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 |
| 0.406 | 0.531 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.72 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 8.64e-05 |
| Time: | 22:47:53 | Log-Likelihood: | -100.44 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.8133 | 115.964 | 0.507 | 0.618 | -183.901 301.528 |
| C(dose)[T.1] | 222.6502 | 205.262 | 1.085 | 0.292 | -206.968 652.268 |
| expression | -0.6252 | 15.723 | -0.040 | 0.969 | -33.533 32.283 |
| expression:C(dose)[T.1] | -21.8869 | 26.966 | -0.812 | 0.427 | -78.327 34.553 |
| Omnibus: | 0.020 | Durbin-Watson: | 2.053 |
| Prob(Omnibus): | 0.990 | Jarque-Bera (JB): | 0.226 |
| Skew: | 0.010 | Prob(JB): | 0.893 |
| Kurtosis: | 2.515 | Cond. No. | 438. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.07 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.32e-05 |
| Time: | 22:47:54 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 113.6180 | 93.470 | 1.216 | 0.238 | -81.358 308.594 |
| C(dose)[T.1] | 56.2411 | 9.807 | 5.735 | 0.000 | 35.785 76.697 |
| expression | -8.0659 | 12.664 | -0.637 | 0.531 | -34.483 18.351 |
| Omnibus: | 0.038 | Durbin-Watson: | 1.931 |
| Prob(Omnibus): | 0.981 | Jarque-Bera (JB): | 0.247 |
| Skew: | -0.040 | Prob(JB): | 0.884 |
| Kurtosis: | 2.498 | Cond. No. | 166. |
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: | 22:47:54 | 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: | 0.090 |
| Model: | OLS | Adj. R-squared: | 0.047 |
| Method: | Least Squares | F-statistic: | 2.086 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.163 |
| Time: | 22:47:54 | Log-Likelihood: | -112.02 |
| No. Observations: | 23 | AIC: | 228.0 |
| Df Residuals: | 21 | BIC: | 230.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -114.0166 | 134.299 | -0.849 | 0.405 | -393.306 165.273 |
| expression | 25.7021 | 17.794 | 1.444 | 0.163 | -11.302 62.706 |
| Omnibus: | 3.083 | Durbin-Watson: | 2.270 |
| Prob(Omnibus): | 0.214 | Jarque-Bera (JB): | 1.682 |
| Skew: | 0.378 | Prob(JB): | 0.431 |
| Kurtosis: | 1.911 | Cond. No. | 150. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.239 | 0.634 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.323 |
| Method: | Least Squares | F-statistic: | 3.225 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0650 |
| Time: | 22:47:54 | Log-Likelihood: | -70.568 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 60.9755 | 188.208 | 0.324 | 0.752 | -353.268 475.219 |
| C(dose)[T.1] | -59.7325 | 263.142 | -0.227 | 0.825 | -638.904 519.439 |
| expression | 0.9240 | 26.898 | 0.034 | 0.973 | -58.277 60.125 |
| expression:C(dose)[T.1] | 15.7675 | 37.798 | 0.417 | 0.685 | -67.426 98.961 |
| Omnibus: | 1.576 | Durbin-Watson: | 0.837 |
| Prob(Omnibus): | 0.455 | Jarque-Bera (JB): | 1.147 |
| Skew: | -0.634 | Prob(JB): | 0.563 |
| Kurtosis: | 2.524 | Cond. No. | 309. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 5.101 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0249 |
| Time: | 22:47:54 | Log-Likelihood: | -70.685 |
| 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 | 5.2170 | 127.856 | 0.041 | 0.968 | -273.358 283.792 |
| C(dose)[T.1] | 49.8278 | 15.639 | 3.186 | 0.008 | 15.754 83.902 |
| expression | 8.9084 | 18.236 | 0.489 | 0.634 | -30.824 48.641 |
| Omnibus: | 2.043 | Durbin-Watson: | 0.838 |
| Prob(Omnibus): | 0.360 | Jarque-Bera (JB): | 1.474 |
| Skew: | -0.729 | Prob(JB): | 0.478 |
| Kurtosis: | 2.517 | Cond. No. | 117. |
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: | 22:47:54 | 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: | 0.02997 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.865 |
| Time: | 22:47:55 | Log-Likelihood: | -75.283 |
| 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 | 65.1423 | 165.083 | 0.395 | 0.700 | -291.497 421.782 |
| expression | 4.1068 | 23.723 | 0.173 | 0.865 | -47.143 55.357 |
| Omnibus: | 0.489 | Durbin-Watson: | 1.598 |
| Prob(Omnibus): | 0.783 | Jarque-Bera (JB): | 0.538 |
| Skew: | 0.054 | Prob(JB): | 0.764 |
| Kurtosis: | 2.079 | Cond. No. | 116. |