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 |
| 0.670 | 0.423 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 13.03 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.41e-05 |
| Time: | 04:03:38 | Log-Likelihood: | -100.25 |
| No. Observations: | 23 | AIC: | 208.5 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 59.2803 | 168.313 | 0.352 | 0.729 | -293.004 411.564 |
| C(dose)[T.1] | 288.3218 | 264.285 | 1.091 | 0.289 | -264.834 841.477 |
| expression | -0.7703 | 25.546 | -0.030 | 0.976 | -54.238 52.697 |
| expression:C(dose)[T.1] | -32.5156 | 38.079 | -0.854 | 0.404 | -112.217 47.186 |
| Omnibus: | 1.280 | Durbin-Watson: | 1.718 |
| Prob(Omnibus): | 0.527 | Jarque-Bera (JB): | 0.957 |
| Skew: | 0.209 | Prob(JB): | 0.620 |
| Kurtosis: | 2.093 | Cond. No. | 544. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 19.45 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.04e-05 |
| Time: | 04:03:38 | Log-Likelihood: | -100.68 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 155.6342 | 124.036 | 1.255 | 0.224 | -103.101 414.369 |
| C(dose)[T.1] | 63.0019 | 14.622 | 4.309 | 0.000 | 32.502 93.502 |
| expression | -15.4037 | 18.816 | -0.819 | 0.423 | -54.653 23.845 |
| Omnibus: | 0.736 | Durbin-Watson: | 1.813 |
| Prob(Omnibus): | 0.692 | Jarque-Bera (JB): | 0.759 |
| Skew: | 0.247 | Prob(JB): | 0.684 |
| Kurtosis: | 2.259 | Cond. No. | 204. |
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: | 04:03:38 | 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: | 0.345 |
| Model: | OLS | Adj. R-squared: | 0.314 |
| Method: | Least Squares | F-statistic: | 11.07 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00320 |
| Time: | 04:03:38 | Log-Likelihood: | -108.24 |
| No. Observations: | 23 | AIC: | 220.5 |
| Df Residuals: | 21 | BIC: | 222.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -264.9023 | 103.735 | -2.554 | 0.018 | -480.630 -49.174 |
| expression | 50.0565 | 15.044 | 3.327 | 0.003 | 18.771 81.342 |
| Omnibus: | 0.448 | Durbin-Watson: | 2.029 |
| Prob(Omnibus): | 0.799 | Jarque-Bera (JB): | 0.557 |
| Skew: | -0.081 | Prob(JB): | 0.757 |
| Kurtosis: | 2.255 | Cond. No. | 125. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.330 | 0.577 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.317 |
| Method: | Least Squares | F-statistic: | 3.169 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0677 |
| Time: | 04:03:38 | Log-Likelihood: | -70.628 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.0254 | 122.176 | 1.081 | 0.303 | -136.883 400.934 |
| C(dose)[T.1] | 36.0919 | 366.918 | 0.098 | 0.923 | -771.490 843.673 |
| expression | -10.4161 | 19.608 | -0.531 | 0.606 | -53.573 32.741 |
| expression:C(dose)[T.1] | 2.6139 | 55.945 | 0.047 | 0.964 | -120.521 125.749 |
| Omnibus: | 2.109 | Durbin-Watson: | 0.913 |
| Prob(Omnibus): | 0.348 | Jarque-Bera (JB): | 1.399 |
| Skew: | -0.728 | Prob(JB): | 0.497 |
| Kurtosis: | 2.660 | Cond. No. | 354. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 5.184 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0238 |
| Time: | 04:03:38 | Log-Likelihood: | -70.630 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 130.0341 | 109.638 | 1.186 | 0.259 | -108.847 368.915 |
| C(dose)[T.1] | 53.2148 | 17.033 | 3.124 | 0.009 | 16.104 90.325 |
| expression | -10.0950 | 17.584 | -0.574 | 0.577 | -48.407 28.217 |
| Omnibus: | 2.230 | Durbin-Watson: | 0.910 |
| Prob(Omnibus): | 0.328 | Jarque-Bera (JB): | 1.445 |
| Skew: | -0.745 | Prob(JB): | 0.485 |
| Kurtosis: | 2.699 | Cond. No. | 93.7 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:03:38 | 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: | 0.027 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.3622 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.558 |
| Time: | 04:03:38 | Log-Likelihood: | -75.094 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 13.6104 | 133.406 | 0.102 | 0.920 | -274.597 301.817 |
| expression | 12.4816 | 20.741 | 0.602 | 0.558 | -32.326 57.289 |
| Omnibus: | 0.128 | Durbin-Watson: | 1.520 |
| Prob(Omnibus): | 0.938 | Jarque-Bera (JB): | 0.236 |
| Skew: | -0.175 | Prob(JB): | 0.889 |
| Kurtosis: | 2.495 | Cond. No. | 87.7 |