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 |
| 0.058 | 0.812 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.713 |
| Model: | OLS | Adj. R-squared: | 0.668 |
| Method: | Least Squares | F-statistic: | 15.75 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.18e-05 |
| Time: | 22:53:04 | Log-Likelihood: | -98.740 |
| No. Observations: | 23 | AIC: | 205.5 |
| Df Residuals: | 19 | BIC: | 210.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -12.5093 | 45.116 | -0.277 | 0.785 | -106.939 81.920 |
| C(dose)[T.1] | 183.9021 | 63.844 | 2.880 | 0.010 | 50.275 317.529 |
| expression | 12.4357 | 8.344 | 1.490 | 0.153 | -5.028 29.899 |
| expression:C(dose)[T.1] | -27.0983 | 13.245 | -2.046 | 0.055 | -54.819 0.623 |
| Omnibus: | 0.225 | Durbin-Watson: | 2.351 |
| Prob(Omnibus): | 0.894 | Jarque-Bera (JB): | 0.393 |
| Skew: | -0.177 | Prob(JB): | 0.822 |
| Kurtosis: | 2.466 | Cond. No. | 100. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.58 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.75e-05 |
| Time: | 22:53:04 | Log-Likelihood: | -101.03 |
| 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 | 45.1887 | 37.918 | 1.192 | 0.247 | -33.907 124.285 |
| C(dose)[T.1] | 55.0362 | 11.243 | 4.895 | 0.000 | 31.584 78.489 |
| expression | 1.6812 | 6.977 | 0.241 | 0.812 | -12.872 16.235 |
| Omnibus: | 0.415 | Durbin-Watson: | 1.956 |
| Prob(Omnibus): | 0.813 | Jarque-Bera (JB): | 0.534 |
| Skew: | 0.017 | Prob(JB): | 0.766 |
| Kurtosis: | 2.254 | Cond. No. | 45.6 |
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: | 22:53:05 | 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: | 0.231 |
| Model: | OLS | Adj. R-squared: | 0.194 |
| Method: | Least Squares | F-statistic: | 6.302 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0203 |
| Time: | 22:53:05 | Log-Likelihood: | -110.09 |
| No. Observations: | 23 | AIC: | 224.2 |
| Df Residuals: | 21 | BIC: | 226.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 176.0722 | 38.902 | 4.526 | 0.000 | 95.171 256.973 |
| expression | -19.7380 | 7.863 | -2.510 | 0.020 | -36.090 -3.386 |
| Omnibus: | 1.036 | Durbin-Watson: | 1.831 |
| Prob(Omnibus): | 0.596 | Jarque-Bera (JB): | 0.819 |
| Skew: | 0.127 | Prob(JB): | 0.664 |
| Kurtosis: | 2.111 | Cond. No. | 31.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.270 | 0.282 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.509 |
| Model: | OLS | Adj. R-squared: | 0.375 |
| Method: | Least Squares | F-statistic: | 3.799 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0431 |
| Time: | 22:53:05 | Log-Likelihood: | -69.967 |
| No. Observations: | 15 | AIC: | 147.9 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 246.9592 | 203.159 | 1.216 | 0.250 | -200.190 694.109 |
| C(dose)[T.1] | -45.0000 | 233.076 | -0.193 | 0.850 | -557.996 467.996 |
| expression | -30.7766 | 34.773 | -0.885 | 0.395 | -107.311 45.758 |
| expression:C(dose)[T.1] | 16.1572 | 39.861 | 0.405 | 0.693 | -71.576 103.891 |
| Omnibus: | 2.376 | Durbin-Watson: | 0.882 |
| Prob(Omnibus): | 0.305 | Jarque-Bera (JB): | 1.118 |
| Skew: | -0.667 | Prob(JB): | 0.572 |
| Kurtosis: | 3.105 | Cond. No. | 271. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.502 |
| Model: | OLS | Adj. R-squared: | 0.418 |
| Method: | Least Squares | F-statistic: | 6.037 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0153 |
| Time: | 22:53:05 | Log-Likelihood: | -70.078 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 12 | BIC: | 148.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 175.2341 | 96.272 | 1.820 | 0.094 | -34.524 384.992 |
| C(dose)[T.1] | 49.2653 | 14.967 | 3.291 | 0.006 | 16.654 81.877 |
| expression | -18.4809 | 16.397 | -1.127 | 0.282 | -54.207 17.245 |
| Omnibus: | 2.892 | Durbin-Watson: | 0.963 |
| Prob(Omnibus): | 0.235 | Jarque-Bera (JB): | 1.328 |
| Skew: | -0.718 | Prob(JB): | 0.515 |
| Kurtosis: | 3.248 | Cond. No. | 78.0 |
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: | 22:53:05 | 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: | 0.052 |
| Model: | OLS | Adj. R-squared: | -0.021 |
| Method: | Least Squares | F-statistic: | 0.7061 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.416 |
| Time: | 22:53:05 | Log-Likelihood: | -74.903 |
| No. Observations: | 15 | AIC: | 153.8 |
| Df Residuals: | 13 | BIC: | 155.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 200.2232 | 127.193 | 1.574 | 0.139 | -74.561 475.007 |
| expression | -18.2606 | 21.731 | -0.840 | 0.416 | -65.207 28.686 |
| Omnibus: | 1.054 | Durbin-Watson: | 1.741 |
| Prob(Omnibus): | 0.590 | Jarque-Bera (JB): | 0.760 |
| Skew: | 0.169 | Prob(JB): | 0.684 |
| Kurtosis: | 1.950 | Cond. No. | 77.4 |