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 |
| 0.363 | 0.554 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.602 |
| Method: | Least Squares | F-statistic: | 12.07 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000119 |
| Time: | 04:41:00 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 190.9842 | 390.166 | 0.489 | 0.630 | -625.643 1007.612 |
| C(dose)[T.1] | 165.9264 | 629.088 | 0.264 | 0.795 | -1150.771 1482.624 |
| expression | -12.8904 | 36.767 | -0.351 | 0.730 | -89.844 64.063 |
| expression:C(dose)[T.1] | -10.8167 | 59.602 | -0.181 | 0.858 | -135.565 113.932 |
| Omnibus: | 0.027 | Durbin-Watson: | 1.861 |
| Prob(Omnibus): | 0.987 | Jarque-Bera (JB): | 0.225 |
| Skew: | 0.045 | Prob(JB): | 0.894 |
| Kurtosis: | 2.524 | Cond. No. | 1.86e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.01 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.37e-05 |
| Time: | 04:41:00 | Log-Likelihood: | -100.86 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.6577 | 299.594 | 0.783 | 0.443 | -390.285 859.600 |
| C(dose)[T.1] | 51.7713 | 9.072 | 5.707 | 0.000 | 32.848 70.694 |
| expression | -17.0064 | 28.229 | -0.602 | 0.554 | -75.892 41.879 |
| Omnibus: | 0.047 | Durbin-Watson: | 1.848 |
| Prob(Omnibus): | 0.977 | Jarque-Bera (JB): | 0.235 |
| Skew: | 0.077 | Prob(JB): | 0.889 |
| Kurtosis: | 2.530 | Cond. No. | 737. |
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: | 04:41:00 | 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: | 0.094 |
| Model: | OLS | Adj. R-squared: | 0.051 |
| Method: | Least Squares | F-statistic: | 2.179 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.155 |
| Time: | 04:41:00 | Log-Likelihood: | -111.97 |
| No. Observations: | 23 | AIC: | 227.9 |
| Df Residuals: | 21 | BIC: | 230.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 747.1657 | 452.214 | 1.652 | 0.113 | -193.264 1687.596 |
| expression | -63.1656 | 42.791 | -1.476 | 0.155 | -152.155 25.824 |
| Omnibus: | 4.608 | Durbin-Watson: | 2.119 |
| Prob(Omnibus): | 0.100 | Jarque-Bera (JB): | 1.720 |
| Skew: | 0.238 | Prob(JB): | 0.423 |
| Kurtosis: | 1.748 | Cond. No. | 702. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.135 | 0.720 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.501 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 3.677 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0470 |
| Time: | 04:41:00 | Log-Likelihood: | -70.091 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -269.3349 | 337.893 | -0.797 | 0.442 | -1013.032 474.362 |
| C(dose)[T.1] | 506.8206 | 460.326 | 1.101 | 0.294 | -506.351 1519.992 |
| expression | 36.6635 | 36.765 | 0.997 | 0.340 | -44.257 117.584 |
| expression:C(dose)[T.1] | -49.1456 | 48.917 | -1.005 | 0.337 | -156.811 58.520 |
| Omnibus: | 1.244 | Durbin-Watson: | 0.964 |
| Prob(Omnibus): | 0.537 | Jarque-Bera (JB): | 0.962 |
| Skew: | -0.561 | Prob(JB): | 0.618 |
| Kurtosis: | 2.471 | Cond. No. | 772. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.007 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0262 |
| Time: | 04:41:00 | Log-Likelihood: | -70.749 |
| 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 | -14.3361 | 223.137 | -0.064 | 0.950 | -500.509 471.837 |
| C(dose)[T.1] | 44.7678 | 19.765 | 2.265 | 0.043 | 1.703 87.833 |
| expression | 8.9017 | 24.261 | 0.367 | 0.720 | -43.959 61.762 |
| Omnibus: | 3.121 | Durbin-Watson: | 0.738 |
| Prob(Omnibus): | 0.210 | Jarque-Bera (JB): | 2.044 |
| Skew: | -0.895 | Prob(JB): | 0.360 |
| Kurtosis: | 2.745 | Cond. No. | 274. |
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: | 04:41:01 | 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: | 0.222 |
| Model: | OLS | Adj. R-squared: | 0.162 |
| Method: | Least Squares | F-statistic: | 3.706 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0764 |
| Time: | 04:41:01 | Log-Likelihood: | -73.419 |
| No. Observations: | 15 | AIC: | 150.8 |
| Df Residuals: | 13 | BIC: | 152.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -307.5830 | 208.613 | -1.474 | 0.164 | -758.264 143.098 |
| expression | 42.4576 | 22.054 | 1.925 | 0.076 | -5.186 90.102 |
| Omnibus: | 0.151 | Durbin-Watson: | 1.122 |
| Prob(Omnibus): | 0.927 | Jarque-Bera (JB): | 0.333 |
| Skew: | 0.167 | Prob(JB): | 0.847 |
| Kurtosis: | 2.350 | Cond. No. | 223. |