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 |
| 0.039 | 0.846 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.606 |
| Method: | Least Squares | F-statistic: | 12.26 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000108 |
| Time: | 04:40:27 | Log-Likelihood: | -100.72 |
| No. Observations: | 23 | AIC: | 209.4 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 37.7132 | 45.791 | 0.824 | 0.420 | -58.128 133.554 |
| C(dose)[T.1] | 101.3923 | 66.133 | 1.533 | 0.142 | -37.026 239.811 |
| expression | 4.5698 | 12.572 | 0.364 | 0.720 | -21.743 30.883 |
| expression:C(dose)[T.1] | -13.4470 | 18.301 | -0.735 | 0.471 | -51.752 24.858 |
| Omnibus: | 0.077 | Durbin-Watson: | 1.749 |
| Prob(Omnibus): | 0.962 | Jarque-Bera (JB): | 0.188 |
| Skew: | -0.116 | Prob(JB): | 0.910 |
| Kurtosis: | 2.623 | Cond. No. | 74.4 |
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: | 18.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.78e-05 |
| Time: | 04:40:27 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 60.6165 | 33.155 | 1.828 | 0.082 | -8.543 129.776 |
| C(dose)[T.1] | 53.2405 | 8.775 | 6.067 | 0.000 | 34.936 71.545 |
| expression | -1.7753 | 9.030 | -0.197 | 0.846 | -20.613 17.062 |
| Omnibus: | 0.142 | Durbin-Watson: | 1.869 |
| Prob(Omnibus): | 0.932 | Jarque-Bera (JB): | 0.361 |
| Skew: | 0.033 | Prob(JB): | 0.835 |
| Kurtosis: | 2.390 | Cond. No. | 29.7 |
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: | 04:40:27 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.042 |
| Method: | Least Squares | F-statistic: | 0.1066 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.747 |
| Time: | 04:40:27 | Log-Likelihood: | -113.05 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 97.0715 | 53.629 | 1.810 | 0.085 | -14.456 208.599 |
| expression | -4.8427 | 14.830 | -0.327 | 0.747 | -35.683 25.998 |
| Omnibus: | 3.506 | Durbin-Watson: | 2.450 |
| Prob(Omnibus): | 0.173 | Jarque-Bera (JB): | 1.535 |
| Skew: | 0.239 | Prob(JB): | 0.464 |
| Kurtosis: | 1.828 | Cond. No. | 29.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.895 | 0.363 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.508 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 3.780 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0437 |
| Time: | 04:40:27 | Log-Likelihood: | -69.986 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.3533 | 87.139 | 1.829 | 0.095 | -32.439 351.146 |
| C(dose)[T.1] | -17.2642 | 106.095 | -0.163 | 0.874 | -250.779 216.250 |
| expression | -23.6639 | 22.241 | -1.064 | 0.310 | -72.616 25.288 |
| expression:C(dose)[T.1] | 17.7601 | 26.182 | 0.678 | 0.512 | -39.867 75.387 |
| Omnibus: | 1.592 | Durbin-Watson: | 1.284 |
| Prob(Omnibus): | 0.451 | Jarque-Bera (JB): | 1.220 |
| Skew: | -0.638 | Prob(JB): | 0.543 |
| Kurtosis: | 2.431 | Cond. No. | 89.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.487 |
| Model: | OLS | Adj. R-squared: | 0.402 |
| Method: | Least Squares | F-statistic: | 5.696 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0182 |
| Time: | 04:40:27 | Log-Likelihood: | -70.294 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 12 | BIC: | 148.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 109.5693 | 45.908 | 2.387 | 0.034 | 9.544 209.595 |
| C(dose)[T.1] | 53.8455 | 15.959 | 3.374 | 0.006 | 19.073 88.618 |
| expression | -10.8482 | 11.468 | -0.946 | 0.363 | -35.835 14.139 |
| Omnibus: | 1.483 | Durbin-Watson: | 1.049 |
| Prob(Omnibus): | 0.476 | Jarque-Bera (JB): | 1.205 |
| Skew: | -0.596 | Prob(JB): | 0.547 |
| Kurtosis: | 2.288 | Cond. No. | 27.0 |
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: | 04:40:27 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.005320 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.943 |
| Time: | 04:40:27 | Log-Likelihood: | -75.297 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.2767 | 61.040 | 1.463 | 0.167 | -42.591 221.145 |
| expression | 1.0673 | 14.633 | 0.073 | 0.943 | -30.546 32.680 |
| Omnibus: | 0.474 | Durbin-Watson: | 1.589 |
| Prob(Omnibus): | 0.789 | Jarque-Bera (JB): | 0.530 |
| Skew: | 0.029 | Prob(JB): | 0.767 |
| Kurtosis: | 2.081 | Cond. No. | 26.5 |