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 |
| 0.865 | 0.363 | 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: | 0.617 |
| Method: | Least Squares | F-statistic: | 12.82 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.23e-05 |
| Time: | 04:52:14 | Log-Likelihood: | -100.38 |
| 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 | 228.4543 | 325.404 | 0.702 | 0.491 | -452.623 909.532 |
| C(dose)[T.1] | 462.0490 | 703.317 | 0.657 | 0.519 | -1010.011 1934.109 |
| expression | -14.1547 | 26.429 | -0.536 | 0.598 | -69.472 41.162 |
| expression:C(dose)[T.1] | -31.8418 | 55.844 | -0.570 | 0.575 | -148.724 85.041 |
| Omnibus: | 2.447 | Durbin-Watson: | 1.737 |
| Prob(Omnibus): | 0.294 | Jarque-Bera (JB): | 1.182 |
| Skew: | 0.077 | Prob(JB): | 0.554 |
| Kurtosis: | 1.900 | Cond. No. | 2.37e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 19.73 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.86e-05 |
| Time: | 04:52:14 | Log-Likelihood: | -100.58 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 316.2512 | 281.790 | 1.122 | 0.275 | -271.552 904.054 |
| C(dose)[T.1] | 61.0818 | 11.960 | 5.107 | 0.000 | 36.133 86.031 |
| expression | -21.2867 | 22.886 | -0.930 | 0.363 | -69.026 26.452 |
| Omnibus: | 1.997 | Durbin-Watson: | 1.754 |
| Prob(Omnibus): | 0.368 | Jarque-Bera (JB): | 1.063 |
| Skew: | 0.003 | Prob(JB): | 0.588 |
| Kurtosis: | 1.947 | Cond. No. | 827. |
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: | 04:52:14 | 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: | 0.225 |
| Model: | OLS | Adj. R-squared: | 0.188 |
| Method: | Least Squares | F-statistic: | 6.094 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0222 |
| Time: | 04:52:14 | Log-Likelihood: | -110.17 |
| No. Observations: | 23 | AIC: | 224.3 |
| Df Residuals: | 21 | BIC: | 226.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -670.3278 | 303.897 | -2.206 | 0.039 | -1302.316 -38.340 |
| expression | 60.0797 | 24.337 | 2.469 | 0.022 | 9.468 110.692 |
| Omnibus: | 3.025 | Durbin-Watson: | 2.300 |
| Prob(Omnibus): | 0.220 | Jarque-Bera (JB): | 1.617 |
| Skew: | 0.351 | Prob(JB): | 0.445 |
| Kurtosis: | 1.906 | Cond. No. | 601. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.148 | 0.707 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.544 |
| Model: | OLS | Adj. R-squared: | 0.420 |
| Method: | Least Squares | F-statistic: | 4.380 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0293 |
| Time: | 04:52:14 | Log-Likelihood: | -69.405 |
| No. Observations: | 15 | AIC: | 146.8 |
| Df Residuals: | 11 | BIC: | 149.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 422.7609 | 359.543 | 1.176 | 0.264 | -368.588 1214.110 |
| C(dose)[T.1] | -585.0358 | 433.494 | -1.350 | 0.204 | -1539.150 369.078 |
| expression | -30.4213 | 30.768 | -0.989 | 0.344 | -98.140 37.298 |
| expression:C(dose)[T.1] | 54.3610 | 37.121 | 1.464 | 0.171 | -27.342 136.064 |
| Omnibus: | 0.105 | Durbin-Watson: | 1.415 |
| Prob(Omnibus): | 0.949 | Jarque-Bera (JB): | 0.183 |
| Skew: | -0.149 | Prob(JB): | 0.913 |
| Kurtosis: | 2.548 | Cond. No. | 988. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 5.019 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0261 |
| Time: | 04:52:14 | Log-Likelihood: | -70.741 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -13.4453 | 210.743 | -0.064 | 0.950 | -472.615 445.725 |
| C(dose)[T.1] | 49.4058 | 15.653 | 3.156 | 0.008 | 15.301 83.511 |
| expression | 6.9239 | 18.016 | 0.384 | 0.707 | -32.330 46.177 |
| Omnibus: | 1.826 | Durbin-Watson: | 0.888 |
| Prob(Omnibus): | 0.401 | Jarque-Bera (JB): | 1.331 |
| Skew: | -0.687 | Prob(JB): | 0.514 |
| Kurtosis: | 2.506 | Cond. No. | 318. |
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: | 04:52:14 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.073 |
| Method: | Least Squares | F-statistic: | 0.04464 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.836 |
| Time: | 04:52:14 | Log-Likelihood: | -75.274 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 35.9912 | 273.160 | 0.132 | 0.897 | -554.134 626.117 |
| expression | 4.9446 | 23.402 | 0.211 | 0.836 | -45.613 55.502 |
| Omnibus: | 0.270 | Durbin-Watson: | 1.622 |
| Prob(Omnibus): | 0.874 | Jarque-Bera (JB): | 0.436 |
| Skew: | 0.027 | Prob(JB): | 0.804 |
| Kurtosis: | 2.167 | Cond. No. | 317. |