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 |
| 0.480 | 0.496 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.15 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000114 |
| Time: | 04:46:07 | Log-Likelihood: | -100.79 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 113.4386 | 114.262 | 0.993 | 0.333 | -125.715 352.592 |
| C(dose)[T.1] | 44.3449 | 162.830 | 0.272 | 0.788 | -296.463 385.153 |
| expression | -7.4747 | 14.399 | -0.519 | 0.610 | -37.611 22.662 |
| expression:C(dose)[T.1] | 0.8516 | 20.988 | 0.041 | 0.968 | -43.077 44.781 |
| Omnibus: | 0.080 | Durbin-Watson: | 1.925 |
| Prob(Omnibus): | 0.961 | Jarque-Bera (JB): | 0.184 |
| Skew: | -0.118 | Prob(JB): | 0.912 |
| Kurtosis: | 2.631 | Cond. No. | 371. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.18 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.24e-05 |
| Time: | 04:46:07 | Log-Likelihood: | -100.79 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 110.2627 | 81.136 | 1.359 | 0.189 | -58.985 279.510 |
| C(dose)[T.1] | 50.9402 | 9.332 | 5.459 | 0.000 | 31.475 70.406 |
| expression | -7.0739 | 10.211 | -0.693 | 0.496 | -28.374 14.226 |
| Omnibus: | 0.110 | Durbin-Watson: | 1.917 |
| Prob(Omnibus): | 0.947 | Jarque-Bera (JB): | 0.182 |
| Skew: | -0.135 | Prob(JB): | 0.913 |
| Kurtosis: | 2.657 | Cond. No. | 149. |
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: | 04:46:07 | 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: | 0.147 |
| Model: | OLS | Adj. R-squared: | 0.106 |
| Method: | Least Squares | F-statistic: | 3.609 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0713 |
| Time: | 04:46:07 | Log-Likelihood: | -111.28 |
| No. Observations: | 23 | AIC: | 226.6 |
| Df Residuals: | 21 | BIC: | 228.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 295.0500 | 113.551 | 2.598 | 0.017 | 58.907 531.193 |
| expression | -27.7417 | 14.604 | -1.900 | 0.071 | -58.112 2.629 |
| Omnibus: | 7.328 | Durbin-Watson: | 2.080 |
| Prob(Omnibus): | 0.026 | Jarque-Bera (JB): | 1.891 |
| Skew: | 0.031 | Prob(JB): | 0.388 |
| Kurtosis: | 1.597 | Cond. No. | 135. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.023 | 0.883 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.323 |
| Method: | Least Squares | F-statistic: | 3.231 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0647 |
| Time: | 04:46:07 | Log-Likelihood: | -70.561 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 338.5158 | 633.540 | 0.534 | 0.604 | -1055.897 1732.928 |
| C(dose)[T.1] | -421.8552 | 761.855 | -0.554 | 0.591 | -2098.687 1254.976 |
| expression | -32.1666 | 75.161 | -0.428 | 0.677 | -197.596 133.262 |
| expression:C(dose)[T.1] | 56.6577 | 91.287 | 0.621 | 0.547 | -144.263 257.579 |
| Omnibus: | 1.676 | Durbin-Watson: | 0.865 |
| Prob(Omnibus): | 0.433 | Jarque-Bera (JB): | 1.325 |
| Skew: | -0.646 | Prob(JB): | 0.516 |
| Kurtosis: | 2.329 | Cond. No. | 1.14e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.905 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 04:46:07 | Log-Likelihood: | -70.819 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 14.8205 | 350.348 | 0.042 | 0.967 | -748.522 778.163 |
| C(dose)[T.1] | 50.8371 | 19.145 | 2.655 | 0.021 | 9.124 92.550 |
| expression | 6.2423 | 41.549 | 0.150 | 0.883 | -84.285 96.770 |
| Omnibus: | 2.516 | Durbin-Watson: | 0.866 |
| Prob(Omnibus): | 0.284 | Jarque-Bera (JB): | 1.781 |
| Skew: | -0.814 | Prob(JB): | 0.411 |
| Kurtosis: | 2.555 | Cond. No. | 376. |
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: | 04:46:07 | 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: | 0.127 |
| Model: | OLS | Adj. R-squared: | 0.059 |
| Method: | Least Squares | F-statistic: | 1.883 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.193 |
| Time: | 04:46:07 | Log-Likelihood: | -74.286 |
| No. Observations: | 15 | AIC: | 152.6 |
| Df Residuals: | 13 | BIC: | 154.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 563.4842 | 342.508 | 1.645 | 0.124 | -176.460 1303.428 |
| expression | -56.6904 | 41.313 | -1.372 | 0.193 | -145.941 32.560 |
| Omnibus: | 0.445 | Durbin-Watson: | 1.179 |
| Prob(Omnibus): | 0.800 | Jarque-Bera (JB): | 0.517 |
| Skew: | -0.004 | Prob(JB): | 0.772 |
| Kurtosis: | 2.090 | Cond. No. | 303. |