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 |
| 0.746 | 0.398 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.40 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000101 |
| Time: | 04:50:01 | Log-Likelihood: | -100.63 |
| No. Observations: | 23 | AIC: | 209.3 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.7317 | 53.225 | 0.314 | 0.757 | -94.670 128.133 |
| C(dose)[T.1] | 64.2725 | 77.571 | 0.829 | 0.418 | -98.085 226.630 |
| expression | 7.1306 | 10.060 | 0.709 | 0.487 | -13.926 28.187 |
| expression:C(dose)[T.1] | -1.9471 | 14.870 | -0.131 | 0.897 | -33.070 29.176 |
| Omnibus: | 0.117 | Durbin-Watson: | 1.800 |
| Prob(Omnibus): | 0.943 | Jarque-Bera (JB): | 0.319 |
| Skew: | 0.106 | Prob(JB): | 0.852 |
| Kurtosis: | 2.463 | Cond. No. | 121. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.56 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.96e-05 |
| Time: | 04:50:01 | Log-Likelihood: | -100.64 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 21.4156 | 38.431 | 0.557 | 0.584 | -58.751 101.582 |
| C(dose)[T.1] | 54.1823 | 8.666 | 6.252 | 0.000 | 36.105 72.260 |
| expression | 6.2394 | 7.224 | 0.864 | 0.398 | -8.830 21.308 |
| Omnibus: | 0.114 | Durbin-Watson: | 1.765 |
| Prob(Omnibus): | 0.945 | Jarque-Bera (JB): | 0.325 |
| Skew: | 0.089 | Prob(JB): | 0.850 |
| Kurtosis: | 2.446 | Cond. No. | 48.6 |
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, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:50:02 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.008957 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.925 |
| Time: | 04:50:02 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.8022 | 62.916 | 1.173 | 0.254 | -57.038 204.642 |
| expression | 1.1395 | 12.040 | 0.095 | 0.925 | -23.900 26.179 |
| Omnibus: | 3.617 | Durbin-Watson: | 2.470 |
| Prob(Omnibus): | 0.164 | Jarque-Bera (JB): | 1.637 |
| Skew: | 0.293 | Prob(JB): | 0.441 |
| Kurtosis: | 1.832 | Cond. No. | 47.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.971 | 0.110 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.560 |
| Model: | OLS | Adj. R-squared: | 0.441 |
| Method: | Least Squares | F-statistic: | 4.676 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0243 |
| Time: | 04:50:02 | Log-Likelihood: | -69.135 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -30.6794 | 73.741 | -0.416 | 0.685 | -192.982 131.623 |
| C(dose)[T.1] | 80.3838 | 100.374 | 0.801 | 0.440 | -140.538 301.305 |
| expression | 17.0297 | 12.664 | 1.345 | 0.206 | -10.844 44.903 |
| expression:C(dose)[T.1] | -4.3288 | 17.994 | -0.241 | 0.814 | -43.934 35.276 |
| Omnibus: | 0.323 | Durbin-Watson: | 1.361 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.471 |
| Skew: | -0.189 | Prob(JB): | 0.790 |
| Kurtosis: | 2.219 | Cond. No. | 104. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.558 |
| Model: | OLS | Adj. R-squared: | 0.485 |
| Method: | Least Squares | F-statistic: | 7.580 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00744 |
| Time: | 04:50:02 | Log-Likelihood: | -69.174 |
| No. Observations: | 15 | AIC: | 144.3 |
| Df Residuals: | 12 | BIC: | 146.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -18.3271 | 50.807 | -0.361 | 0.725 | -129.026 92.371 |
| C(dose)[T.1] | 56.5205 | 14.718 | 3.840 | 0.002 | 24.452 88.589 |
| expression | 14.8856 | 8.636 | 1.724 | 0.110 | -3.931 33.702 |
| Omnibus: | 0.168 | Durbin-Watson: | 1.359 |
| Prob(Omnibus): | 0.920 | Jarque-Bera (JB): | 0.370 |
| Skew: | -0.120 | Prob(JB): | 0.831 |
| Kurtosis: | 2.269 | Cond. No. | 41.9 |
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: | 04:50:02 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.061 |
| Method: | Least Squares | F-statistic: | 0.2005 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.662 |
| Time: | 04:50:02 | Log-Likelihood: | -75.185 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 64.4642 | 65.989 | 0.977 | 0.346 | -78.097 207.025 |
| expression | 5.3109 | 11.860 | 0.448 | 0.662 | -20.311 30.933 |
| Omnibus: | 0.656 | Durbin-Watson: | 1.763 |
| Prob(Omnibus): | 0.721 | Jarque-Bera (JB): | 0.643 |
| Skew: | 0.221 | Prob(JB): | 0.725 |
| Kurtosis: | 2.086 | Cond. No. | 37.6 |