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 |
| 0.327 | 0.574 | 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): | 8.95e-05 |
| Time: | 04:45:01 | 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 | 237.9821 | 190.672 | 1.248 | 0.227 | -161.099 637.064 |
| C(dose)[T.1] | -179.2906 | 289.551 | -0.619 | 0.543 | -785.327 426.746 |
| expression | -19.1559 | 19.865 | -0.964 | 0.347 | -60.734 22.422 |
| expression:C(dose)[T.1] | 24.1131 | 29.717 | 0.811 | 0.427 | -38.085 86.312 |
| Omnibus: | 0.360 | Durbin-Watson: | 1.622 |
| Prob(Omnibus): | 0.835 | Jarque-Bera (JB): | 0.511 |
| Skew: | -0.099 | Prob(JB): | 0.775 |
| Kurtosis: | 2.297 | Cond. No. | 822. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 18.96 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.41e-05 |
| Time: | 04:45:01 | Log-Likelihood: | -100.88 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 134.6116 | 140.652 | 0.957 | 0.350 | -158.783 428.006 |
| C(dose)[T.1] | 55.5292 | 9.505 | 5.842 | 0.000 | 35.702 75.357 |
| expression | -8.3809 | 14.648 | -0.572 | 0.574 | -38.935 22.173 |
| Omnibus: | 1.062 | Durbin-Watson: | 1.779 |
| Prob(Omnibus): | 0.588 | Jarque-Bera (JB): | 0.807 |
| Skew: | 0.070 | Prob(JB): | 0.668 |
| Kurtosis: | 2.093 | Cond. No. | 319. |
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: | 04:45:02 | 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: | 0.065 |
| Model: | OLS | Adj. R-squared: | 0.021 |
| Method: | Least Squares | F-statistic: | 1.472 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.239 |
| Time: | 04:45:02 | Log-Likelihood: | -112.33 |
| No. Observations: | 23 | AIC: | 228.7 |
| Df Residuals: | 21 | BIC: | 230.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -174.0280 | 209.278 | -0.832 | 0.415 | -609.245 261.189 |
| expression | 26.1091 | 21.522 | 1.213 | 0.239 | -18.648 70.866 |
| Omnibus: | 3.323 | Durbin-Watson: | 2.504 |
| Prob(Omnibus): | 0.190 | Jarque-Bera (JB): | 1.432 |
| Skew: | 0.181 | Prob(JB): | 0.489 |
| Kurtosis: | 1.832 | Cond. No. | 295. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.023 | 0.882 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.302 |
| Method: | Least Squares | F-statistic: | 3.023 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0755 |
| Time: | 04:45:02 | Log-Likelihood: | -70.790 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 61.4264 | 269.491 | 0.228 | 0.824 | -531.720 654.573 |
| C(dose)[T.1] | -61.5928 | 538.847 | -0.114 | 0.911 | -1247.587 1124.402 |
| expression | 0.6411 | 28.757 | 0.022 | 0.983 | -62.653 63.936 |
| expression:C(dose)[T.1] | 11.7794 | 57.342 | 0.205 | 0.841 | -114.429 137.987 |
| Omnibus: | 3.179 | Durbin-Watson: | 0.757 |
| Prob(Omnibus): | 0.204 | Jarque-Bera (JB): | 2.126 |
| Skew: | -0.911 | Prob(JB): | 0.345 |
| Kurtosis: | 2.708 | Cond. No. | 754. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.905 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0277 |
| Time: | 04:45:02 | Log-Likelihood: | -70.819 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 33.6903 | 223.727 | 0.151 | 0.883 | -453.769 521.150 |
| C(dose)[T.1] | 49.0482 | 15.755 | 3.113 | 0.009 | 14.720 83.376 |
| expression | 3.6038 | 23.866 | 0.151 | 0.882 | -48.396 55.603 |
| Omnibus: | 2.944 | Durbin-Watson: | 0.768 |
| Prob(Omnibus): | 0.230 | Jarque-Bera (JB): | 2.000 |
| Skew: | -0.878 | Prob(JB): | 0.368 |
| Kurtosis: | 2.659 | Cond. No. | 271. |
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: | 04:45:02 | 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: | 0.005 |
| Model: | OLS | Adj. R-squared: | -0.071 |
| Method: | Least Squares | F-statistic: | 0.07162 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.793 |
| Time: | 04:45:02 | Log-Likelihood: | -75.259 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 16.3958 | 288.907 | 0.057 | 0.956 | -607.749 640.541 |
| expression | 8.2344 | 30.768 | 0.268 | 0.793 | -58.237 74.706 |
| Omnibus: | 0.492 | Durbin-Watson: | 1.607 |
| Prob(Omnibus): | 0.782 | Jarque-Bera (JB): | 0.536 |
| Skew: | 0.001 | Prob(JB): | 0.765 |
| Kurtosis: | 2.074 | Cond. No. | 271. |