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 |
| 0.398 | 0.535 | 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: | 0.605 |
| Method: | Least Squares | F-statistic: | 12.23 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000110 |
| Time: | 04:57:12 | Log-Likelihood: | -100.74 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 75.1911 | 59.236 | 1.269 | 0.220 | -48.791 199.173 |
| C(dose)[T.1] | 95.9978 | 115.101 | 0.834 | 0.415 | -144.911 336.907 |
| expression | -3.9232 | 11.016 | -0.356 | 0.726 | -26.980 19.133 |
| expression:C(dose)[T.1] | -9.4793 | 23.483 | -0.404 | 0.691 | -58.629 39.671 |
| Omnibus: | 0.456 | Durbin-Watson: | 1.964 |
| Prob(Omnibus): | 0.796 | Jarque-Bera (JB): | 0.574 |
| Skew: | 0.149 | Prob(JB): | 0.751 |
| Kurtosis: | 2.286 | Cond. No. | 158. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.06 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.33e-05 |
| Time: | 04:57:12 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.3479 | 51.285 | 1.684 | 0.108 | -20.630 193.325 |
| C(dose)[T.1] | 49.7331 | 10.394 | 4.785 | 0.000 | 28.052 71.414 |
| expression | -6.0092 | 9.523 | -0.631 | 0.535 | -25.873 13.855 |
| Omnibus: | 0.471 | Durbin-Watson: | 1.983 |
| Prob(Omnibus): | 0.790 | Jarque-Bera (JB): | 0.576 |
| Skew: | 0.119 | Prob(JB): | 0.750 |
| Kurtosis: | 2.262 | Cond. No. | 63.1 |
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: | 04:57:12 | 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: | 0.262 |
| Model: | OLS | Adj. R-squared: | 0.227 |
| Method: | Least Squares | F-statistic: | 7.455 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0125 |
| Time: | 04:57:12 | Log-Likelihood: | -109.61 |
| No. Observations: | 23 | AIC: | 223.2 |
| Df Residuals: | 21 | BIC: | 225.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 236.8672 | 57.889 | 4.092 | 0.001 | 116.480 357.254 |
| expression | -31.0478 | 11.371 | -2.730 | 0.013 | -54.696 -7.400 |
| Omnibus: | 0.883 | Durbin-Watson: | 2.525 |
| Prob(Omnibus): | 0.643 | Jarque-Bera (JB): | 0.744 |
| Skew: | 0.075 | Prob(JB): | 0.689 |
| Kurtosis: | 2.132 | Cond. No. | 49.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.269 | 0.613 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.551 |
| Model: | OLS | Adj. R-squared: | 0.429 |
| Method: | Least Squares | F-statistic: | 4.502 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0271 |
| Time: | 04:57:12 | Log-Likelihood: | -69.292 |
| No. Observations: | 15 | AIC: | 146.6 |
| Df Residuals: | 11 | BIC: | 149.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 150.2643 | 153.231 | 0.981 | 0.348 | -186.994 487.523 |
| C(dose)[T.1] | -299.1960 | 235.406 | -1.271 | 0.230 | -817.322 218.930 |
| expression | -17.6628 | 32.591 | -0.542 | 0.599 | -89.396 54.070 |
| expression:C(dose)[T.1] | 75.2608 | 50.594 | 1.488 | 0.165 | -36.096 186.618 |
| Omnibus: | 0.654 | Durbin-Watson: | 0.827 |
| Prob(Omnibus): | 0.721 | Jarque-Bera (JB): | 0.640 |
| Skew: | -0.212 | Prob(JB): | 0.726 |
| Kurtosis: | 2.082 | Cond. No. | 198. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.461 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 5.129 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0246 |
| Time: | 04:57:12 | Log-Likelihood: | -70.667 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 3.8014 | 123.201 | 0.031 | 0.976 | -264.631 272.234 |
| C(dose)[T.1] | 50.2725 | 15.704 | 3.201 | 0.008 | 16.057 84.488 |
| expression | 13.5670 | 26.158 | 0.519 | 0.613 | -43.426 70.560 |
| Omnibus: | 1.655 | Durbin-Watson: | 0.804 |
| Prob(Omnibus): | 0.437 | Jarque-Bera (JB): | 1.316 |
| Skew: | -0.638 | Prob(JB): | 0.518 |
| Kurtosis: | 2.311 | Cond. No. | 77.7 |
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: | 04:57:12 | 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: | 0.005447 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.942 |
| Time: | 04:57:12 | 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 | 82.0323 | 157.971 | 0.519 | 0.612 | -259.243 423.307 |
| expression | 2.5033 | 33.920 | 0.074 | 0.942 | -70.776 75.783 |
| Omnibus: | 0.550 | Durbin-Watson: | 1.622 |
| Prob(Omnibus): | 0.760 | Jarque-Bera (JB): | 0.561 |
| Skew: | 0.041 | Prob(JB): | 0.756 |
| Kurtosis: | 2.056 | Cond. No. | 75.7 |