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 |
| 0.264 | 0.613 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 11.97 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000126 |
| Time: | 23:04:43 | Log-Likelihood: | -100.90 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.8057 | 67.890 | 1.146 | 0.266 | -64.290 219.901 |
| C(dose)[T.1] | 43.3639 | 77.105 | 0.562 | 0.580 | -118.019 204.747 |
| expression | -3.7079 | 10.624 | -0.349 | 0.731 | -25.943 18.527 |
| expression:C(dose)[T.1] | 1.4416 | 12.193 | 0.118 | 0.907 | -24.079 26.963 |
| Omnibus: | 0.314 | Durbin-Watson: | 1.903 |
| Prob(Omnibus): | 0.855 | Jarque-Bera (JB): | 0.479 |
| Skew: | -0.037 | Prob(JB): | 0.787 |
| Kurtosis: | 2.297 | Cond. No. | 161. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.87 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.49e-05 |
| Time: | 23:04:43 | Log-Likelihood: | -100.91 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 70.8417 | 32.913 | 2.152 | 0.044 | 2.187 139.496 |
| C(dose)[T.1] | 52.4157 | 8.895 | 5.893 | 0.000 | 33.861 70.970 |
| expression | -2.6136 | 5.084 | -0.514 | 0.613 | -13.219 7.992 |
| Omnibus: | 0.227 | Durbin-Watson: | 1.899 |
| Prob(Omnibus): | 0.893 | Jarque-Bera (JB): | 0.424 |
| Skew: | -0.022 | Prob(JB): | 0.809 |
| Kurtosis: | 2.336 | Cond. No. | 48.8 |
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: | 23:04:43 | 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: | 0.052 |
| Model: | OLS | Adj. R-squared: | 0.007 |
| Method: | Least Squares | F-statistic: | 1.158 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.294 |
| Time: | 23:04:43 | Log-Likelihood: | -112.49 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.3128 | 50.299 | 2.650 | 0.015 | 28.711 237.915 |
| expression | -8.6508 | 8.039 | -1.076 | 0.294 | -25.369 8.067 |
| Omnibus: | 1.828 | Durbin-Watson: | 2.641 |
| Prob(Omnibus): | 0.401 | Jarque-Bera (JB): | 1.220 |
| Skew: | 0.293 | Prob(JB): | 0.543 |
| Kurtosis: | 2.036 | Cond. No. | 45.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.956 | 0.347 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.684 |
| Model: | OLS | Adj. R-squared: | 0.597 |
| Method: | Least Squares | F-statistic: | 7.925 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00430 |
| Time: | 23:04:43 | Log-Likelihood: | -66.668 |
| No. Observations: | 15 | AIC: | 141.3 |
| Df Residuals: | 11 | BIC: | 144.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 118.5976 | 94.358 | 1.257 | 0.235 | -89.083 326.279 |
| C(dose)[T.1] | -348.0193 | 155.527 | -2.238 | 0.047 | -690.331 -5.708 |
| expression | -7.2961 | 13.392 | -0.545 | 0.597 | -36.771 22.179 |
| expression:C(dose)[T.1] | 59.8093 | 23.014 | 2.599 | 0.025 | 9.155 110.464 |
| Omnibus: | 5.115 | Durbin-Watson: | 1.461 |
| Prob(Omnibus): | 0.078 | Jarque-Bera (JB): | 2.754 |
| Skew: | -1.023 | Prob(JB): | 0.252 |
| Kurtosis: | 3.465 | Cond. No. | 216. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.489 |
| Model: | OLS | Adj. R-squared: | 0.404 |
| Method: | Least Squares | F-statistic: | 5.752 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0177 |
| Time: | 23:04:43 | Log-Likelihood: | -70.258 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 12 | BIC: | 148.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -23.4248 | 93.562 | -0.250 | 0.807 | -227.278 180.429 |
| C(dose)[T.1] | 54.6826 | 16.153 | 3.385 | 0.005 | 19.488 89.877 |
| expression | 12.9546 | 13.247 | 0.978 | 0.347 | -15.909 41.818 |
| Omnibus: | 0.915 | Durbin-Watson: | 0.979 |
| Prob(Omnibus): | 0.633 | Jarque-Bera (JB): | 0.827 |
| Skew: | -0.460 | Prob(JB): | 0.661 |
| Kurtosis: | 2.311 | Cond. No. | 86.6 |
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: | 23:04:43 | 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: | 0.002 |
| Model: | OLS | Adj. R-squared: | -0.075 |
| Method: | Least Squares | F-statistic: | 0.02466 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.878 |
| Time: | 23:04:43 | Log-Likelihood: | -75.286 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.4529 | 113.721 | 0.980 | 0.345 | -134.227 357.133 |
| expression | -2.6205 | 16.688 | -0.157 | 0.878 | -38.673 33.432 |
| Omnibus: | 0.654 | Durbin-Watson: | 1.572 |
| Prob(Omnibus): | 0.721 | Jarque-Bera (JB): | 0.600 |
| Skew: | 0.056 | Prob(JB): | 0.741 |
| Kurtosis: | 2.026 | Cond. No. | 78.0 |