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 |
| 0.394 | 0.537 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.611 |
| Method: | Least Squares | F-statistic: | 12.51 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 9.57e-05 |
| Time: | 22:53:03 | Log-Likelihood: | -100.57 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.5995 | 146.666 | 0.154 | 0.879 | -284.375 329.574 |
| C(dose)[T.1] | -168.6926 | 320.899 | -0.526 | 0.605 | -840.342 502.957 |
| expression | 3.2071 | 14.868 | 0.216 | 0.832 | -27.912 34.326 |
| expression:C(dose)[T.1] | 20.6875 | 30.720 | 0.673 | 0.509 | -43.610 84.985 |
| Omnibus: | 0.215 | Durbin-Watson: | 2.035 |
| Prob(Omnibus): | 0.898 | Jarque-Bera (JB): | 0.408 |
| Skew: | 0.136 | Prob(JB): | 0.816 |
| Kurtosis: | 2.407 | Cond. No. | 890. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.06 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.33e-05 |
| Time: | 22:53:03 | Log-Likelihood: | -100.84 |
| 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 | -25.1621 | 126.611 | -0.199 | 0.844 | -289.268 238.944 |
| C(dose)[T.1] | 47.2256 | 13.048 | 3.619 | 0.002 | 20.008 74.444 |
| expression | 8.0530 | 12.832 | 0.628 | 0.537 | -18.713 34.819 |
| Omnibus: | 0.276 | Durbin-Watson: | 1.895 |
| Prob(Omnibus): | 0.871 | Jarque-Bera (JB): | 0.408 |
| Skew: | 0.214 | Prob(JB): | 0.816 |
| Kurtosis: | 2.507 | Cond. No. | 303. |
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: | 22:53:03 | 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: | 0.430 |
| Model: | OLS | Adj. R-squared: | 0.403 |
| Method: | Least Squares | F-statistic: | 15.87 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000676 |
| Time: | 22:53:03 | Log-Likelihood: | -106.63 |
| No. Observations: | 23 | AIC: | 217.3 |
| Df Residuals: | 21 | BIC: | 219.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -356.7682 | 109.706 | -3.252 | 0.004 | -584.915 -128.621 |
| expression | 42.7134 | 10.722 | 3.984 | 0.001 | 20.415 65.012 |
| Omnibus: | 0.750 | Durbin-Watson: | 2.262 |
| Prob(Omnibus): | 0.687 | Jarque-Bera (JB): | 0.788 |
| Skew: | 0.300 | Prob(JB): | 0.674 |
| Kurtosis: | 2.320 | Cond. No. | 208. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.066 | 0.801 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.497 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 3.629 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0486 |
| Time: | 22:53:03 | Log-Likelihood: | -70.140 |
| No. Observations: | 15 | AIC: | 148.3 |
| Df Residuals: | 11 | BIC: | 151.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 198.5280 | 169.787 | 1.169 | 0.267 | -175.171 572.227 |
| C(dose)[T.1] | -264.2583 | 316.850 | -0.834 | 0.422 | -961.642 433.125 |
| expression | -17.1612 | 22.175 | -0.774 | 0.455 | -65.967 31.645 |
| expression:C(dose)[T.1] | 39.4553 | 39.493 | 0.999 | 0.339 | -47.468 126.379 |
| Omnibus: | 1.251 | Durbin-Watson: | 0.959 |
| Prob(Omnibus): | 0.535 | Jarque-Bera (JB): | 1.048 |
| Skew: | -0.491 | Prob(JB): | 0.592 |
| Kurtosis: | 2.156 | Cond. No. | 404. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.945 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0271 |
| Time: | 22:53:03 | Log-Likelihood: | -70.792 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 103.5025 | 140.633 | 0.736 | 0.476 | -202.910 409.915 |
| C(dose)[T.1] | 51.7473 | 18.564 | 2.788 | 0.016 | 11.300 92.194 |
| expression | -4.7221 | 18.348 | -0.257 | 0.801 | -44.699 35.254 |
| Omnibus: | 3.559 | Durbin-Watson: | 0.765 |
| Prob(Omnibus): | 0.169 | Jarque-Bera (JB): | 2.266 |
| Skew: | -0.949 | Prob(JB): | 0.322 |
| Kurtosis: | 2.844 | Cond. No. | 146. |
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: | 22:53:03 | 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: | 0.097 |
| Model: | OLS | Adj. R-squared: | 0.027 |
| Method: | Least Squares | F-statistic: | 1.394 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.259 |
| Time: | 22:53:04 | Log-Likelihood: | -74.536 |
| No. Observations: | 15 | AIC: | 153.1 |
| Df Residuals: | 13 | BIC: | 154.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -85.3755 | 151.972 | -0.562 | 0.584 | -413.691 242.940 |
| expression | 22.5852 | 19.132 | 1.181 | 0.259 | -18.746 63.917 |
| Omnibus: | 0.196 | Durbin-Watson: | 1.288 |
| Prob(Omnibus): | 0.907 | Jarque-Bera (JB): | 0.393 |
| Skew: | -0.096 | Prob(JB): | 0.822 |
| Kurtosis: | 2.231 | Cond. No. | 127. |