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 |
| 0.520 | 0.479 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 12.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.96e-05 |
| Time: | 04:40:01 | Log-Likelihood: | -100.48 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.0714 | 78.825 | 0.838 | 0.412 | -98.910 231.053 |
| C(dose)[T.1] | -19.3413 | 100.607 | -0.192 | 0.850 | -229.914 191.231 |
| expression | -3.0647 | 20.303 | -0.151 | 0.882 | -45.560 39.431 |
| expression:C(dose)[T.1] | 17.0950 | 24.862 | 0.688 | 0.500 | -34.941 69.131 |
| Omnibus: | 0.271 | Durbin-Watson: | 2.017 |
| Prob(Omnibus): | 0.873 | Jarque-Bera (JB): | 0.454 |
| Skew: | -0.110 | Prob(JB): | 0.797 |
| Kurtosis: | 2.347 | Cond. No. | 140. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 19.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.19e-05 |
| Time: | 04:40:01 | Log-Likelihood: | -100.77 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 21.9405 | 45.154 | 0.486 | 0.632 | -72.250 116.131 |
| C(dose)[T.1] | 49.4712 | 10.184 | 4.858 | 0.000 | 28.228 70.715 |
| expression | 8.3361 | 11.562 | 0.721 | 0.479 | -15.782 32.455 |
| Omnibus: | 0.251 | Durbin-Watson: | 1.773 |
| Prob(Omnibus): | 0.882 | Jarque-Bera (JB): | 0.440 |
| Skew: | 0.004 | Prob(JB): | 0.803 |
| Kurtosis: | 2.323 | Cond. No. | 46.2 |
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: | 04:40:01 | 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: | 0.254 |
| Model: | OLS | Adj. R-squared: | 0.219 |
| Method: | Least Squares | F-statistic: | 7.164 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0141 |
| Time: | 04:40:01 | Log-Likelihood: | -109.73 |
| No. Observations: | 23 | AIC: | 223.5 |
| Df Residuals: | 21 | BIC: | 225.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -75.4302 | 58.300 | -1.294 | 0.210 | -196.671 45.810 |
| expression | 37.9090 | 14.163 | 2.677 | 0.014 | 8.455 67.363 |
| Omnibus: | 6.516 | Durbin-Watson: | 1.874 |
| Prob(Omnibus): | 0.038 | Jarque-Bera (JB): | 1.860 |
| Skew: | 0.139 | Prob(JB): | 0.395 |
| Kurtosis: | 1.635 | Cond. No. | 40.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.571 | 0.464 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.601 |
| Model: | OLS | Adj. R-squared: | 0.493 |
| Method: | Least Squares | F-statistic: | 5.533 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0146 |
| Time: | 04:40:01 | Log-Likelihood: | -68.401 |
| No. Observations: | 15 | AIC: | 144.8 |
| Df Residuals: | 11 | BIC: | 147.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 234.3430 | 138.446 | 1.693 | 0.119 | -70.375 539.061 |
| C(dose)[T.1] | -260.6323 | 163.336 | -1.596 | 0.139 | -620.132 98.868 |
| expression | -41.6922 | 34.487 | -1.209 | 0.252 | -117.598 34.214 |
| expression:C(dose)[T.1] | 74.7679 | 39.835 | 1.877 | 0.087 | -12.908 162.444 |
| Omnibus: | 0.145 | Durbin-Watson: | 1.543 |
| Prob(Omnibus): | 0.930 | Jarque-Bera (JB): | 0.231 |
| Skew: | 0.184 | Prob(JB): | 0.891 |
| Kurtosis: | 2.517 | Cond. No. | 155. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 5.403 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0212 |
| Time: | 04:40:01 | Log-Likelihood: | -70.484 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 9.9827 | 76.842 | 0.130 | 0.899 | -157.441 177.407 |
| C(dose)[T.1] | 44.6433 | 16.516 | 2.703 | 0.019 | 8.658 80.629 |
| expression | 14.3489 | 18.988 | 0.756 | 0.464 | -27.021 55.719 |
| Omnibus: | 2.458 | Durbin-Watson: | 0.910 |
| Prob(Omnibus): | 0.293 | Jarque-Bera (JB): | 1.554 |
| Skew: | -0.779 | Prob(JB): | 0.460 |
| Kurtosis: | 2.751 | Cond. No. | 44.9 |
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: | 04:40:01 | 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: | 0.153 |
| Model: | OLS | Adj. R-squared: | 0.088 |
| Method: | Least Squares | F-statistic: | 2.356 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.149 |
| Time: | 04:40:01 | Log-Likelihood: | -74.051 |
| No. Observations: | 15 | AIC: | 152.1 |
| Df Residuals: | 13 | BIC: | 153.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -44.3327 | 90.384 | -0.490 | 0.632 | -239.596 150.931 |
| expression | 33.0717 | 21.545 | 1.535 | 0.149 | -13.472 79.616 |
| Omnibus: | 2.936 | Durbin-Watson: | 1.842 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 0.900 |
| Skew: | -0.389 | Prob(JB): | 0.638 |
| Kurtosis: | 3.914 | Cond. No. | 42.8 |