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 |
| 0.797 | 0.383 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.612 |
| Method: | Least Squares | F-statistic: | 12.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.21e-05 |
| Time: | 04:59:50 | Log-Likelihood: | -100.52 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.2638 | 172.204 | 0.193 | 0.849 | -327.164 393.692 |
| C(dose)[T.1] | -26.0097 | 202.001 | -0.129 | 0.899 | -448.802 396.783 |
| expression | 2.7152 | 22.310 | 0.122 | 0.904 | -43.981 49.411 |
| expression:C(dose)[T.1] | 10.3823 | 26.215 | 0.396 | 0.696 | -44.486 65.251 |
| Omnibus: | 0.736 | Durbin-Watson: | 1.863 |
| Prob(Omnibus): | 0.692 | Jarque-Bera (JB): | 0.780 |
| Skew: | 0.321 | Prob(JB): | 0.677 |
| Kurtosis: | 2.365 | Cond. No. | 516. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.92e-05 |
| Time: | 04:59:50 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -24.7422 | 88.639 | -0.279 | 0.783 | -209.641 160.156 |
| C(dose)[T.1] | 53.9153 | 8.625 | 6.251 | 0.000 | 35.925 71.906 |
| expression | 10.2350 | 11.465 | 0.893 | 0.383 | -13.681 34.151 |
| Omnibus: | 0.682 | Durbin-Watson: | 1.771 |
| Prob(Omnibus): | 0.711 | Jarque-Bera (JB): | 0.724 |
| Skew: | 0.233 | Prob(JB): | 0.696 |
| Kurtosis: | 2.266 | Cond. No. | 162. |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:59:50 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.06403 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.803 |
| Time: | 04:59:50 | Log-Likelihood: | -113.07 |
| 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 | 42.4182 | 147.579 | 0.287 | 0.777 | -264.488 349.325 |
| expression | 4.8524 | 19.176 | 0.253 | 0.803 | -35.027 44.731 |
| Omnibus: | 3.076 | Durbin-Watson: | 2.488 |
| Prob(Omnibus): | 0.215 | Jarque-Bera (JB): | 1.511 |
| Skew: | 0.282 | Prob(JB): | 0.470 |
| Kurtosis: | 1.878 | Cond. No. | 160. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.548 | 0.473 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.473 |
| Model: | OLS | Adj. R-squared: | 0.329 |
| Method: | Least Squares | F-statistic: | 3.291 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0619 |
| Time: | 04:59:50 | Log-Likelihood: | -70.496 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 181.8425 | 178.528 | 1.019 | 0.330 | -211.095 574.780 |
| C(dose)[T.1] | 23.5187 | 341.478 | 0.069 | 0.946 | -728.070 775.107 |
| expression | -15.2578 | 23.756 | -0.642 | 0.534 | -67.545 37.029 |
| expression:C(dose)[T.1] | 2.6398 | 47.700 | 0.055 | 0.957 | -102.347 107.626 |
| Omnibus: | 2.111 | Durbin-Watson: | 0.951 |
| Prob(Omnibus): | 0.348 | Jarque-Bera (JB): | 1.461 |
| Skew: | -0.737 | Prob(JB): | 0.482 |
| Kurtosis: | 2.592 | Cond. No. | 377. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.473 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 5.382 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0215 |
| Time: | 04:59:50 | Log-Likelihood: | -70.498 |
| 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 | 176.9326 | 148.347 | 1.193 | 0.256 | -146.288 500.153 |
| C(dose)[T.1] | 42.3882 | 17.930 | 2.364 | 0.036 | 3.321 81.455 |
| expression | -14.6030 | 19.726 | -0.740 | 0.473 | -57.583 28.376 |
| Omnibus: | 2.077 | Durbin-Watson: | 0.935 |
| Prob(Omnibus): | 0.354 | Jarque-Bera (JB): | 1.441 |
| Skew: | -0.731 | Prob(JB): | 0.487 |
| Kurtosis: | 2.588 | Cond. No. | 144. |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:59:50 | 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: | 0.227 |
| Model: | OLS | Adj. R-squared: | 0.168 |
| Method: | Least Squares | F-statistic: | 3.825 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0723 |
| Time: | 04:59:50 | Log-Likelihood: | -73.366 |
| No. Observations: | 15 | AIC: | 150.7 |
| Df Residuals: | 13 | BIC: | 152.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 372.9542 | 143.082 | 2.607 | 0.022 | 63.844 682.065 |
| expression | -38.5221 | 19.697 | -1.956 | 0.072 | -81.075 4.030 |
| Omnibus: | 1.374 | Durbin-Watson: | 1.447 |
| Prob(Omnibus): | 0.503 | Jarque-Bera (JB): | 0.819 |
| Skew: | 0.094 | Prob(JB): | 0.664 |
| Kurtosis: | 1.871 | Cond. No. | 119. |