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 |
| 0.190 | 0.668 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.15 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000114 |
| Time: | 04:51:36 | Log-Likelihood: | -100.79 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 68.6098 | 105.114 | 0.653 | 0.522 | -151.396 288.616 |
| C(dose)[T.1] | -17.0321 | 134.659 | -0.126 | 0.901 | -298.876 264.812 |
| expression | -2.2499 | 16.393 | -0.137 | 0.892 | -36.561 32.061 |
| expression:C(dose)[T.1] | 11.2503 | 21.234 | 0.530 | 0.602 | -33.193 55.693 |
| Omnibus: | 1.047 | Durbin-Watson: | 1.876 |
| Prob(Omnibus): | 0.592 | Jarque-Bera (JB): | 0.887 |
| Skew: | 0.229 | Prob(JB): | 0.642 |
| Kurtosis: | 2.153 | Cond. No. | 266. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.76 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.58e-05 |
| Time: | 04:51:36 | Log-Likelihood: | -100.95 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.6869 | 65.762 | 0.391 | 0.700 | -111.489 162.863 |
| C(dose)[T.1] | 54.1515 | 8.927 | 6.066 | 0.000 | 35.531 72.772 |
| expression | 4.4557 | 10.230 | 0.436 | 0.668 | -16.884 25.795 |
| Omnibus: | 0.717 | Durbin-Watson: | 1.910 |
| Prob(Omnibus): | 0.699 | Jarque-Bera (JB): | 0.707 |
| Skew: | 0.156 | Prob(JB): | 0.702 |
| Kurtosis: | 2.200 | Cond. No. | 98.1 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:51:36 | 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: | 0.013 |
| Model: | OLS | Adj. R-squared: | -0.034 |
| Method: | Least Squares | F-statistic: | 0.2696 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.609 |
| Time: | 04:51:36 | Log-Likelihood: | -112.96 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.6533 | 104.117 | 1.284 | 0.213 | -82.870 350.176 |
| expression | -8.5427 | 16.452 | -0.519 | 0.609 | -42.756 25.670 |
| Omnibus: | 3.908 | Durbin-Watson: | 2.484 |
| Prob(Omnibus): | 0.142 | Jarque-Bera (JB): | 1.673 |
| Skew: | 0.283 | Prob(JB): | 0.433 |
| Kurtosis: | 1.806 | Cond. No. | 94.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.089 | 0.317 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.501 |
| Model: | OLS | Adj. R-squared: | 0.365 |
| Method: | Least Squares | F-statistic: | 3.683 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0468 |
| Time: | 04:51:36 | Log-Likelihood: | -70.085 |
| No. Observations: | 15 | AIC: | 148.2 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 215.8378 | 146.401 | 1.474 | 0.168 | -106.388 538.063 |
| C(dose)[T.1] | -40.9134 | 223.300 | -0.183 | 0.858 | -532.393 450.566 |
| expression | -19.4830 | 19.161 | -1.017 | 0.331 | -61.655 22.689 |
| expression:C(dose)[T.1] | 11.4035 | 30.182 | 0.378 | 0.713 | -55.027 77.834 |
| Omnibus: | 2.527 | Durbin-Watson: | 0.835 |
| Prob(Omnibus): | 0.283 | Jarque-Bera (JB): | 1.812 |
| Skew: | -0.818 | Prob(JB): | 0.404 |
| Kurtosis: | 2.527 | Cond. No. | 275. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.495 |
| Model: | OLS | Adj. R-squared: | 0.410 |
| Method: | Least Squares | F-statistic: | 5.873 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0167 |
| Time: | 04:51:36 | Log-Likelihood: | -70.182 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 180.8302 | 109.225 | 1.656 | 0.124 | -57.150 418.810 |
| C(dose)[T.1] | 43.2166 | 16.123 | 2.680 | 0.020 | 8.087 78.346 |
| expression | -14.8872 | 14.266 | -1.044 | 0.317 | -45.970 16.195 |
| Omnibus: | 2.908 | Durbin-Watson: | 0.754 |
| Prob(Omnibus): | 0.234 | Jarque-Bera (JB): | 2.133 |
| Skew: | -0.885 | Prob(JB): | 0.344 |
| Kurtosis: | 2.468 | Cond. No. | 110. |
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, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:51:36 | 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: | 0.192 |
| Model: | OLS | Adj. R-squared: | 0.130 |
| Method: | Least Squares | F-statistic: | 3.091 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.102 |
| Time: | 04:51:36 | Log-Likelihood: | -73.700 |
| No. Observations: | 15 | AIC: | 151.4 |
| Df Residuals: | 13 | BIC: | 152.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 304.4875 | 120.269 | 2.532 | 0.025 | 44.663 564.312 |
| expression | -28.4772 | 16.199 | -1.758 | 0.102 | -63.472 6.518 |
| Omnibus: | 5.678 | Durbin-Watson: | 1.722 |
| Prob(Omnibus): | 0.058 | Jarque-Bera (JB): | 1.518 |
| Skew: | 0.163 | Prob(JB): | 0.468 |
| Kurtosis: | 1.476 | Cond. No. | 99.5 |