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 |
| 3.865 | 0.063 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.713 |
| Model: | OLS | Adj. R-squared: | 0.668 |
| Method: | Least Squares | F-statistic: | 15.77 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.17e-05 |
| Time: | 04:33:36 | Log-Likelihood: | -98.732 |
| No. Observations: | 23 | AIC: | 205.5 |
| Df Residuals: | 19 | BIC: | 210.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -308.9502 | 215.096 | -1.436 | 0.167 | -759.151 141.250 |
| C(dose)[T.1] | 238.1058 | 262.255 | 0.908 | 0.375 | -310.801 787.013 |
| expression | 42.7080 | 25.287 | 1.689 | 0.108 | -10.218 95.634 |
| expression:C(dose)[T.1] | -21.7731 | 30.806 | -0.707 | 0.488 | -86.250 42.704 |
| Omnibus: | 0.337 | Durbin-Watson: | 2.286 |
| Prob(Omnibus): | 0.845 | Jarque-Bera (JB): | 0.494 |
| Skew: | 0.196 | Prob(JB): | 0.781 |
| Kurtosis: | 2.399 | Cond. No. | 781. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.706 |
| Model: | OLS | Adj. R-squared: | 0.676 |
| Method: | Least Squares | F-statistic: | 24.00 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 4.84e-06 |
| Time: | 04:33:36 | Log-Likelihood: | -99.031 |
| No. Observations: | 23 | AIC: | 204.1 |
| Df Residuals: | 20 | BIC: | 207.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -184.2005 | 121.388 | -1.517 | 0.145 | -437.412 69.011 |
| C(dose)[T.1] | 52.8355 | 8.032 | 6.578 | 0.000 | 36.080 69.591 |
| expression | 28.0372 | 14.260 | 1.966 | 0.063 | -1.710 57.784 |
| Omnibus: | 0.553 | Durbin-Watson: | 2.297 |
| Prob(Omnibus): | 0.758 | Jarque-Bera (JB): | 0.638 |
| Skew: | 0.291 | Prob(JB): | 0.727 |
| Kurtosis: | 2.428 | Cond. No. | 262. |
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: | 04:33:36 | 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: | 0.070 |
| Model: | OLS | Adj. R-squared: | 0.025 |
| Method: | Least Squares | F-statistic: | 1.572 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.224 |
| Time: | 04:33:36 | Log-Likelihood: | -112.27 |
| No. Observations: | 23 | AIC: | 228.5 |
| Df Residuals: | 21 | BIC: | 230.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -184.2925 | 210.698 | -0.875 | 0.392 | -622.462 253.877 |
| expression | 31.0168 | 24.740 | 1.254 | 0.224 | -20.433 82.466 |
| Omnibus: | 1.699 | Durbin-Watson: | 2.851 |
| Prob(Omnibus): | 0.428 | Jarque-Bera (JB): | 1.355 |
| Skew: | 0.417 | Prob(JB): | 0.508 |
| Kurtosis: | 2.153 | Cond. No. | 261. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.022 | 0.181 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.539 |
| Model: | OLS | Adj. R-squared: | 0.413 |
| Method: | Least Squares | F-statistic: | 4.280 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0313 |
| Time: | 04:33:36 | Log-Likelihood: | -69.499 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 348.3128 | 250.254 | 1.392 | 0.191 | -202.493 899.119 |
| C(dose)[T.1] | -99.6676 | 287.360 | -0.347 | 0.735 | -732.142 532.807 |
| expression | -31.1356 | 27.714 | -1.123 | 0.285 | -92.133 29.862 |
| expression:C(dose)[T.1] | 15.9213 | 32.118 | 0.496 | 0.630 | -54.770 86.613 |
| Omnibus: | 1.287 | Durbin-Watson: | 0.861 |
| Prob(Omnibus): | 0.526 | Jarque-Bera (JB): | 0.905 |
| Skew: | -0.296 | Prob(JB): | 0.636 |
| Kurtosis: | 1.952 | Cond. No. | 508. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.528 |
| Model: | OLS | Adj. R-squared: | 0.450 |
| Method: | Least Squares | F-statistic: | 6.719 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0110 |
| Time: | 04:33:36 | Log-Likelihood: | -69.665 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 12 | BIC: | 147.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 241.3739 | 122.790 | 1.966 | 0.073 | -26.163 508.911 |
| C(dose)[T.1] | 42.5649 | 15.289 | 2.784 | 0.017 | 9.252 75.878 |
| expression | -19.2816 | 13.560 | -1.422 | 0.181 | -48.826 10.263 |
| Omnibus: | 1.465 | Durbin-Watson: | 0.864 |
| Prob(Omnibus): | 0.481 | Jarque-Bera (JB): | 0.899 |
| Skew: | -0.219 | Prob(JB): | 0.638 |
| Kurtosis: | 1.883 | Cond. No. | 152. |
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: | 04:33:36 | 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: | 0.224 |
| Model: | OLS | Adj. R-squared: | 0.164 |
| Method: | Least Squares | F-statistic: | 3.743 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0751 |
| Time: | 04:33:36 | Log-Likelihood: | -73.402 |
| No. Observations: | 15 | AIC: | 150.8 |
| Df Residuals: | 13 | BIC: | 152.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 365.8433 | 140.960 | 2.595 | 0.022 | 61.317 670.370 |
| expression | -30.7965 | 15.917 | -1.935 | 0.075 | -65.184 3.591 |
| Omnibus: | 1.604 | Durbin-Watson: | 1.616 |
| Prob(Omnibus): | 0.448 | Jarque-Bera (JB): | 1.243 |
| Skew: | 0.640 | Prob(JB): | 0.537 |
| Kurtosis: | 2.407 | Cond. No. | 141. |