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 |
| 0.584 | 0.454 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.731 |
| Model: | OLS | Adj. R-squared: | 0.689 |
| Method: | Least Squares | F-statistic: | 17.22 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 1.20e-05 |
| Time: | 17:26:20 | Log-Likelihood: | -97.998 |
| No. Observations: | 23 | AIC: | 204.0 |
| Df Residuals: | 19 | BIC: | 208.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.6779 | 31.991 | 0.021 | 0.983 | -66.280 67.636 |
| C(dose)[T.1] | 211.1276 | 69.131 | 3.054 | 0.007 | 66.434 355.821 |
| expression | 9.4011 | 5.536 | 1.698 | 0.106 | -2.187 20.989 |
| expression:C(dose)[T.1] | -30.2130 | 13.380 | -2.258 | 0.036 | -58.217 -2.209 |
| Omnibus: | 1.441 | Durbin-Watson: | 1.786 |
| Prob(Omnibus): | 0.486 | Jarque-Bera (JB): | 0.559 |
| Skew: | -0.363 | Prob(JB): | 0.756 |
| Kurtosis: | 3.239 | Cond. No. | 112. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.33 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.13e-05 |
| Time: | 17:26:20 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.1340 | 32.065 | 0.940 | 0.359 | -36.752 97.020 |
| C(dose)[T.1] | 56.2310 | 9.438 | 5.958 | 0.000 | 36.545 75.917 |
| expression | 4.2280 | 5.533 | 0.764 | 0.454 | -7.313 15.769 |
| Omnibus: | 0.471 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.790 | Jarque-Bera (JB): | 0.567 |
| Skew: | -0.070 | Prob(JB): | 0.753 |
| Kurtosis: | 2.244 | Cond. No. | 42.2 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 17:26:20 | 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: | 0.054 |
| Model: | OLS | Adj. R-squared: | 0.009 |
| Method: | Least Squares | F-statistic: | 1.193 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.287 |
| Time: | 17:26:20 | Log-Likelihood: | -112.47 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 128.0108 | 44.767 | 2.859 | 0.009 | 34.912 221.110 |
| expression | -8.9987 | 8.238 | -1.092 | 0.287 | -26.131 8.134 |
| Omnibus: | 5.469 | Durbin-Watson: | 2.407 |
| Prob(Omnibus): | 0.065 | Jarque-Bera (JB): | 1.698 |
| Skew: | 0.097 | Prob(JB): | 0.428 |
| Kurtosis: | 1.683 | Cond. No. | 35.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.048 | 0.326 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.355 |
| Method: | Least Squares | F-statistic: | 3.571 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0506 |
| Time: | 17:26:20 | Log-Likelihood: | -70.200 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -10.6018 | 146.286 | -0.072 | 0.944 | -332.575 311.371 |
| C(dose)[T.1] | 61.0993 | 167.100 | 0.366 | 0.722 | -306.686 428.884 |
| expression | 13.8296 | 25.846 | 0.535 | 0.603 | -43.058 70.717 |
| expression:C(dose)[T.1] | -2.5184 | 29.249 | -0.086 | 0.933 | -66.895 61.858 |
| Omnibus: | 1.701 | Durbin-Watson: | 0.982 |
| Prob(Omnibus): | 0.427 | Jarque-Bera (JB): | 1.356 |
| Skew: | -0.638 | Prob(JB): | 0.508 |
| Kurtosis: | 2.264 | Cond. No. | 191. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 5.835 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0170 |
| Time: | 17:26:20 | Log-Likelihood: | -70.205 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 0.4940 | 66.304 | 0.007 | 0.994 | -143.970 144.958 |
| C(dose)[T.1] | 46.7774 | 15.278 | 3.062 | 0.010 | 13.489 80.065 |
| expression | 11.8630 | 11.588 | 1.024 | 0.326 | -13.384 37.110 |
| Omnibus: | 1.648 | Durbin-Watson: | 0.954 |
| Prob(Omnibus): | 0.439 | Jarque-Bera (JB): | 1.318 |
| Skew: | -0.630 | Prob(JB): | 0.517 |
| Kurtosis: | 2.278 | Cond. No. | 52.7 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 17:26:20 | 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: | 0.028 |
| Method: | Least Squares | F-statistic: | 1.397 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.258 |
| Time: | 17:26:20 | Log-Likelihood: | -74.535 |
| 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 | -6.1143 | 84.973 | -0.072 | 0.944 | -189.687 177.459 |
| expression | 17.3501 | 14.680 | 1.182 | 0.258 | -14.363 49.063 |
| Omnibus: | 0.327 | Durbin-Watson: | 1.461 |
| Prob(Omnibus): | 0.849 | Jarque-Bera (JB): | 0.470 |
| Skew: | 0.217 | Prob(JB): | 0.790 |
| Kurtosis: | 2.249 | Cond. No. | 52.4 |