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 |
| 0.310 | 0.584 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.18 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000113 |
| Time: | 23:01:29 | Log-Likelihood: | -100.77 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 153.6914 | 145.428 | 1.057 | 0.304 | -150.693 458.076 |
| C(dose)[T.1] | -29.2880 | 181.854 | -0.161 | 0.874 | -409.912 351.336 |
| expression | -11.5392 | 16.853 | -0.685 | 0.502 | -46.814 23.735 |
| expression:C(dose)[T.1] | 9.4844 | 21.460 | 0.442 | 0.664 | -35.432 54.401 |
| Omnibus: | 0.354 | Durbin-Watson: | 1.969 |
| Prob(Omnibus): | 0.838 | Jarque-Bera (JB): | 0.349 |
| Skew: | 0.250 | Prob(JB): | 0.840 |
| Kurtosis: | 2.661 | Cond. No. | 479. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.94 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.43e-05 |
| Time: | 23:01:30 | Log-Likelihood: | -100.89 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 103.2610 | 88.327 | 1.169 | 0.256 | -80.987 287.509 |
| C(dose)[T.1] | 50.9640 | 9.691 | 5.259 | 0.000 | 30.749 71.179 |
| expression | -5.6897 | 10.221 | -0.557 | 0.584 | -27.011 15.632 |
| Omnibus: | 0.311 | Durbin-Watson: | 1.954 |
| Prob(Omnibus): | 0.856 | Jarque-Bera (JB): | 0.416 |
| Skew: | 0.232 | Prob(JB): | 0.812 |
| Kurtosis: | 2.533 | Cond. No. | 174. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:01:30 | 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: | 0.177 |
| Model: | OLS | Adj. R-squared: | 0.137 |
| Method: | Least Squares | F-statistic: | 4.501 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0459 |
| Time: | 23:01:30 | Log-Likelihood: | -110.87 |
| No. Observations: | 23 | AIC: | 225.7 |
| Df Residuals: | 21 | BIC: | 228.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 326.7946 | 116.641 | 2.802 | 0.011 | 84.227 569.362 |
| expression | -29.3376 | 13.828 | -2.122 | 0.046 | -58.094 -0.581 |
| Omnibus: | 3.104 | Durbin-Watson: | 2.496 |
| Prob(Omnibus): | 0.212 | Jarque-Bera (JB): | 2.489 |
| Skew: | 0.790 | Prob(JB): | 0.288 |
| Kurtosis: | 2.678 | Cond. No. | 152. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.638 | 0.225 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.538 |
| Model: | OLS | Adj. R-squared: | 0.412 |
| Method: | Least Squares | F-statistic: | 4.272 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0314 |
| Time: | 23:01:30 | Log-Likelihood: | -69.506 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 124.2043 | 157.411 | 0.789 | 0.447 | -222.254 470.663 |
| C(dose)[T.1] | 214.3679 | 222.572 | 0.963 | 0.356 | -275.510 704.246 |
| expression | -6.5415 | 18.092 | -0.362 | 0.725 | -46.362 33.279 |
| expression:C(dose)[T.1] | -19.0014 | 25.571 | -0.743 | 0.473 | -75.282 37.280 |
| Omnibus: | 1.864 | Durbin-Watson: | 0.880 |
| Prob(Omnibus): | 0.394 | Jarque-Bera (JB): | 0.979 |
| Skew: | -0.196 | Prob(JB): | 0.613 |
| Kurtosis: | 1.812 | Cond. No. | 348. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.515 |
| Model: | OLS | Adj. R-squared: | 0.434 |
| Method: | Least Squares | F-statistic: | 6.370 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0130 |
| Time: | 23:01:30 | Log-Likelihood: | -69.874 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 12 | BIC: | 147.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 206.7621 | 109.410 | 1.890 | 0.083 | -31.623 445.147 |
| C(dose)[T.1] | 49.3558 | 14.765 | 3.343 | 0.006 | 17.186 81.526 |
| expression | -16.0536 | 12.545 | -1.280 | 0.225 | -43.386 11.279 |
| Omnibus: | 3.400 | Durbin-Watson: | 0.826 |
| Prob(Omnibus): | 0.183 | Jarque-Bera (JB): | 1.299 |
| Skew: | -0.248 | Prob(JB): | 0.522 |
| Kurtosis: | 1.647 | Cond. No. | 131. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:01:30 | 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: | 0.063 |
| Model: | OLS | Adj. R-squared: | -0.009 |
| Method: | Least Squares | F-statistic: | 0.8787 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.366 |
| Time: | 23:01:30 | Log-Likelihood: | -74.810 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 230.0143 | 145.784 | 1.578 | 0.139 | -84.932 544.961 |
| expression | -15.7000 | 16.748 | -0.937 | 0.366 | -51.882 20.482 |
| Omnibus: | 0.755 | Durbin-Watson: | 1.675 |
| Prob(Omnibus): | 0.686 | Jarque-Bera (JB): | 0.664 |
| Skew: | 0.173 | Prob(JB): | 0.717 |
| Kurtosis: | 2.029 | Cond. No. | 131. |