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 |
| 0.787 | 0.386 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 13.14 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.03e-05 |
| Time: | 04:45:50 | Log-Likelihood: | -100.19 |
| 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 | 8.0091 | 242.513 | 0.033 | 0.974 | -499.577 515.596 |
| C(dose)[T.1] | 306.2363 | 296.521 | 1.033 | 0.315 | -314.389 926.861 |
| expression | 5.7919 | 30.394 | 0.191 | 0.851 | -57.824 69.408 |
| expression:C(dose)[T.1] | -31.7777 | 37.193 | -0.854 | 0.404 | -109.623 46.068 |
| Omnibus: | 2.066 | Durbin-Watson: | 1.862 |
| Prob(Omnibus): | 0.356 | Jarque-Bera (JB): | 1.120 |
| Skew: | 0.128 | Prob(JB): | 0.571 |
| Kurtosis: | 1.950 | Cond. No. | 776. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.62 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.93e-05 |
| Time: | 04:45:50 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 177.2872 | 138.909 | 1.276 | 0.216 | -112.471 467.046 |
| C(dose)[T.1] | 52.9957 | 8.611 | 6.154 | 0.000 | 35.034 70.958 |
| expression | -15.4302 | 17.399 | -0.887 | 0.386 | -51.723 20.863 |
| Omnibus: | 1.489 | Durbin-Watson: | 1.942 |
| Prob(Omnibus): | 0.475 | Jarque-Bera (JB): | 0.941 |
| Skew: | 0.073 | Prob(JB): | 0.625 |
| Kurtosis: | 2.020 | Cond. No. | 262. |
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: | 04:45:50 | 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: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.024 |
| Method: | Least Squares | F-statistic: | 0.4909 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.491 |
| Time: | 04:45:50 | Log-Likelihood: | -112.84 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 231.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 240.7643 | 229.971 | 1.047 | 0.307 | -237.487 719.016 |
| expression | -20.2170 | 28.856 | -0.701 | 0.491 | -80.226 39.791 |
| Omnibus: | 2.951 | Durbin-Watson: | 2.437 |
| Prob(Omnibus): | 0.229 | Jarque-Bera (JB): | 1.384 |
| Skew: | 0.204 | Prob(JB): | 0.501 |
| Kurtosis: | 1.870 | Cond. No. | 261. |
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: | 0.489 |
| Model: | OLS | Adj. R-squared: | 0.350 |
| Method: | Least Squares | F-statistic: | 3.508 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0529 |
| Time: | 04:45:50 | Log-Likelihood: | -70.265 |
| No. Observations: | 15 | AIC: | 148.5 |
| Df Residuals: | 11 | BIC: | 151.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -239.8582 | 822.582 | -0.292 | 0.776 | -2050.350 1570.633 |
| C(dose)[T.1] | -158.6821 | 1021.172 | -0.155 | 0.879 | -2406.266 2088.902 |
| expression | 35.5001 | 95.022 | 0.374 | 0.716 | -173.641 244.641 |
| expression:C(dose)[T.1] | 23.8935 | 117.874 | 0.203 | 0.843 | -235.546 283.333 |
| Omnibus: | 2.121 | Durbin-Watson: | 0.787 |
| Prob(Omnibus): | 0.346 | Jarque-Bera (JB): | 1.646 |
| Skew: | -0.716 | Prob(JB): | 0.439 |
| Kurtosis: | 2.236 | Cond. No. | 1.61e+03 |
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: | 5.697 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0182 |
| Time: | 04:45:50 | Log-Likelihood: | -70.293 |
| 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 | -374.2583 | 466.991 | -0.801 | 0.438 | -1391.744 643.227 |
| C(dose)[T.1] | 48.2875 | 15.214 | 3.174 | 0.008 | 15.139 81.436 |
| expression | 51.0270 | 53.935 | 0.946 | 0.363 | -66.487 168.542 |
| Omnibus: | 2.021 | Durbin-Watson: | 0.811 |
| Prob(Omnibus): | 0.364 | Jarque-Bera (JB): | 1.573 |
| Skew: | -0.685 | Prob(JB): | 0.455 |
| Kurtosis: | 2.200 | Cond. No. | 542. |
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: | 04:45:50 | 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: | 0.056 |
| Model: | OLS | Adj. R-squared: | -0.016 |
| Method: | Least Squares | F-statistic: | 0.7773 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.394 |
| Time: | 04:45:50 | Log-Likelihood: | -74.865 |
| No. Observations: | 15 | AIC: | 153.7 |
| Df Residuals: | 13 | BIC: | 155.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -442.1805 | 607.879 | -0.727 | 0.480 | -1755.423 871.062 |
| expression | 61.8373 | 70.141 | 0.882 | 0.394 | -89.692 213.367 |
| Omnibus: | 1.038 | Durbin-Watson: | 1.375 |
| Prob(Omnibus): | 0.595 | Jarque-Bera (JB): | 0.729 |
| Skew: | 0.094 | Prob(JB): | 0.694 |
| Kurtosis: | 1.936 | Cond. No. | 541. |