235 | 0.819 | -147.978 183.677 |
| C(dose)[T.1] | 51.3164 | 15.794 | 3.249 | 0.007 | 16.904 85.728 |
| expression | 9.5251 | 14.460 | 0.659 | 0.523 | -21.981 41.031 |
| Omnibus: | 1.782 | Durbin-Watson: | 0.685 |
| Prob(Omnibus): | 0.410 | Jarque-Bera (JB): | 1.322 |
| Skew: | -0.678 | Prob(JB): | 0.516 |
| Kurtosis: | 2.477 | Cond. No. | 52.7 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:49:22 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 6.794e-06 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.998 |
| Time: | 22:49:22 | Log-Likelihood: | -75.300 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.9139 | 95.394 | 0.984 | 0.343 | -112.172 300.000 |
| expression | -0.0486 | 18.648 | -0.003 | 0.998 | -40.335 40.238 |
| Omnibus: | 0.579 | Durbin-Watson: | 1.622 |
| Prob(Omnibus): | 0.749 | Jarque-Bera (JB): | 0.572 |
| Skew: | 0.044 | Prob(JB): | 0.751 |
| Kurtosis: | 2.048 | Cond. No. | 49.8 |