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 |
| 0.001 | 0.972 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000141 |
| Time: | 04:34:51 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 48.1141 | 45.233 | 1.064 | 0.301 | -46.560 142.788 |
| C(dose)[T.1] | 64.9710 | 69.789 | 0.931 | 0.364 | -81.098 211.040 |
| expression | 1.2665 | 9.311 | 0.136 | 0.893 | -18.222 20.755 |
| expression:C(dose)[T.1] | -2.4043 | 14.286 | -0.168 | 0.868 | -32.304 27.496 |
| Omnibus: | 0.435 | Durbin-Watson: | 1.913 |
| Prob(Omnibus): | 0.805 | Jarque-Bera (JB): | 0.545 |
| Skew: | 0.036 | Prob(JB): | 0.761 |
| Kurtosis: | 2.249 | Cond. No. | 99.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 04:34:51 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 53.0291 | 33.694 | 1.574 | 0.131 | -17.255 123.313 |
| C(dose)[T.1] | 53.3232 | 8.778 | 6.074 | 0.000 | 35.012 71.634 |
| expression | 0.2451 | 6.888 | 0.036 | 0.972 | -14.123 14.613 |
| Omnibus: | 0.308 | Durbin-Watson: | 1.887 |
| Prob(Omnibus): | 0.857 | Jarque-Bera (JB): | 0.477 |
| Skew: | 0.057 | Prob(JB): | 0.788 |
| Kurtosis: | 2.304 | Cond. No. | 39.3 |
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: | 04:34:51 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.03462 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.854 |
| Time: | 04:34:51 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 69.5198 | 55.281 | 1.258 | 0.222 | -45.444 184.484 |
| expression | 2.1074 | 11.327 | 0.186 | 0.854 | -21.448 25.662 |
| Omnibus: | 2.921 | Durbin-Watson: | 2.476 |
| Prob(Omnibus): | 0.232 | Jarque-Bera (JB): | 1.489 |
| Skew: | 0.289 | Prob(JB): | 0.475 |
| Kurtosis: | 1.895 | Cond. No. | 39.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.575 | 0.463 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.589 |
| Model: | OLS | Adj. R-squared: | 0.477 |
| Method: | Least Squares | F-statistic: | 5.263 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0170 |
| Time: | 04:34:51 | Log-Likelihood: | -68.624 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 11 | BIC: | 148.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 185.0570 | 86.697 | 2.135 | 0.056 | -5.762 375.876 |
| C(dose)[T.1] | -368.2335 | 234.366 | -1.571 | 0.144 | -884.069 147.602 |
| expression | -19.6581 | 14.385 | -1.367 | 0.199 | -51.319 12.003 |
| expression:C(dose)[T.1] | 75.0087 | 42.659 | 1.758 | 0.106 | -18.883 168.900 |
| Omnibus: | 0.893 | Durbin-Watson: | 1.323 |
| Prob(Omnibus): | 0.640 | Jarque-Bera (JB): | 0.433 |
| Skew: | -0.406 | Prob(JB): | 0.805 |
| Kurtosis: | 2.817 | Cond. No. | 224. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 5.406 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0212 |
| Time: | 04:34:51 | Log-Likelihood: | -70.482 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 134.0200 | 88.528 | 1.514 | 0.156 | -58.867 326.907 |
| C(dose)[T.1] | 42.8831 | 17.485 | 2.453 | 0.030 | 4.787 80.979 |
| expression | -11.1288 | 14.675 | -0.758 | 0.463 | -43.104 20.846 |
| Omnibus: | 2.953 | Durbin-Watson: | 0.777 |
| Prob(Omnibus): | 0.228 | Jarque-Bera (JB): | 1.901 |
| Skew: | -0.864 | Prob(JB): | 0.386 |
| Kurtosis: | 2.762 | Cond. No. | 68.5 |
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: | 04:34:51 | 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: | 0.210 |
| Model: | OLS | Adj. R-squared: | 0.150 |
| Method: | Least Squares | F-statistic: | 3.462 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0856 |
| Time: | 04:34:51 | Log-Likelihood: | -73.529 |
| No. Observations: | 15 | AIC: | 151.1 |
| Df Residuals: | 13 | BIC: | 152.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 254.2533 | 86.778 | 2.930 | 0.012 | 66.781 441.726 |
| expression | -28.2666 | 15.192 | -1.861 | 0.086 | -61.086 4.553 |
| Omnibus: | 3.921 | Durbin-Watson: | 1.572 |
| Prob(Omnibus): | 0.141 | Jarque-Bera (JB): | 1.341 |
| Skew: | 0.209 | Prob(JB): | 0.512 |
| Kurtosis: | 1.596 | Cond. No. | 56.6 |