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 |
| 0.589 | 0.452 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.700 |
| Model: | OLS | Adj. R-squared: | 0.653 |
| Method: | Least Squares | F-statistic: | 14.78 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.32e-05 |
| Time: | 04:40:08 | Log-Likelihood: | -99.257 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 19 | BIC: | 211.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.2530 | 30.387 | 3.168 | 0.005 | 32.652 159.854 |
| C(dose)[T.1] | -56.5525 | 68.242 | -0.829 | 0.418 | -199.384 86.279 |
| expression | -10.1006 | 7.168 | -1.409 | 0.175 | -25.104 4.902 |
| expression:C(dose)[T.1] | 27.0288 | 16.780 | 1.611 | 0.124 | -8.092 62.150 |
| Omnibus: | 3.963 | Durbin-Watson: | 2.234 |
| Prob(Omnibus): | 0.138 | Jarque-Bera (JB): | 1.563 |
| Skew: | 0.197 | Prob(JB): | 0.458 |
| Kurtosis: | 1.785 | Cond. No. | 82.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.33 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.12e-05 |
| Time: | 04:40:08 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.7224 | 28.663 | 2.642 | 0.016 | 15.931 135.513 |
| C(dose)[T.1] | 52.5370 | 8.706 | 6.034 | 0.000 | 34.376 70.698 |
| expression | -5.1684 | 6.735 | -0.767 | 0.452 | -19.216 8.880 |
| Omnibus: | 0.031 | Durbin-Watson: | 2.051 |
| Prob(Omnibus): | 0.985 | Jarque-Bera (JB): | 0.243 |
| Skew: | -0.018 | Prob(JB): | 0.886 |
| Kurtosis: | 2.498 | Cond. No. | 29.3 |
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: | 04:40:08 | 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: | 0.038 |
| Model: | OLS | Adj. R-squared: | -0.007 |
| Method: | Least Squares | F-statistic: | 0.8385 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.370 |
| Time: | 04:40:08 | Log-Likelihood: | -112.65 |
| No. Observations: | 23 | AIC: | 229.3 |
| Df Residuals: | 21 | BIC: | 231.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.7459 | 45.361 | 2.662 | 0.015 | 26.413 215.079 |
| expression | -10.0349 | 10.959 | -0.916 | 0.370 | -32.825 12.755 |
| Omnibus: | 3.305 | Durbin-Watson: | 2.634 |
| Prob(Omnibus): | 0.192 | Jarque-Bera (JB): | 1.795 |
| Skew: | 0.408 | Prob(JB): | 0.408 |
| Kurtosis: | 1.901 | Cond. No. | 28.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.213 | 0.653 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.458 |
| Model: | OLS | Adj. R-squared: | 0.311 |
| Method: | Least Squares | F-statistic: | 3.103 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0711 |
| Time: | 04:40:08 | Log-Likelihood: | -70.701 |
| No. Observations: | 15 | AIC: | 149.4 |
| Df Residuals: | 11 | BIC: | 152.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 46.1934 | 60.932 | 0.758 | 0.464 | -87.918 180.305 |
| C(dose)[T.1] | 51.0428 | 96.484 | 0.529 | 0.607 | -161.317 263.402 |
| expression | 4.4945 | 12.648 | 0.355 | 0.729 | -23.344 32.333 |
| expression:C(dose)[T.1] | -0.0884 | 21.038 | -0.004 | 0.997 | -46.392 46.215 |
| Omnibus: | 2.916 | Durbin-Watson: | 0.719 |
| Prob(Omnibus): | 0.233 | Jarque-Bera (JB): | 1.772 |
| Skew: | -0.839 | Prob(JB): | 0.412 |
| Kurtosis: | 2.856 | Cond. No. | 71.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.458 |
| Model: | OLS | Adj. R-squared: | 0.368 |
| Method: | Least Squares | F-statistic: | 5.078 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0252 |
| Time: | 04:40:08 | Log-Likelihood: | -70.701 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 46.3444 | 47.118 | 0.984 | 0.345 | -56.317 149.006 |
| C(dose)[T.1] | 50.6434 | 15.914 | 3.182 | 0.008 | 15.969 85.318 |
| expression | 4.4625 | 9.677 | 0.461 | 0.653 | -16.621 25.546 |
| Omnibus: | 2.937 | Durbin-Watson: | 0.719 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 1.782 |
| Skew: | -0.841 | Prob(JB): | 0.410 |
| Kurtosis: | 2.861 | Cond. No. | 29.6 |
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: | 04:40:08 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.01689 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.899 |
| Time: | 04:40:08 | Log-Likelihood: | -75.290 |
| 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 | 100.9889 | 57.244 | 1.764 | 0.101 | -22.679 224.657 |
| expression | -1.6087 | 12.377 | -0.130 | 0.899 | -28.347 25.130 |
| Omnibus: | 0.313 | Durbin-Watson: | 1.615 |
| Prob(Omnibus): | 0.855 | Jarque-Bera (JB): | 0.457 |
| Skew: | 0.031 | Prob(JB): | 0.796 |
| Kurtosis: | 2.147 | Cond. No. | 27.3 |