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 |
| 3.304 | 0.084 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.706 |
| Model: | OLS | Adj. R-squared: | 0.659 |
| Method: | Least Squares | F-statistic: | 15.20 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.76e-05 |
| Time: | 23:02:21 | Log-Likelihood: | -99.030 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 19 | BIC: | 210.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -384.7578 | 254.910 | -1.509 | 0.148 | -918.291 148.775 |
| C(dose)[T.1] | 293.1873 | 348.117 | 0.842 | 0.410 | -435.431 1021.805 |
| expression | 45.8369 | 26.611 | 1.722 | 0.101 | -9.861 101.535 |
| expression:C(dose)[T.1] | -24.7520 | 36.579 | -0.677 | 0.507 | -101.312 51.808 |
| Omnibus: | 0.441 | Durbin-Watson: | 1.787 |
| Prob(Omnibus): | 0.802 | Jarque-Bera (JB): | 0.571 |
| Skew: | -0.188 | Prob(JB): | 0.752 |
| Kurtosis: | 2.326 | Cond. No. | 1.07e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.699 |
| Model: | OLS | Adj. R-squared: | 0.669 |
| Method: | Least Squares | F-statistic: | 23.20 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 6.14e-06 |
| Time: | 23:02:21 | Log-Likelihood: | -99.304 |
| No. Observations: | 23 | AIC: | 204.6 |
| Df Residuals: | 20 | BIC: | 208.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -259.3017 | 172.558 | -1.503 | 0.149 | -619.251 100.647 |
| C(dose)[T.1] | 57.6967 | 8.471 | 6.811 | 0.000 | 40.027 75.367 |
| expression | 32.7367 | 18.009 | 1.818 | 0.084 | -4.829 70.303 |
| Omnibus: | 0.334 | Durbin-Watson: | 1.716 |
| Prob(Omnibus): | 0.846 | Jarque-Bera (JB): | 0.498 |
| Skew: | -0.139 | Prob(JB): | 0.780 |
| Kurtosis: | 2.335 | Cond. No. | 410. |
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: | 23:02:21 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.004203 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.949 |
| Time: | 23:02:21 | 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 | 98.6568 | 292.242 | 0.338 | 0.739 | -509.094 706.408 |
| expression | -1.9909 | 30.711 | -0.065 | 0.949 | -65.858 61.876 |
| Omnibus: | 3.160 | Durbin-Watson: | 2.500 |
| Prob(Omnibus): | 0.206 | Jarque-Bera (JB): | 1.537 |
| Skew: | 0.287 | Prob(JB): | 0.464 |
| Kurtosis: | 1.871 | Cond. No. | 390. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.008 | 0.929 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.523 |
| Model: | OLS | Adj. R-squared: | 0.393 |
| Method: | Least Squares | F-statistic: | 4.021 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0371 |
| Time: | 23:02:21 | Log-Likelihood: | -69.748 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 169.0989 | 130.681 | 1.294 | 0.222 | -118.528 456.726 |
| C(dose)[T.1] | -280.1280 | 253.346 | -1.106 | 0.292 | -837.738 277.482 |
| expression | -13.6061 | 17.425 | -0.781 | 0.451 | -51.957 24.745 |
| expression:C(dose)[T.1] | 42.3098 | 32.415 | 1.305 | 0.218 | -29.036 113.655 |
| Omnibus: | 3.206 | Durbin-Watson: | 0.983 |
| Prob(Omnibus): | 0.201 | Jarque-Bera (JB): | 1.755 |
| Skew: | -0.837 | Prob(JB): | 0.416 |
| Kurtosis: | 3.058 | Cond. No. | 320. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.892 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0279 |
| Time: | 23:02:21 | Log-Likelihood: | -70.828 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.7453 | 113.548 | 0.685 | 0.507 | -169.655 325.146 |
| C(dose)[T.1] | 49.8298 | 17.195 | 2.898 | 0.013 | 12.365 87.294 |
| expression | -1.3806 | 15.118 | -0.091 | 0.929 | -34.319 31.558 |
| Omnibus: | 2.780 | Durbin-Watson: | 0.840 |
| Prob(Omnibus): | 0.249 | Jarque-Bera (JB): | 1.893 |
| Skew: | -0.852 | Prob(JB): | 0.388 |
| Kurtosis: | 2.645 | Cond. No. | 114. |
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: | 23:02:21 | 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: | 0.064 |
| Model: | OLS | Adj. R-squared: | -0.008 |
| Method: | Least Squares | F-statistic: | 0.8838 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.364 |
| Time: | 23:02:22 | Log-Likelihood: | -74.807 |
| No. Observations: | 15 | AIC: | 153.6 |
| Df Residuals: | 13 | BIC: | 155.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -32.0444 | 134.083 | -0.239 | 0.815 | -321.714 257.625 |
| expression | 16.2900 | 17.328 | 0.940 | 0.364 | -21.145 53.725 |
| Omnibus: | 1.094 | Durbin-Watson: | 1.254 |
| Prob(Omnibus): | 0.579 | Jarque-Bera (JB): | 0.775 |
| Skew: | -0.175 | Prob(JB): | 0.679 |
| Kurtosis: | 1.944 | Cond. No. | 107. |