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 |
| 1.114 | 0.304 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.669 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 12.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.38e-05 |
| Time: | 04:45:01 | Log-Likelihood: | -100.40 |
| No. Observations: | 23 | AIC: | 208.8 |
| Df Residuals: | 19 | BIC: | 213.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -53.1040 | 192.259 | -0.276 | 0.785 | -455.506 349.298 |
| C(dose)[T.1] | 102.3307 | 202.971 | 0.504 | 0.620 | -322.492 527.153 |
| expression | 12.6819 | 22.709 | 0.558 | 0.583 | -34.849 60.213 |
| expression:C(dose)[T.1] | -5.9184 | 23.919 | -0.247 | 0.807 | -55.982 44.145 |
| Omnibus: | 0.449 | Durbin-Watson: | 2.163 |
| Prob(Omnibus): | 0.799 | Jarque-Bera (JB): | 0.578 |
| Skew: | 0.215 | Prob(JB): | 0.749 |
| Kurtosis: | 2.353 | Cond. No. | 624. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.634 |
| Method: | Least Squares | F-statistic: | 20.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.65e-05 |
| Time: | 04:45:01 | Log-Likelihood: | -100.44 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -7.9606 | 59.200 | -0.134 | 0.894 | -131.449 115.528 |
| C(dose)[T.1] | 52.1560 | 8.608 | 6.059 | 0.000 | 34.199 70.113 |
| expression | 7.3469 | 6.961 | 1.055 | 0.304 | -7.174 21.868 |
| Omnibus: | 0.423 | Durbin-Watson: | 2.155 |
| Prob(Omnibus): | 0.810 | Jarque-Bera (JB): | 0.560 |
| Skew: | 0.202 | Prob(JB): | 0.756 |
| Kurtosis: | 2.352 | Cond. No. | 121. |
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: | 04:45:02 | 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: | 0.057 |
| Model: | OLS | Adj. R-squared: | 0.013 |
| Method: | Least Squares | F-statistic: | 1.280 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.271 |
| Time: | 04:45:02 | Log-Likelihood: | -112.42 |
| No. Observations: | 23 | AIC: | 228.8 |
| Df Residuals: | 21 | BIC: | 231.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -29.8325 | 97.101 | -0.307 | 0.762 | -231.765 172.100 |
| expression | 12.8297 | 11.342 | 1.131 | 0.271 | -10.758 36.417 |
| Omnibus: | 2.266 | Durbin-Watson: | 2.594 |
| Prob(Omnibus): | 0.322 | Jarque-Bera (JB): | 1.468 |
| Skew: | 0.373 | Prob(JB): | 0.480 |
| Kurtosis: | 2.012 | Cond. No. | 120. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.987 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.551 |
| Model: | OLS | Adj. R-squared: | 0.428 |
| Method: | Least Squares | F-statistic: | 4.495 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0272 |
| Time: | 04:45:02 | Log-Likelihood: | -69.299 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 11 | BIC: | 149.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 241.2834 | 216.880 | 1.113 | 0.290 | -236.066 718.632 |
| C(dose)[T.1] | -611.5896 | 418.504 | -1.461 | 0.172 | -1532.711 309.532 |
| expression | -18.4736 | 23.017 | -0.803 | 0.439 | -69.133 32.186 |
| expression:C(dose)[T.1] | 72.9775 | 46.191 | 1.580 | 0.142 | -28.687 174.642 |
| Omnibus: | 1.274 | Durbin-Watson: | 1.347 |
| Prob(Omnibus): | 0.529 | Jarque-Bera (JB): | 0.846 |
| Skew: | -0.554 | Prob(JB): | 0.655 |
| Kurtosis: | 2.642 | Cond. No. | 628. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:45:02 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.7541 | 199.496 | 0.355 | 0.729 | -363.911 505.419 |
| C(dose)[T.1] | 49.0278 | 18.700 | 2.622 | 0.022 | 8.284 89.771 |
| expression | -0.3534 | 21.163 | -0.017 | 0.987 | -46.464 45.757 |
| Omnibus: | 2.752 | Durbin-Watson: | 0.816 |
| Prob(Omnibus): | 0.253 | Jarque-Bera (JB): | 1.884 |
| Skew: | -0.849 | Prob(JB): | 0.390 |
| Kurtosis: | 2.633 | Cond. No. | 236. |
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: | 04:45:02 | 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: | 0.133 |
| Model: | OLS | Adj. R-squared: | 0.066 |
| Method: | Least Squares | F-statistic: | 1.995 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.181 |
| Time: | 04:45:02 | Log-Likelihood: | -74.229 |
| 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 | 371.2349 | 196.753 | 1.887 | 0.082 | -53.825 796.295 |
| expression | -30.3137 | 21.463 | -1.412 | 0.181 | -76.681 16.054 |
| Omnibus: | 0.836 | Durbin-Watson: | 1.754 |
| Prob(Omnibus): | 0.658 | Jarque-Bera (JB): | 0.676 |
| Skew: | -0.123 | Prob(JB): | 0.713 |
| Kurtosis: | 1.990 | Cond. No. | 193. |