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 |
| 0.241 | 0.629 | 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: | 12.19 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000112 |
| Time: | 04:52:00 | 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 | -5.5983 | 85.865 | -0.065 | 0.949 | -185.316 174.119 |
| C(dose)[T.1] | 123.5223 | 137.299 | 0.900 | 0.380 | -163.848 410.893 |
| expression | 8.7322 | 12.505 | 0.698 | 0.493 | -17.441 34.905 |
| expression:C(dose)[T.1] | -10.2329 | 19.888 | -0.515 | 0.613 | -51.858 31.392 |
| Omnibus: | 0.406 | Durbin-Watson: | 1.714 |
| Prob(Omnibus): | 0.816 | Jarque-Bera (JB): | 0.530 |
| Skew: | -0.027 | Prob(JB): | 0.767 |
| Kurtosis: | 2.258 | Cond. No. | 270. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.84 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.51e-05 |
| Time: | 04:52:00 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.1100 | 65.639 | 0.337 | 0.740 | -114.810 159.030 |
| C(dose)[T.1] | 53.0255 | 8.741 | 6.067 | 0.000 | 34.793 71.258 |
| expression | 4.6866 | 9.543 | 0.491 | 0.629 | -15.220 24.593 |
| Omnibus: | 0.173 | Durbin-Watson: | 1.785 |
| Prob(Omnibus): | 0.917 | Jarque-Bera (JB): | 0.380 |
| Skew: | 0.097 | Prob(JB): | 0.827 |
| Kurtosis: | 2.401 | Cond. No. | 106. |
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: | 04:52:00 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.032 |
| Method: | Least Squares | F-statistic: | 0.3225 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.576 |
| Time: | 04:52:00 | Log-Likelihood: | -112.93 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 18.5467 | 107.950 | 0.172 | 0.865 | -205.947 243.041 |
| expression | 8.8900 | 15.654 | 0.568 | 0.576 | -23.664 41.444 |
| Omnibus: | 1.953 | Durbin-Watson: | 2.388 |
| Prob(Omnibus): | 0.377 | Jarque-Bera (JB): | 1.338 |
| Skew: | 0.350 | Prob(JB): | 0.512 |
| Kurtosis: | 2.048 | Cond. No. | 106. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.884 | 0.115 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.567 |
| Model: | OLS | Adj. R-squared: | 0.449 |
| Method: | Least Squares | F-statistic: | 4.806 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0224 |
| Time: | 04:52:00 | Log-Likelihood: | -69.018 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 11 | BIC: | 148.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 214.3929 | 139.904 | 1.532 | 0.154 | -93.534 522.319 |
| C(dose)[T.1] | 162.1941 | 234.981 | 0.690 | 0.504 | -354.995 679.383 |
| expression | -20.1848 | 19.159 | -1.054 | 0.315 | -62.354 21.985 |
| expression:C(dose)[T.1] | -18.4975 | 33.972 | -0.544 | 0.597 | -93.268 56.274 |
| Omnibus: | 0.216 | Durbin-Watson: | 1.085 |
| Prob(Omnibus): | 0.897 | Jarque-Bera (JB): | 0.121 |
| Skew: | 0.162 | Prob(JB): | 0.941 |
| Kurtosis: | 2.702 | Cond. No. | 283. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.556 |
| Model: | OLS | Adj. R-squared: | 0.482 |
| Method: | Least Squares | F-statistic: | 7.501 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00771 |
| Time: | 04:52:00 | Log-Likelihood: | -69.218 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 12 | BIC: | 146.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 257.2313 | 112.244 | 2.292 | 0.041 | 12.673 501.790 |
| C(dose)[T.1] | 34.5851 | 16.546 | 2.090 | 0.059 | -1.465 70.636 |
| expression | -26.0684 | 15.351 | -1.698 | 0.115 | -59.515 7.378 |
| Omnibus: | 0.199 | Durbin-Watson: | 0.922 |
| Prob(Omnibus): | 0.906 | Jarque-Bera (JB): | 0.385 |
| Skew: | -0.161 | Prob(JB): | 0.825 |
| Kurtosis: | 2.284 | Cond. No. | 114. |
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: | 04:52:00 | 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: | 0.394 |
| Model: | OLS | Adj. R-squared: | 0.347 |
| Method: | Least Squares | F-statistic: | 8.444 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0123 |
| Time: | 04:52:00 | Log-Likelihood: | -71.546 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 13 | BIC: | 148.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 392.1768 | 103.033 | 3.806 | 0.002 | 169.588 614.766 |
| expression | -42.7541 | 14.713 | -2.906 | 0.012 | -74.540 -10.968 |
| Omnibus: | 1.182 | Durbin-Watson: | 1.348 |
| Prob(Omnibus): | 0.554 | Jarque-Bera (JB): | 0.345 |
| Skew: | 0.369 | Prob(JB): | 0.841 |
| Kurtosis: | 3.093 | Cond. No. | 93.0 |