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 |
| 9.835 | 0.005 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.768 |
| Model: | OLS | Adj. R-squared: | 0.731 |
| Method: | Least Squares | F-statistic: | 20.91 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.07e-06 |
| Time: | 04:14:46 | Log-Likelihood: | -96.325 |
| No. Observations: | 23 | AIC: | 200.6 |
| Df Residuals: | 19 | BIC: | 205.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -128.7463 | 83.400 | -1.544 | 0.139 | -303.305 45.813 |
| C(dose)[T.1] | 109.5348 | 101.449 | 1.080 | 0.294 | -102.801 321.871 |
| expression | 55.0529 | 25.050 | 2.198 | 0.041 | 2.623 107.483 |
| expression:C(dose)[T.1] | -14.8597 | 30.986 | -0.480 | 0.637 | -79.714 49.995 |
| Omnibus: | 2.814 | Durbin-Watson: | 2.535 |
| Prob(Omnibus): | 0.245 | Jarque-Bera (JB): | 1.287 |
| Skew: | 0.473 | Prob(JB): | 0.525 |
| Kurtosis: | 3.668 | Cond. No. | 137. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.765 |
| Model: | OLS | Adj. R-squared: | 0.741 |
| Method: | Least Squares | F-statistic: | 32.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.19e-07 |
| Time: | 04:14:46 | Log-Likelihood: | -96.463 |
| No. Observations: | 23 | AIC: | 198.9 |
| Df Residuals: | 20 | BIC: | 202.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -96.4727 | 48.303 | -1.997 | 0.060 | -197.230 4.285 |
| C(dose)[T.1] | 61.0254 | 7.587 | 8.043 | 0.000 | 45.199 76.852 |
| expression | 45.3415 | 14.458 | 3.136 | 0.005 | 15.183 75.500 |
| Omnibus: | 3.789 | Durbin-Watson: | 2.522 |
| Prob(Omnibus): | 0.150 | Jarque-Bera (JB): | 1.986 |
| Skew: | 0.588 | Prob(JB): | 0.370 |
| Kurtosis: | 3.830 | Cond. No. | 48.3 |
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: | 04:14:46 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.07994 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.780 |
| Time: | 04:14:46 | Log-Likelihood: | -113.06 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.5306 | 89.376 | 0.610 | 0.548 | -131.336 240.398 |
| expression | 7.7685 | 27.477 | 0.283 | 0.780 | -49.373 64.910 |
| Omnibus: | 3.056 | Durbin-Watson: | 2.579 |
| Prob(Omnibus): | 0.217 | Jarque-Bera (JB): | 1.498 |
| Skew: | 0.276 | Prob(JB): | 0.473 |
| Kurtosis: | 1.878 | Cond. No. | 44.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.071 | 0.176 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.612 |
| Model: | OLS | Adj. R-squared: | 0.506 |
| Method: | Least Squares | F-statistic: | 5.778 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0127 |
| Time: | 04:14:46 | Log-Likelihood: | -68.203 |
| 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 | 15.2971 | 157.691 | 0.097 | 0.924 | -331.778 362.372 |
| C(dose)[T.1] | 356.3255 | 198.430 | 1.796 | 0.100 | -80.417 793.068 |
| expression | 15.4616 | 46.674 | 0.331 | 0.747 | -87.266 118.189 |
| expression:C(dose)[T.1] | -88.1673 | 57.885 | -1.523 | 0.156 | -215.571 39.236 |
| Omnibus: | 1.407 | Durbin-Watson: | 0.838 |
| Prob(Omnibus): | 0.495 | Jarque-Bera (JB): | 1.137 |
| Skew: | -0.512 | Prob(JB): | 0.566 |
| Kurtosis: | 2.122 | Cond. No. | 154. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.530 |
| Model: | OLS | Adj. R-squared: | 0.452 |
| Method: | Least Squares | F-statistic: | 6.764 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0108 |
| Time: | 04:14:46 | Log-Likelihood: | -69.639 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 12 | BIC: | 147.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 208.5679 | 98.640 | 2.114 | 0.056 | -6.349 423.485 |
| C(dose)[T.1] | 54.8713 | 15.060 | 3.643 | 0.003 | 22.057 87.685 |
| expression | -41.8602 | 29.085 | -1.439 | 0.176 | -105.232 21.511 |
| Omnibus: | 2.608 | Durbin-Watson: | 0.939 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.604 |
| Skew: | -0.568 | Prob(JB): | 0.448 |
| Kurtosis: | 1.871 | Cond. No. | 51.6 |
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: | 04:14:46 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.066 |
| Method: | Least Squares | F-statistic: | 0.1301 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.724 |
| Time: | 04:14:46 | Log-Likelihood: | -75.225 |
| 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 | 142.2805 | 135.177 | 1.053 | 0.312 | -149.753 434.314 |
| expression | -14.1156 | 39.140 | -0.361 | 0.724 | -98.673 70.442 |
| Omnibus: | 0.328 | Durbin-Watson: | 1.652 |
| Prob(Omnibus): | 0.849 | Jarque-Bera (JB): | 0.465 |
| Skew: | 0.004 | Prob(JB): | 0.793 |
| Kurtosis: | 2.138 | Cond. No. | 50.0 |