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 |
| 0.194 | 0.665 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.687 |
| Model: | OLS | Adj. R-squared: | 0.637 |
| Method: | Least Squares | F-statistic: | 13.87 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.99e-05 |
| Time: | 03:56:47 | Log-Likelihood: | -99.762 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 19 | BIC: | 212.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 284.0912 | 174.932 | 1.624 | 0.121 | -82.046 650.228 |
| C(dose)[T.1] | -316.5824 | 257.911 | -1.227 | 0.235 | -856.396 223.231 |
| expression | -28.3186 | 21.537 | -1.315 | 0.204 | -73.397 16.759 |
| expression:C(dose)[T.1] | 45.2549 | 31.443 | 1.439 | 0.166 | -20.556 111.066 |
| Omnibus: | 1.042 | Durbin-Watson: | 2.087 |
| Prob(Omnibus): | 0.594 | Jarque-Bera (JB): | 0.799 |
| Skew: | -0.065 | Prob(JB): | 0.671 |
| Kurtosis: | 2.096 | Cond. No. | 646. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.77 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.57e-05 |
| Time: | 03:56:48 | Log-Likelihood: | -100.95 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.7313 | 130.886 | 0.854 | 0.403 | -161.293 384.755 |
| C(dose)[T.1] | 54.4051 | 9.059 | 6.006 | 0.000 | 35.508 73.302 |
| expression | -7.0861 | 16.106 | -0.440 | 0.665 | -40.683 26.511 |
| Omnibus: | 0.348 | Durbin-Watson: | 1.879 |
| Prob(Omnibus): | 0.840 | Jarque-Bera (JB): | 0.498 |
| Skew: | 0.015 | Prob(JB): | 0.780 |
| Kurtosis: | 2.280 | Cond. No. | 250. |
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: | 03:56:48 | 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: | 0.026 |
| Model: | OLS | Adj. R-squared: | -0.021 |
| Method: | Least Squares | F-statistic: | 0.5518 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.466 |
| Time: | 03:56:48 | Log-Likelihood: | -112.81 |
| No. Observations: | 23 | AIC: | 229.6 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -74.5340 | 207.774 | -0.359 | 0.723 | -506.624 357.556 |
| expression | 18.8346 | 25.355 | 0.743 | 0.466 | -33.894 71.563 |
| Omnibus: | 2.927 | Durbin-Watson: | 2.362 |
| Prob(Omnibus): | 0.231 | Jarque-Bera (JB): | 1.353 |
| Skew: | 0.180 | Prob(JB): | 0.508 |
| Kurtosis: | 1.867 | Cond. No. | 243. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.621 | 0.446 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.582 |
| Model: | OLS | Adj. R-squared: | 0.467 |
| Method: | Least Squares | F-statistic: | 5.097 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0188 |
| Time: | 03:56:48 | Log-Likelihood: | -68.765 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 11 | BIC: | 148.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 7.3830 | 183.243 | 0.040 | 0.969 | -395.932 410.698 |
| C(dose)[T.1] | 571.3219 | 311.081 | 1.837 | 0.093 | -113.362 1256.006 |
| expression | 7.8886 | 24.035 | 0.328 | 0.749 | -45.011 60.789 |
| expression:C(dose)[T.1] | -66.6174 | 39.961 | -1.667 | 0.124 | -154.572 21.337 |
| Omnibus: | 0.580 | Durbin-Watson: | 1.176 |
| Prob(Omnibus): | 0.748 | Jarque-Bera (JB): | 0.543 |
| Skew: | -0.379 | Prob(JB): | 0.762 |
| Kurtosis: | 2.456 | Cond. No. | 431. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.476 |
| Model: | OLS | Adj. R-squared: | 0.389 |
| Method: | Least Squares | F-statistic: | 5.448 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0207 |
| Time: | 03:56:48 | Log-Likelihood: | -70.455 |
| No. Observations: | 15 | AIC: | 146.9 |
| Df Residuals: | 12 | BIC: | 149.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 190.8099 | 157.017 | 1.215 | 0.248 | -151.300 532.920 |
| C(dose)[T.1] | 53.3517 | 16.229 | 3.287 | 0.006 | 17.992 88.711 |
| expression | -16.2095 | 20.576 | -0.788 | 0.446 | -61.040 28.621 |
| Omnibus: | 0.858 | Durbin-Watson: | 0.876 |
| Prob(Omnibus): | 0.651 | Jarque-Bera (JB): | 0.734 |
| Skew: | -0.462 | Prob(JB): | 0.693 |
| Kurtosis: | 2.433 | Cond. No. | 162. |
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: | 03:56:48 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.05021 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.826 |
| Time: | 03:56:48 | Log-Likelihood: | -75.271 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.9181 | 199.964 | 0.245 | 0.811 | -383.078 480.914 |
| expression | 5.7752 | 25.774 | 0.224 | 0.826 | -49.906 61.456 |
| Omnibus: | 0.501 | Durbin-Watson: | 1.580 |
| Prob(Omnibus): | 0.778 | Jarque-Bera (JB): | 0.542 |
| Skew: | 0.044 | Prob(JB): | 0.763 |
| Kurtosis: | 2.073 | Cond. No. | 156. |