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 |
| 1.585 | 0.223 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.684 |
| Model: | OLS | Adj. R-squared: | 0.634 |
| Method: | Least Squares | F-statistic: | 13.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.43e-05 |
| Time: | 04:31:04 | Log-Likelihood: | -99.866 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 19 | BIC: | 212.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 68.8623 | 84.237 | 0.817 | 0.424 | -107.447 245.172 |
| C(dose)[T.1] | 132.6874 | 106.944 | 1.241 | 0.230 | -91.149 356.524 |
| expression | -4.6258 | 26.526 | -0.174 | 0.863 | -60.145 50.893 |
| expression:C(dose)[T.1] | -24.4409 | 33.392 | -0.732 | 0.473 | -94.331 45.449 |
| Omnibus: | 0.957 | Durbin-Watson: | 1.697 |
| Prob(Omnibus): | 0.620 | Jarque-Bera (JB): | 0.929 |
| Skew: | 0.342 | Prob(JB): | 0.628 |
| Kurtosis: | 2.291 | Cond. No. | 121. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.642 |
| Method: | Least Squares | F-statistic: | 20.75 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.32e-05 |
| Time: | 04:31:04 | Log-Likelihood: | -100.19 |
| No. Observations: | 23 | AIC: | 206.4 |
| Df Residuals: | 20 | BIC: | 209.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 117.7198 | 50.783 | 2.318 | 0.031 | 11.788 223.651 |
| C(dose)[T.1] | 54.6648 | 8.507 | 6.426 | 0.000 | 36.919 72.411 |
| expression | -20.0487 | 15.924 | -1.259 | 0.223 | -53.266 13.169 |
| Omnibus: | 0.773 | Durbin-Watson: | 1.511 |
| Prob(Omnibus): | 0.679 | Jarque-Bera (JB): | 0.781 |
| Skew: | 0.253 | Prob(JB): | 0.677 |
| Kurtosis: | 2.252 | Cond. No. | 42.9 |
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: | 04:31:04 | 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: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.07443 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.788 |
| Time: | 04:31:04 | 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 | 103.2808 | 86.671 | 1.192 | 0.247 | -76.961 283.522 |
| expression | -7.3646 | 26.995 | -0.273 | 0.788 | -63.503 48.774 |
| Omnibus: | 3.438 | Durbin-Watson: | 2.483 |
| Prob(Omnibus): | 0.179 | Jarque-Bera (JB): | 1.565 |
| Skew: | 0.270 | Prob(JB): | 0.457 |
| Kurtosis: | 1.841 | Cond. No. | 42.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.248 | 0.627 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.461 |
| Model: | OLS | Adj. R-squared: | 0.314 |
| Method: | Least Squares | F-statistic: | 3.134 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0695 |
| Time: | 04:31:04 | Log-Likelihood: | -70.667 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 110.7512 | 94.565 | 1.171 | 0.266 | -97.384 318.887 |
| C(dose)[T.1] | 26.0983 | 146.227 | 0.178 | 0.862 | -295.744 347.941 |
| expression | -12.9083 | 27.953 | -0.462 | 0.653 | -74.433 48.617 |
| expression:C(dose)[T.1] | 6.2639 | 45.943 | 0.136 | 0.894 | -94.856 107.384 |
| Omnibus: | 1.788 | Durbin-Watson: | 0.876 |
| Prob(Omnibus): | 0.409 | Jarque-Bera (JB): | 1.336 |
| Skew: | -0.680 | Prob(JB): | 0.513 |
| Kurtosis: | 2.461 | Cond. No. | 79.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.460 |
| Model: | OLS | Adj. R-squared: | 0.370 |
| Method: | Least Squares | F-statistic: | 5.110 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0248 |
| Time: | 04:31:04 | Log-Likelihood: | -70.679 |
| 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 | 102.9687 | 72.245 | 1.425 | 0.180 | -54.440 260.377 |
| C(dose)[T.1] | 45.8887 | 16.935 | 2.710 | 0.019 | 8.990 82.788 |
| expression | -10.5895 | 21.257 | -0.498 | 0.627 | -56.905 35.726 |
| Omnibus: | 1.737 | Durbin-Watson: | 0.889 |
| Prob(Omnibus): | 0.420 | Jarque-Bera (JB): | 1.296 |
| Skew: | -0.669 | Prob(JB): | 0.523 |
| Kurtosis: | 2.469 | Cond. No. | 33.1 |
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: | 04:31:04 | 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: | 0.130 |
| Model: | OLS | Adj. R-squared: | 0.063 |
| Method: | Least Squares | F-statistic: | 1.934 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.188 |
| Time: | 04:31:04 | Log-Likelihood: | -74.260 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 153.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 199.4752 | 76.670 | 2.602 | 0.022 | 33.840 365.111 |
| expression | -33.1731 | 23.853 | -1.391 | 0.188 | -84.705 18.358 |
| Omnibus: | 1.174 | Durbin-Watson: | 1.522 |
| Prob(Omnibus): | 0.556 | Jarque-Bera (JB): | 0.842 |
| Skew: | 0.254 | Prob(JB): | 0.656 |
| Kurtosis: | 1.956 | Cond. No. | 28.5 |