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 |
| 1.142 | 0.298 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.95 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 7.72e-05 |
| Time: | 22:55:51 | Log-Likelihood: | -100.30 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -30.3864 | 81.202 | -0.374 | 0.712 | -200.345 139.572 |
| C(dose)[T.1] | 101.5243 | 113.758 | 0.892 | 0.383 | -136.573 339.622 |
| expression | 15.3664 | 14.710 | 1.045 | 0.309 | -15.421 46.154 |
| expression:C(dose)[T.1] | -9.0976 | 20.084 | -0.453 | 0.656 | -51.134 32.939 |
| Omnibus: | 0.570 | Durbin-Watson: | 2.145 |
| Prob(Omnibus): | 0.752 | Jarque-Bera (JB): | 0.609 |
| Skew: | -0.029 | Prob(JB): | 0.737 |
| Kurtosis: | 2.205 | Cond. No. | 201. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.12 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.63e-05 |
| Time: | 22:55:51 | Log-Likelihood: | -100.42 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.5208 | 54.351 | -0.065 | 0.949 | -116.895 109.853 |
| C(dose)[T.1] | 50.1642 | 9.032 | 5.554 | 0.000 | 31.324 69.004 |
| expression | 10.4863 | 9.814 | 1.068 | 0.298 | -9.986 30.959 |
| Omnibus: | 0.516 | Durbin-Watson: | 2.096 |
| Prob(Omnibus): | 0.773 | Jarque-Bera (JB): | 0.584 |
| Skew: | -0.029 | Prob(JB): | 0.747 |
| Kurtosis: | 2.221 | Cond. No. | 74.9 |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:55:51 | 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: | 0.156 |
| Model: | OLS | Adj. R-squared: | 0.116 |
| Method: | Least Squares | F-statistic: | 3.880 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0622 |
| Time: | 22:55:51 | Log-Likelihood: | -111.16 |
| No. Observations: | 23 | AIC: | 226.3 |
| Df Residuals: | 21 | BIC: | 228.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -80.7872 | 81.756 | -0.988 | 0.334 | -250.808 89.234 |
| expression | 28.4085 | 14.423 | 1.970 | 0.062 | -1.585 58.402 |
| Omnibus: | 2.613 | Durbin-Watson: | 2.719 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.612 |
| Skew: | 0.400 | Prob(JB): | 0.447 |
| Kurtosis: | 1.978 | Cond. No. | 72.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.136 | 0.102 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.622 |
| Model: | OLS | Adj. R-squared: | 0.518 |
| Method: | Least Squares | F-statistic: | 6.022 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0111 |
| Time: | 22:55:52 | Log-Likelihood: | -68.012 |
| No. Observations: | 15 | AIC: | 144.0 |
| Df Residuals: | 11 | BIC: | 146.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.3648 | 117.376 | 0.139 | 0.892 | -241.978 274.708 |
| C(dose)[T.1] | -165.5515 | 168.784 | -0.981 | 0.348 | -537.043 205.940 |
| expression | 7.9712 | 18.257 | 0.437 | 0.671 | -32.212 48.154 |
| expression:C(dose)[T.1] | 34.8289 | 26.692 | 1.305 | 0.219 | -23.920 93.578 |
| Omnibus: | 0.121 | Durbin-Watson: | 1.223 |
| Prob(Omnibus): | 0.941 | Jarque-Bera (JB): | 0.245 |
| Skew: | -0.171 | Prob(JB): | 0.885 |
| Kurtosis: | 2.476 | Cond. No. | 211. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.563 |
| Model: | OLS | Adj. R-squared: | 0.490 |
| Method: | Least Squares | F-statistic: | 7.729 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00697 |
| Time: | 22:55:52 | Log-Likelihood: | -69.092 |
| No. Observations: | 15 | AIC: | 144.2 |
| Df Residuals: | 12 | BIC: | 146.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -88.0145 | 88.375 | -0.996 | 0.339 | -280.567 104.538 |
| C(dose)[T.1] | 53.9402 | 14.268 | 3.780 | 0.003 | 22.852 85.028 |
| expression | 24.2651 | 13.703 | 1.771 | 0.102 | -5.591 54.121 |
| Omnibus: | 0.823 | Durbin-Watson: | 0.917 |
| Prob(Omnibus): | 0.663 | Jarque-Bera (JB): | 0.780 |
| Skew: | -0.415 | Prob(JB): | 0.677 |
| Kurtosis: | 2.253 | Cond. No. | 82.2 |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:55:52 | 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: | 0.043 |
| Model: | OLS | Adj. R-squared: | -0.031 |
| Method: | Least Squares | F-statistic: | 0.5770 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.461 |
| Time: | 22:55:52 | Log-Likelihood: | -74.974 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 2.0418 | 121.027 | 0.017 | 0.987 | -259.422 263.505 |
| expression | 14.5395 | 19.140 | 0.760 | 0.461 | -26.811 55.890 |
| Omnibus: | 0.334 | Durbin-Watson: | 1.666 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.467 |
| Skew: | 0.005 | Prob(JB): | 0.792 |
| Kurtosis: | 2.135 | Cond. No. | 78.9 |