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 |
| 0.729 | 0.403 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.42 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.99e-05 |
| Time: | 04:42:14 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.9783 | 53.807 | 1.802 | 0.087 | -15.641 209.598 |
| C(dose)[T.1] | 33.9225 | 90.104 | 0.376 | 0.711 | -154.668 222.513 |
| expression | -7.6898 | 9.612 | -0.800 | 0.434 | -27.808 12.428 |
| expression:C(dose)[T.1] | 3.5838 | 15.893 | 0.225 | 0.824 | -29.681 36.848 |
| Omnibus: | 0.144 | Durbin-Watson: | 1.835 |
| Prob(Omnibus): | 0.931 | Jarque-Bera (JB): | 0.362 |
| Skew: | 0.051 | Prob(JB): | 0.835 |
| Kurtosis: | 2.394 | Cond. No. | 146. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.661 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.98e-05 |
| Time: | 04:42:14 | Log-Likelihood: | -100.65 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.6878 | 41.977 | 2.137 | 0.045 | 2.125 177.250 |
| C(dose)[T.1] | 54.1415 | 8.666 | 6.248 | 0.000 | 36.065 72.218 |
| expression | -6.3790 | 7.471 | -0.854 | 0.403 | -21.963 9.205 |
| Omnibus: | 0.122 | Durbin-Watson: | 1.835 |
| Prob(Omnibus): | 0.941 | Jarque-Bera (JB): | 0.343 |
| Skew: | 0.040 | Prob(JB): | 0.842 |
| Kurtosis: | 2.407 | Cond. No. | 57.0 |
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: | 04:42:14 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.01098 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.918 |
| Time: | 04:42:14 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 87.0521 | 70.378 | 1.237 | 0.230 | -59.307 233.412 |
| expression | -1.3046 | 12.452 | -0.105 | 0.918 | -27.200 24.591 |
| Omnibus: | 3.435 | Durbin-Watson: | 2.494 |
| Prob(Omnibus): | 0.180 | Jarque-Bera (JB): | 1.606 |
| Skew: | 0.296 | Prob(JB): | 0.448 |
| Kurtosis: | 1.849 | Cond. No. | 56.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.309 | 0.589 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.317 |
| Method: | Least Squares | F-statistic: | 3.167 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0678 |
| Time: | 04:42:14 | Log-Likelihood: | -70.631 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 13.5622 | 115.129 | 0.118 | 0.908 | -239.836 266.960 |
| C(dose)[T.1] | 68.6074 | 168.589 | 0.407 | 0.692 | -302.455 439.670 |
| expression | 9.3823 | 19.947 | 0.470 | 0.647 | -34.520 53.284 |
| expression:C(dose)[T.1] | -3.6929 | 28.427 | -0.130 | 0.899 | -66.259 58.874 |
| Omnibus: | 2.624 | Durbin-Watson: | 0.726 |
| Prob(Omnibus): | 0.269 | Jarque-Bera (JB): | 1.820 |
| Skew: | -0.829 | Prob(JB): | 0.403 |
| Kurtosis: | 2.600 | Cond. No. | 168. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 5.165 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0241 |
| Time: | 04:42:14 | Log-Likelihood: | -70.643 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 24.0014 | 78.999 | 0.304 | 0.766 | -148.122 196.125 |
| C(dose)[T.1] | 46.8151 | 16.122 | 2.904 | 0.013 | 11.689 81.941 |
| expression | 7.5640 | 13.617 | 0.555 | 0.589 | -22.105 37.233 |
| Omnibus: | 2.658 | Durbin-Watson: | 0.724 |
| Prob(Omnibus): | 0.265 | Jarque-Bera (JB): | 1.832 |
| Skew: | -0.834 | Prob(JB): | 0.400 |
| Kurtosis: | 2.614 | Cond. No. | 62.5 |
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: | 04:42:14 | 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: | 0.085 |
| Model: | OLS | Adj. R-squared: | 0.015 |
| Method: | Least Squares | F-statistic: | 1.207 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.292 |
| Time: | 04:42:14 | Log-Likelihood: | -74.634 |
| No. Observations: | 15 | AIC: | 153.3 |
| Df Residuals: | 13 | BIC: | 154.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -13.1642 | 97.731 | -0.135 | 0.895 | -224.300 197.971 |
| expression | 18.0788 | 16.457 | 1.099 | 0.292 | -17.474 53.632 |
| Omnibus: | 0.831 | Durbin-Watson: | 1.452 |
| Prob(Omnibus): | 0.660 | Jarque-Bera (JB): | 0.676 |
| Skew: | 0.131 | Prob(JB): | 0.713 |
| Kurtosis: | 1.993 | Cond. No. | 61.4 |