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 |
| 1.497 | 0.235 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.674 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.11e-05 |
| Time: | 04:58:59 | Log-Likelihood: | -100.20 |
| No. Observations: | 23 | AIC: | 208.4 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 217.6951 | 197.257 | 1.104 | 0.284 | -195.169 630.559 |
| C(dose)[T.1] | -12.2445 | 225.827 | -0.054 | 0.957 | -484.907 460.418 |
| expression | -17.3489 | 20.923 | -0.829 | 0.417 | -61.141 26.443 |
| expression:C(dose)[T.1] | 5.8711 | 24.563 | 0.239 | 0.814 | -45.540 57.283 |
| Omnibus: | 0.413 | Durbin-Watson: | 2.127 |
| Prob(Omnibus): | 0.813 | Jarque-Bera (JB): | 0.534 |
| Skew: | -0.041 | Prob(JB): | 0.766 |
| Kurtosis: | 2.258 | Cond. No. | 679. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.673 |
| Model: | OLS | Adj. R-squared: | 0.641 |
| Method: | Least Squares | F-statistic: | 20.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.38e-05 |
| Time: | 04:58:59 | Log-Likelihood: | -100.23 |
| No. Observations: | 23 | AIC: | 206.5 |
| Df Residuals: | 20 | BIC: | 209.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 177.5527 | 100.996 | 1.758 | 0.094 | -33.122 388.227 |
| C(dose)[T.1] | 41.6424 | 12.765 | 3.262 | 0.004 | 15.015 68.270 |
| expression | -13.0890 | 10.700 | -1.223 | 0.235 | -35.408 9.230 |
| Omnibus: | 0.332 | Durbin-Watson: | 2.108 |
| Prob(Omnibus): | 0.847 | Jarque-Bera (JB): | 0.489 |
| Skew: | 0.004 | Prob(JB): | 0.783 |
| Kurtosis: | 2.286 | Cond. No. | 219. |
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: | 04:58:59 | 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: | 0.500 |
| Model: | OLS | Adj. R-squared: | 0.476 |
| Method: | Least Squares | F-statistic: | 20.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000162 |
| Time: | 04:58:59 | Log-Likelihood: | -105.14 |
| No. Observations: | 23 | AIC: | 214.3 |
| Df Residuals: | 21 | BIC: | 216.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 432.6283 | 77.219 | 5.603 | 0.000 | 272.042 593.214 |
| expression | -39.2291 | 8.565 | -4.580 | 0.000 | -57.041 -21.418 |
| Omnibus: | 1.027 | Durbin-Watson: | 2.536 |
| Prob(Omnibus): | 0.598 | Jarque-Bera (JB): | 0.852 |
| Skew: | 0.190 | Prob(JB): | 0.653 |
| Kurtosis: | 2.137 | Cond. No. | 138. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.000 | 0.985 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.477 |
| Model: | OLS | Adj. R-squared: | 0.335 |
| Method: | Least Squares | F-statistic: | 3.347 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0594 |
| Time: | 04:58:59 | Log-Likelihood: | -70.436 |
| No. Observations: | 15 | AIC: | 148.9 |
| Df Residuals: | 11 | BIC: | 151.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 1.0155 | 121.779 | 0.008 | 0.993 | -267.019 269.050 |
| C(dose)[T.1] | 179.9975 | 169.914 | 1.059 | 0.312 | -193.980 553.975 |
| expression | 7.7238 | 14.097 | 0.548 | 0.595 | -23.304 38.752 |
| expression:C(dose)[T.1] | -15.0273 | 19.438 | -0.773 | 0.456 | -57.811 27.756 |
| Omnibus: | 1.072 | Durbin-Watson: | 0.817 |
| Prob(Omnibus): | 0.585 | Jarque-Bera (JB): | 0.894 |
| Skew: | -0.516 | Prob(JB): | 0.639 |
| Kurtosis: | 2.396 | Cond. No. | 254. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.885 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:58:59 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 68.9774 | 82.848 | 0.833 | 0.421 | -111.532 249.487 |
| C(dose)[T.1] | 49.2356 | 15.876 | 3.101 | 0.009 | 14.646 83.826 |
| expression | -0.1801 | 9.542 | -0.019 | 0.985 | -20.970 20.610 |
| Omnibus: | 2.712 | Durbin-Watson: | 0.815 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.869 |
| Skew: | -0.843 | Prob(JB): | 0.393 |
| Kurtosis: | 2.617 | Cond. No. | 93.7 |
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: | 04:58:59 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.069 |
| Method: | Least Squares | F-statistic: | 0.09143 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.767 |
| Time: | 04:58:59 | Log-Likelihood: | -75.248 |
| 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 | 61.5215 | 106.791 | 0.576 | 0.574 | -169.187 292.230 |
| expression | 3.6887 | 12.199 | 0.302 | 0.767 | -22.666 30.043 |
| Omnibus: | 1.263 | Durbin-Watson: | 1.567 |
| Prob(Omnibus): | 0.532 | Jarque-Bera (JB): | 0.792 |
| Skew: | 0.100 | Prob(JB): | 0.673 |
| Kurtosis: | 1.892 | Cond. No. | 93.5 |