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 |
| 0.079 | 0.781 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.10 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.17e-05 |
| Time: | 03:39:37 | Log-Likelihood: | -100.21 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 213.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 91.1228 | 40.515 | 2.249 | 0.037 | 6.325 175.921 |
| C(dose)[T.1] | -23.1994 | 65.075 | -0.357 | 0.725 | -159.403 113.004 |
| expression | -7.2988 | 7.922 | -0.921 | 0.368 | -23.881 9.283 |
| expression:C(dose)[T.1] | 15.8394 | 13.470 | 1.176 | 0.254 | -12.353 44.032 |
| Omnibus: | 0.185 | Durbin-Watson: | 1.908 |
| Prob(Omnibus): | 0.912 | Jarque-Bera (JB): | 0.395 |
| Skew: | 0.029 | Prob(JB): | 0.821 |
| Kurtosis: | 2.361 | Cond. No. | 93.0 |
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: | 18.61 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.72e-05 |
| Time: | 03:39:37 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 63.4104 | 33.269 | 1.906 | 0.071 | -5.988 132.809 |
| C(dose)[T.1] | 52.5759 | 9.161 | 5.739 | 0.000 | 33.466 71.686 |
| expression | -1.8194 | 6.468 | -0.281 | 0.781 | -15.312 11.673 |
| Omnibus: | 0.187 | Durbin-Watson: | 1.887 |
| Prob(Omnibus): | 0.911 | Jarque-Bera (JB): | 0.396 |
| Skew: | 0.050 | Prob(JB): | 0.821 |
| Kurtosis: | 2.365 | Cond. No. | 39.2 |
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: | 03:39:37 | 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: | 0.075 |
| Model: | OLS | Adj. R-squared: | 0.031 |
| Method: | Least Squares | F-statistic: | 1.698 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.207 |
| Time: | 03:39:37 | Log-Likelihood: | -112.21 |
| No. Observations: | 23 | AIC: | 228.4 |
| Df Residuals: | 21 | BIC: | 230.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 141.8199 | 48.160 | 2.945 | 0.008 | 41.665 241.975 |
| expression | -12.7848 | 9.811 | -1.303 | 0.207 | -33.188 7.618 |
| Omnibus: | 2.332 | Durbin-Watson: | 2.335 |
| Prob(Omnibus): | 0.312 | Jarque-Bera (JB): | 1.566 |
| Skew: | 0.416 | Prob(JB): | 0.457 |
| Kurtosis: | 2.029 | Cond. No. | 35.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.225 | 0.644 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.485 |
| Model: | OLS | Adj. R-squared: | 0.344 |
| Method: | Least Squares | F-statistic: | 3.450 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0551 |
| Time: | 03:39:37 | Log-Likelihood: | -70.327 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 11 | BIC: | 151.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 122.1275 | 67.738 | 1.803 | 0.099 | -26.964 271.219 |
| C(dose)[T.1] | -30.0305 | 104.618 | -0.287 | 0.779 | -260.294 200.233 |
| expression | -10.2343 | 12.487 | -0.820 | 0.430 | -37.717 17.248 |
| expression:C(dose)[T.1] | 15.3450 | 20.658 | 0.743 | 0.473 | -30.124 60.814 |
| Omnibus: | 4.722 | Durbin-Watson: | 1.162 |
| Prob(Omnibus): | 0.094 | Jarque-Bera (JB): | 2.732 |
| Skew: | -1.040 | Prob(JB): | 0.255 |
| Kurtosis: | 3.210 | Cond. No. | 87.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 5.089 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0251 |
| Time: | 03:39:37 | Log-Likelihood: | -70.694 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 92.1648 | 53.392 | 1.726 | 0.110 | -24.167 208.497 |
| C(dose)[T.1] | 46.6722 | 16.478 | 2.832 | 0.015 | 10.770 82.574 |
| expression | -4.6282 | 9.760 | -0.474 | 0.644 | -25.893 16.637 |
| Omnibus: | 2.539 | Durbin-Watson: | 0.939 |
| Prob(Omnibus): | 0.281 | Jarque-Bera (JB): | 1.836 |
| Skew: | -0.821 | Prob(JB): | 0.399 |
| Kurtosis: | 2.506 | Cond. No. | 36.9 |
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: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 03:39:37 | 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: | 0.097 |
| Model: | OLS | Adj. R-squared: | 0.028 |
| Method: | Least Squares | F-statistic: | 1.399 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.258 |
| Time: | 03:39:37 | Log-Likelihood: | -74.533 |
| No. Observations: | 15 | AIC: | 153.1 |
| Df Residuals: | 13 | BIC: | 154.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 162.1892 | 58.732 | 2.762 | 0.016 | 35.307 289.071 |
| expression | -13.5586 | 11.463 | -1.183 | 0.258 | -38.323 11.206 |
| Omnibus: | 0.429 | Durbin-Watson: | 1.930 |
| Prob(Omnibus): | 0.807 | Jarque-Bera (JB): | 0.534 |
| Skew: | 0.267 | Prob(JB): | 0.766 |
| Kurtosis: | 2.245 | Cond. No. | 32.3 |