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 |
| 0.001 | 0.973 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.605 |
| Method: | Least Squares | F-statistic: | 12.22 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000110 |
| Time: | 22:55:12 | Log-Likelihood: | -100.74 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 20.9989 | 73.164 | 0.287 | 0.777 | -132.136 174.134 |
| C(dose)[T.1] | 143.3107 | 123.573 | 1.160 | 0.261 | -115.330 401.951 |
| expression | 4.9516 | 10.870 | 0.456 | 0.654 | -17.801 27.704 |
| expression:C(dose)[T.1] | -13.5238 | 18.531 | -0.730 | 0.474 | -52.309 25.262 |
| Omnibus: | 0.932 | Durbin-Watson: | 1.807 |
| Prob(Omnibus): | 0.628 | Jarque-Bera (JB): | 0.801 |
| Skew: | 0.167 | Prob(JB): | 0.670 |
| Kurtosis: | 2.149 | Cond. No. | 231. |
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, 03 Apr 2025 | Prob (F-statistic): | 2.83e-05 |
| Time: | 22:55:12 | 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 | 52.2108 | 58.665 | 0.890 | 0.384 | -70.162 174.584 |
| C(dose)[T.1] | 53.3624 | 8.801 | 6.063 | 0.000 | 35.005 71.720 |
| expression | 0.2978 | 8.700 | 0.034 | 0.973 | -17.850 18.446 |
| Omnibus: | 0.285 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.867 | Jarque-Bera (JB): | 0.462 |
| Skew: | 0.051 | Prob(JB): | 0.794 |
| Kurtosis: | 2.313 | Cond. No. | 91.7 |
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: | 22:55:12 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.08406 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.775 |
| Time: | 22:55:12 | Log-Likelihood: | -113.06 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 107.2655 | 95.290 | 1.126 | 0.273 | -90.900 305.431 |
| expression | -4.1325 | 14.254 | -0.290 | 0.775 | -33.774 25.509 |
| Omnibus: | 3.112 | Durbin-Watson: | 2.554 |
| Prob(Omnibus): | 0.211 | Jarque-Bera (JB): | 1.517 |
| Skew: | 0.281 | Prob(JB): | 0.468 |
| Kurtosis: | 1.874 | Cond. No. | 90.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.580 | 0.461 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.526 |
| Model: | OLS | Adj. R-squared: | 0.397 |
| Method: | Least Squares | F-statistic: | 4.074 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0358 |
| Time: | 22:55:12 | Log-Likelihood: | -69.696 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 11 | BIC: | 150.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -59.4910 | 102.253 | -0.582 | 0.572 | -284.548 165.566 |
| C(dose)[T.1] | 249.3126 | 180.806 | 1.379 | 0.195 | -148.638 647.263 |
| expression | 18.1838 | 14.563 | 1.249 | 0.238 | -13.869 50.236 |
| expression:C(dose)[T.1] | -28.9870 | 26.345 | -1.100 | 0.295 | -86.972 28.998 |
| Omnibus: | 3.452 | Durbin-Watson: | 0.883 |
| Prob(Omnibus): | 0.178 | Jarque-Bera (JB): | 2.089 |
| Skew: | -0.913 | Prob(JB): | 0.352 |
| Kurtosis: | 2.926 | Cond. No. | 205. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.387 |
| Method: | Least Squares | F-statistic: | 5.411 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0211 |
| Time: | 22:55:13 | Log-Likelihood: | -70.479 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.3296 | 86.179 | 0.027 | 0.979 | -185.439 190.098 |
| C(dose)[T.1] | 51.1027 | 15.575 | 3.281 | 0.007 | 17.169 85.037 |
| expression | 9.3267 | 12.242 | 0.762 | 0.461 | -17.346 35.999 |
| Omnibus: | 1.590 | Durbin-Watson: | 0.947 |
| Prob(Omnibus): | 0.452 | Jarque-Bera (JB): | 0.970 |
| Skew: | -0.609 | Prob(JB): | 0.616 |
| Kurtosis: | 2.734 | Cond. No. | 79.4 |
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: | 22:55:13 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.074 |
| Method: | Least Squares | F-statistic: | 0.03231 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.860 |
| Time: | 22:55:13 | Log-Likelihood: | -75.281 |
| 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 | 73.9200 | 110.329 | 0.670 | 0.515 | -164.431 312.271 |
| expression | 2.8740 | 15.990 | 0.180 | 0.860 | -31.669 37.417 |
| Omnibus: | 0.781 | Durbin-Watson: | 1.667 |
| Prob(Omnibus): | 0.677 | Jarque-Bera (JB): | 0.661 |
| Skew: | 0.137 | Prob(JB): | 0.718 |
| Kurtosis: | 2.008 | Cond. No. | 76.6 |