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 |
| 0.069 | 0.796 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.49 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 5.98e-05 |
| Time: | 18:29:46 | Log-Likelihood: | -99.985 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 122.1998 | 57.711 | 2.117 | 0.048 | 1.410 242.990 |
| C(dose)[T.1] | -47.0004 | 74.977 | -0.627 | 0.538 | -203.930 109.929 |
| expression | -16.1041 | 13.597 | -1.184 | 0.251 | -44.562 12.354 |
| expression:C(dose)[T.1] | 24.2132 | 18.068 | 1.340 | 0.196 | -13.603 62.029 |
| Omnibus: | 1.204 | Durbin-Watson: | 2.042 |
| Prob(Omnibus): | 0.548 | Jarque-Bera (JB): | 0.845 |
| Skew: | -0.021 | Prob(JB): | 0.655 |
| Kurtosis: | 2.062 | Cond. No. | 102. |
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: | 18.59 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 2.74e-05 |
| Time: | 18:29:46 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.3071 | 39.021 | 1.648 | 0.115 | -17.090 145.704 |
| C(dose)[T.1] | 52.7796 | 9.010 | 5.858 | 0.000 | 33.985 71.574 |
| expression | -2.3919 | 9.130 | -0.262 | 0.796 | -21.438 16.654 |
| Omnibus: | 0.225 | Durbin-Watson: | 1.974 |
| Prob(Omnibus): | 0.894 | Jarque-Bera (JB): | 0.423 |
| Skew: | 0.037 | Prob(JB): | 0.809 |
| Kurtosis: | 2.340 | Cond. No. | 39.3 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 18:29:46 | 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: | 0.050 |
| Model: | OLS | Adj. R-squared: | 0.005 |
| Method: | Least Squares | F-statistic: | 1.109 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.304 |
| Time: | 18:29:46 | Log-Likelihood: | -112.51 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 141.4829 | 59.071 | 2.395 | 0.026 | 18.637 264.328 |
| expression | -15.0263 | 14.269 | -1.053 | 0.304 | -44.699 14.647 |
| Omnibus: | 1.691 | Durbin-Watson: | 2.670 |
| Prob(Omnibus): | 0.429 | Jarque-Bera (JB): | 1.453 |
| Skew: | 0.490 | Prob(JB): | 0.484 |
| Kurtosis: | 2.254 | Cond. No. | 36.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.418 | 0.146 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.589 |
| Model: | OLS | Adj. R-squared: | 0.477 |
| Method: | Least Squares | F-statistic: | 5.258 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.0171 |
| Time: | 18:29:46 | Log-Likelihood: | -68.628 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 11 | BIC: | 148.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 29.6411 | 67.690 | 0.438 | 0.670 | -119.343 178.625 |
| C(dose)[T.1] | -77.8152 | 112.119 | -0.694 | 0.502 | -324.588 168.957 |
| expression | 6.1874 | 10.953 | 0.565 | 0.583 | -17.920 30.295 |
| expression:C(dose)[T.1] | 20.4837 | 18.076 | 1.133 | 0.281 | -19.302 60.269 |
| Omnibus: | 0.518 | Durbin-Watson: | 0.930 |
| Prob(Omnibus): | 0.772 | Jarque-Bera (JB): | 0.563 |
| Skew: | -0.142 | Prob(JB): | 0.755 |
| Kurtosis: | 2.094 | Cond. No. | 126. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.541 |
| Model: | OLS | Adj. R-squared: | 0.465 |
| Method: | Least Squares | F-statistic: | 7.078 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00933 |
| Time: | 18:29:46 | Log-Likelihood: | -69.456 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 12 | BIC: | 147.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -16.2891 | 54.851 | -0.297 | 0.772 | -135.800 103.222 |
| C(dose)[T.1] | 48.2123 | 14.373 | 3.354 | 0.006 | 16.896 79.529 |
| expression | 13.7082 | 8.816 | 1.555 | 0.146 | -5.500 32.916 |
| Omnibus: | 3.467 | Durbin-Watson: | 0.626 |
| Prob(Omnibus): | 0.177 | Jarque-Bera (JB): | 1.370 |
| Skew: | -0.307 | Prob(JB): | 0.504 |
| Kurtosis: | 1.652 | Cond. No. | 48.9 |
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: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 18:29:46 | 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: | 0.111 |
| Model: | OLS | Adj. R-squared: | 0.043 |
| Method: | Least Squares | F-statistic: | 1.624 |
| Date: | Tue, 28 Jan 2025 | Prob (F-statistic): | 0.225 |
| Time: | 18:29:46 | Log-Likelihood: | -74.417 |
| No. Observations: | 15 | AIC: | 152.8 |
| Df Residuals: | 13 | BIC: | 154.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.4225 | 73.016 | 0.019 | 0.985 | -156.319 159.164 |
| expression | 15.0102 | 11.779 | 1.274 | 0.225 | -10.436 40.456 |
| Omnibus: | 0.280 | Durbin-Watson: | 1.658 |
| Prob(Omnibus): | 0.869 | Jarque-Bera (JB): | 0.443 |
| Skew: | 0.096 | Prob(JB): | 0.801 |
| Kurtosis: | 2.180 | Cond. No. | 48.5 |