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 |
| 0.396 | 0.536 | 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: | 12.25 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000109 |
| Time: | 04:35:26 | Log-Likelihood: | -100.73 |
| 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 | 22.9301 | 43.278 | 0.530 | 0.602 | -67.653 113.513 |
| C(dose)[T.1] | 78.2155 | 56.822 | 1.376 | 0.185 | -40.715 197.146 |
| expression | 5.7257 | 7.842 | 0.730 | 0.474 | -10.689 22.140 |
| expression:C(dose)[T.1] | -4.4987 | 10.480 | -0.429 | 0.673 | -26.433 17.436 |
| Omnibus: | 0.265 | Durbin-Watson: | 1.842 |
| Prob(Omnibus): | 0.876 | Jarque-Bera (JB): | 0.450 |
| Skew: | -0.087 | Prob(JB): | 0.798 |
| Kurtosis: | 2.337 | Cond. No. | 95.6 |
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: | 04:35:26 | 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 | 36.6923 | 28.473 | 1.289 | 0.212 | -22.702 96.086 |
| C(dose)[T.1] | 54.1288 | 8.775 | 6.169 | 0.000 | 35.825 72.433 |
| expression | 3.2064 | 5.095 | 0.629 | 0.536 | -7.421 13.834 |
| Omnibus: | 0.196 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.906 | Jarque-Bera (JB): | 0.403 |
| Skew: | -0.005 | Prob(JB): | 0.817 |
| Kurtosis: | 2.351 | Cond. No. | 36.9 |
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: | 04:35:26 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.046 |
| Method: | Least Squares | F-statistic: | 0.02402 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.878 |
| Time: | 04:35:27 | Log-Likelihood: | -113.09 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 86.6612 | 45.384 | 1.909 | 0.070 | -7.721 181.043 |
| expression | -1.2992 | 8.383 | -0.155 | 0.878 | -18.734 16.135 |
| Omnibus: | 3.501 | Durbin-Watson: | 2.498 |
| Prob(Omnibus): | 0.174 | Jarque-Bera (JB): | 1.605 |
| Skew: | 0.287 | Prob(JB): | 0.448 |
| Kurtosis: | 1.840 | Cond. No. | 35.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.573 | 0.464 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.480 |
| Model: | OLS | Adj. R-squared: | 0.338 |
| Method: | Least Squares | F-statistic: | 3.378 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0581 |
| Time: | 04:35:27 | Log-Likelihood: | -70.402 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 124.2876 | 80.879 | 1.537 | 0.153 | -53.727 302.302 |
| C(dose)[T.1] | 11.4307 | 95.768 | 0.119 | 0.907 | -199.353 222.214 |
| expression | -10.3012 | 14.500 | -0.710 | 0.492 | -42.215 21.613 |
| expression:C(dose)[T.1] | 6.2188 | 18.029 | 0.345 | 0.737 | -33.462 45.900 |
| Omnibus: | 4.106 | Durbin-Watson: | 0.803 |
| Prob(Omnibus): | 0.128 | Jarque-Bera (JB): | 2.393 |
| Skew: | -0.977 | Prob(JB): | 0.302 |
| Kurtosis: | 3.091 | Cond. No. | 90.9 |
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: | 5.405 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0212 |
| Time: | 04:35:27 | Log-Likelihood: | -70.483 |
| 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 | 102.0850 | 47.140 | 2.166 | 0.051 | -0.625 204.795 |
| C(dose)[T.1] | 43.9051 | 16.891 | 2.599 | 0.023 | 7.103 80.708 |
| expression | -6.2788 | 8.295 | -0.757 | 0.464 | -24.351 11.794 |
| Omnibus: | 5.843 | Durbin-Watson: | 0.785 |
| Prob(Omnibus): | 0.054 | Jarque-Bera (JB): | 3.297 |
| Skew: | -1.120 | Prob(JB): | 0.192 |
| Kurtosis: | 3.504 | Cond. No. | 33.6 |
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: | 04:35:27 | 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: | 0.178 |
| Model: | OLS | Adj. R-squared: | 0.114 |
| Method: | Least Squares | F-statistic: | 2.809 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.118 |
| Time: | 04:35:27 | Log-Likelihood: | -73.833 |
| No. Observations: | 15 | AIC: | 151.7 |
| Df Residuals: | 13 | BIC: | 153.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 170.7395 | 46.901 | 3.640 | 0.003 | 69.417 272.062 |
| expression | -15.2012 | 9.070 | -1.676 | 0.118 | -34.796 4.393 |
| Omnibus: | 0.843 | Durbin-Watson: | 1.568 |
| Prob(Omnibus): | 0.656 | Jarque-Bera (JB): | 0.749 |
| Skew: | -0.290 | Prob(JB): | 0.687 |
| Kurtosis: | 2.071 | Cond. No. | 27.3 |