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 |
| 0.528 | 0.476 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.670 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 12.89 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.96e-05 |
| Time: | 03:52:57 | Log-Likelihood: | -100.34 |
| No. Observations: | 23 | AIC: | 208.7 |
| Df Residuals: | 19 | BIC: | 213.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.0246 | 61.618 | 0.714 | 0.484 | -84.944 172.993 |
| C(dose)[T.1] | -38.9608 | 111.958 | -0.348 | 0.732 | -273.292 195.371 |
| expression | 1.4180 | 8.539 | 0.166 | 0.870 | -16.454 19.290 |
| expression:C(dose)[T.1] | 13.6635 | 16.165 | 0.845 | 0.408 | -20.169 47.496 |
| Omnibus: | 1.056 | Durbin-Watson: | 2.026 |
| Prob(Omnibus): | 0.590 | Jarque-Bera (JB): | 0.948 |
| Skew: | 0.298 | Prob(JB): | 0.622 |
| Kurtosis: | 2.204 | Cond. No. | 218. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.25 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.18e-05 |
| Time: | 03:52:57 | Log-Likelihood: | -100.76 |
| 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 | 16.6429 | 52.041 | 0.320 | 0.752 | -91.912 125.198 |
| C(dose)[T.1] | 55.3581 | 9.092 | 6.089 | 0.000 | 36.392 74.324 |
| expression | 5.2308 | 7.198 | 0.727 | 0.476 | -9.785 20.246 |
| Omnibus: | 1.436 | Durbin-Watson: | 1.818 |
| Prob(Omnibus): | 0.488 | Jarque-Bera (JB): | 0.938 |
| Skew: | 0.104 | Prob(JB): | 0.626 |
| Kurtosis: | 2.033 | Cond. No. | 86.6 |
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: | 03:52:57 | 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: | 0.024 |
| Model: | OLS | Adj. R-squared: | -0.022 |
| Method: | Least Squares | F-statistic: | 0.5237 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.477 |
| Time: | 03:52:57 | Log-Likelihood: | -112.82 |
| 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 | 136.9206 | 79.369 | 1.725 | 0.099 | -28.137 301.978 |
| expression | -8.1757 | 11.298 | -0.724 | 0.477 | -31.671 15.320 |
| Omnibus: | 3.197 | Durbin-Watson: | 2.295 |
| Prob(Omnibus): | 0.202 | Jarque-Bera (JB): | 1.549 |
| Skew: | 0.290 | Prob(JB): | 0.461 |
| Kurtosis: | 1.868 | Cond. No. | 79.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.007 | 0.937 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.467 |
| Model: | OLS | Adj. R-squared: | 0.321 |
| Method: | Least Squares | F-statistic: | 3.207 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0658 |
| Time: | 03:52:57 | Log-Likelihood: | -70.587 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.6858 | 90.073 | 1.018 | 0.331 | -106.565 289.936 |
| C(dose)[T.1] | -50.5332 | 166.362 | -0.304 | 0.767 | -416.694 315.628 |
| expression | -3.5473 | 13.058 | -0.272 | 0.791 | -32.288 25.194 |
| expression:C(dose)[T.1] | 13.9814 | 23.283 | 0.600 | 0.560 | -37.264 65.227 |
| Omnibus: | 2.187 | Durbin-Watson: | 0.677 |
| Prob(Omnibus): | 0.335 | Jarque-Bera (JB): | 1.574 |
| Skew: | -0.756 | Prob(JB): | 0.455 |
| Kurtosis: | 2.520 | Cond. No. | 184. |
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: | 4.891 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 03:52:57 | Log-Likelihood: | -70.829 |
| 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 | 61.6122 | 72.845 | 0.846 | 0.414 | -97.104 220.328 |
| C(dose)[T.1] | 48.8605 | 16.275 | 3.002 | 0.011 | 13.401 84.320 |
| expression | 0.8506 | 10.519 | 0.081 | 0.937 | -22.069 23.770 |
| Omnibus: | 2.754 | Durbin-Watson: | 0.814 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.901 |
| Skew: | -0.851 | Prob(JB): | 0.387 |
| Kurtosis: | 2.616 | Cond. No. | 67.3 |
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: | 03:52:57 | 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: | 0.035 |
| Model: | OLS | Adj. R-squared: | -0.039 |
| Method: | Least Squares | F-statistic: | 0.4752 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.503 |
| Time: | 03:52:57 | Log-Likelihood: | -75.031 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.8369 | 91.693 | 0.336 | 0.742 | -167.253 228.927 |
| expression | 8.9134 | 12.931 | 0.689 | 0.503 | -19.022 36.849 |
| Omnibus: | 0.188 | Durbin-Watson: | 1.495 |
| Prob(Omnibus): | 0.910 | Jarque-Bera (JB): | 0.387 |
| Skew: | -0.099 | Prob(JB): | 0.824 |
| Kurtosis: | 2.239 | Cond. No. | 66.4 |