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 |
| 3.632 | 0.071 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.745 |
| Model: | OLS | Adj. R-squared: | 0.705 |
| Method: | Least Squares | F-statistic: | 18.54 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.18e-06 |
| Time: | 03:57:01 | Log-Likelihood: | -97.371 |
| No. Observations: | 23 | AIC: | 202.7 |
| Df Residuals: | 19 | BIC: | 207.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -91.9248 | 168.519 | -0.545 | 0.592 | -444.639 260.790 |
| C(dose)[T.1] | -567.8904 | 346.235 | -1.640 | 0.117 | -1292.569 156.788 |
| expression | 18.7394 | 21.599 | 0.868 | 0.396 | -26.469 63.947 |
| expression:C(dose)[T.1] | 77.9104 | 43.786 | 1.779 | 0.091 | -13.736 169.556 |
| Omnibus: | 0.922 | Durbin-Watson: | 1.558 |
| Prob(Omnibus): | 0.631 | Jarque-Bera (JB): | 0.915 |
| Skew: | 0.373 | Prob(JB): | 0.633 |
| Kurtosis: | 2.369 | Cond. No. | 850. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.703 |
| Model: | OLS | Adj. R-squared: | 0.673 |
| Method: | Least Squares | F-statistic: | 23.67 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.34e-06 |
| Time: | 03:57:01 | Log-Likelihood: | -99.144 |
| No. Observations: | 23 | AIC: | 204.3 |
| Df Residuals: | 20 | BIC: | 207.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -239.7643 | 154.347 | -1.553 | 0.136 | -561.727 82.199 |
| C(dose)[T.1] | 48.0063 | 8.539 | 5.622 | 0.000 | 30.195 65.818 |
| expression | 37.6975 | 19.780 | 1.906 | 0.071 | -3.562 78.957 |
| Omnibus: | 1.320 | Durbin-Watson: | 1.430 |
| Prob(Omnibus): | 0.517 | Jarque-Bera (JB): | 0.920 |
| Skew: | 0.138 | Prob(JB): | 0.631 |
| Kurtosis: | 2.060 | Cond. No. | 307. |
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: | 03:57:01 | 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: | 0.234 |
| Model: | OLS | Adj. R-squared: | 0.197 |
| Method: | Least Squares | F-statistic: | 6.401 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0195 |
| Time: | 03:57:01 | Log-Likelihood: | -110.04 |
| No. Observations: | 23 | AIC: | 224.1 |
| Df Residuals: | 21 | BIC: | 226.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -503.3300 | 230.531 | -2.183 | 0.041 | -982.745 -23.915 |
| expression | 74.1242 | 29.297 | 2.530 | 0.019 | 13.198 135.050 |
| Omnibus: | 1.707 | Durbin-Watson: | 2.071 |
| Prob(Omnibus): | 0.426 | Jarque-Bera (JB): | 1.008 |
| Skew: | -0.090 | Prob(JB): | 0.604 |
| Kurtosis: | 1.990 | Cond. No. | 292. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.472 | 0.248 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.515 |
| Model: | OLS | Adj. R-squared: | 0.383 |
| Method: | Least Squares | F-statistic: | 3.893 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0405 |
| Time: | 03:57:01 | Log-Likelihood: | -69.874 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 11 | BIC: | 150.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 340.6716 | 322.114 | 1.058 | 0.313 | -368.296 1049.640 |
| C(dose)[T.1] | 306.0639 | 681.165 | 0.449 | 0.662 | -1193.170 1805.298 |
| expression | -35.0602 | 41.306 | -0.849 | 0.414 | -125.973 55.853 |
| expression:C(dose)[T.1] | -31.6394 | 86.065 | -0.368 | 0.720 | -221.068 157.789 |
| Omnibus: | 3.628 | Durbin-Watson: | 0.776 |
| Prob(Omnibus): | 0.163 | Jarque-Bera (JB): | 1.645 |
| Skew: | -0.477 | Prob(JB): | 0.439 |
| Kurtosis: | 1.688 | Cond. No. | 851. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.427 |
| Method: | Least Squares | F-statistic: | 6.220 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0140 |
| Time: | 03:57:01 | Log-Likelihood: | -69.965 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 397.4685 | 272.268 | 1.460 | 0.170 | -195.753 990.690 |
| C(dose)[T.1] | 55.7263 | 15.800 | 3.527 | 0.004 | 21.301 90.152 |
| expression | -42.3479 | 34.907 | -1.213 | 0.248 | -118.405 33.709 |
| Omnibus: | 2.767 | Durbin-Watson: | 0.703 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.568 |
| Skew: | -0.527 | Prob(JB): | 0.457 |
| Kurtosis: | 1.818 | Cond. No. | 295. |
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: | 03:57:01 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.077 |
| Method: | Least Squares | F-statistic: | 8.145e-05 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.993 |
| Time: | 03:57:02 | Log-Likelihood: | -75.300 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.8651 | 354.548 | 0.273 | 0.789 | -669.090 862.820 |
| expression | -0.4061 | 44.999 | -0.009 | 0.993 | -97.621 96.808 |
| Omnibus: | 0.600 | Durbin-Watson: | 1.624 |
| Prob(Omnibus): | 0.741 | Jarque-Bera (JB): | 0.580 |
| Skew: | 0.049 | Prob(JB): | 0.748 |
| Kurtosis: | 2.042 | Cond. No. | 279. |