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 |
| 0.270 | 0.609 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 12.69 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.75e-05 |
| Time: | 03:39:30 | Log-Likelihood: | -100.46 |
| No. Observations: | 23 | AIC: | 208.9 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 19.0008 | 125.614 | 0.151 | 0.881 | -243.912 281.913 |
| C(dose)[T.1] | 207.3715 | 175.131 | 1.184 | 0.251 | -159.181 573.924 |
| expression | 4.4560 | 15.880 | 0.281 | 0.782 | -28.780 37.692 |
| expression:C(dose)[T.1] | -19.0809 | 21.844 | -0.874 | 0.393 | -64.800 26.638 |
| Omnibus: | 1.285 | Durbin-Watson: | 1.761 |
| Prob(Omnibus): | 0.526 | Jarque-Bera (JB): | 0.979 |
| Skew: | 0.233 | Prob(JB): | 0.613 |
| Kurtosis: | 2.103 | Cond. No. | 428. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.88 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.48e-05 |
| Time: | 03:39:30 | Log-Likelihood: | -100.91 |
| 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 | 98.6763 | 85.854 | 1.149 | 0.264 | -80.413 277.765 |
| C(dose)[T.1] | 54.5964 | 9.043 | 6.038 | 0.000 | 35.734 73.459 |
| expression | -5.6280 | 10.839 | -0.519 | 0.609 | -28.238 16.982 |
| Omnibus: | 0.713 | Durbin-Watson: | 1.917 |
| Prob(Omnibus): | 0.700 | Jarque-Bera (JB): | 0.683 |
| Skew: | 0.098 | Prob(JB): | 0.711 |
| Kurtosis: | 2.178 | Cond. No. | 161. |
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: | 03:39:30 | 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: | 0.4851 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.494 |
| Time: | 03:39:30 | Log-Likelihood: | -112.84 |
| No. Observations: | 23 | AIC: | 229.7 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -15.7798 | 137.291 | -0.115 | 0.910 | -301.293 269.734 |
| expression | 11.9249 | 17.121 | 0.697 | 0.494 | -23.680 47.529 |
| Omnibus: | 2.108 | Durbin-Watson: | 2.383 |
| Prob(Omnibus): | 0.349 | Jarque-Bera (JB): | 1.414 |
| Skew: | 0.368 | Prob(JB): | 0.493 |
| Kurtosis: | 2.033 | Cond. No. | 157. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.106 | 0.750 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.613 |
| Model: | OLS | Adj. R-squared: | 0.508 |
| Method: | Least Squares | F-statistic: | 5.812 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0125 |
| Time: | 03:39:30 | Log-Likelihood: | -68.177 |
| No. Observations: | 15 | AIC: | 144.4 |
| Df Residuals: | 11 | BIC: | 147.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -32.9868 | 96.196 | -0.343 | 0.738 | -244.712 178.738 |
| C(dose)[T.1] | 369.0777 | 151.081 | 2.443 | 0.033 | 36.552 701.604 |
| expression | 14.3003 | 13.624 | 1.050 | 0.316 | -15.686 44.287 |
| expression:C(dose)[T.1] | -46.1741 | 21.680 | -2.130 | 0.057 | -93.891 1.543 |
| Omnibus: | 0.044 | Durbin-Watson: | 1.218 |
| Prob(Omnibus): | 0.978 | Jarque-Bera (JB): | 0.193 |
| Skew: | -0.103 | Prob(JB): | 0.908 |
| Kurtosis: | 2.483 | Cond. No. | 199. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.454 |
| Model: | OLS | Adj. R-squared: | 0.363 |
| Method: | Least Squares | F-statistic: | 4.981 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0266 |
| Time: | 03:39:30 | Log-Likelihood: | -70.767 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 95.0584 | 85.445 | 1.113 | 0.288 | -91.110 281.227 |
| C(dose)[T.1] | 48.6595 | 15.756 | 3.088 | 0.009 | 14.329 82.990 |
| expression | -3.9348 | 12.059 | -0.326 | 0.750 | -30.208 22.339 |
| Omnibus: | 2.189 | Durbin-Watson: | 0.789 |
| Prob(Omnibus): | 0.335 | Jarque-Bera (JB): | 1.624 |
| Skew: | -0.757 | Prob(JB): | 0.444 |
| Kurtosis: | 2.449 | Cond. No. | 78.1 |
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: | 03:39:30 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.056 |
| Method: | Least Squares | F-statistic: | 0.2569 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.621 |
| Time: | 03:39:30 | Log-Likelihood: | -75.153 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 148.0377 | 107.739 | 1.374 | 0.193 | -84.718 380.794 |
| expression | -7.8241 | 15.436 | -0.507 | 0.621 | -41.172 25.524 |
| Omnibus: | 0.125 | Durbin-Watson: | 1.530 |
| Prob(Omnibus): | 0.939 | Jarque-Bera (JB): | 0.337 |
| Skew: | -0.103 | Prob(JB): | 0.845 |
| Kurtosis: | 2.295 | Cond. No. | 76.3 |