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 |
| 0.008 | 0.929 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.698 |
| Model: | OLS | Adj. R-squared: | 0.650 |
| Method: | Least Squares | F-statistic: | 14.65 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.52e-05 |
| Time: | 04:40:08 | Log-Likelihood: | -99.331 |
| No. Observations: | 23 | AIC: | 206.7 |
| Df Residuals: | 19 | BIC: | 211.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 126.7492 | 52.531 | 2.413 | 0.026 | 16.802 236.697 |
| C(dose)[T.1] | -69.2070 | 70.396 | -0.983 | 0.338 | -216.548 78.134 |
| expression | -20.2117 | 14.548 | -1.389 | 0.181 | -50.660 10.237 |
| expression:C(dose)[T.1] | 33.7414 | 19.229 | 1.755 | 0.095 | -6.506 73.989 |
| Omnibus: | 0.465 | Durbin-Watson: | 2.133 |
| Prob(Omnibus): | 0.793 | Jarque-Bera (JB): | 0.588 |
| Skew: | 0.199 | Prob(JB): | 0.745 |
| Kurtosis: | 2.325 | Cond. No. | 89.0 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.51 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.82e-05 |
| Time: | 04:40:08 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 57.4372 | 36.384 | 1.579 | 0.130 | -18.458 133.332 |
| C(dose)[T.1] | 53.4331 | 8.833 | 6.049 | 0.000 | 35.008 71.858 |
| expression | -0.8996 | 9.996 | -0.090 | 0.929 | -21.750 19.951 |
| Omnibus: | 0.247 | Durbin-Watson: | 1.879 |
| Prob(Omnibus): | 0.884 | Jarque-Bera (JB): | 0.438 |
| Skew: | 0.018 | Prob(JB): | 0.803 |
| Kurtosis: | 2.325 | Cond. No. | 33.0 |
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: | 04:40:08 | 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: | 0.007 |
| Model: | OLS | Adj. R-squared: | -0.040 |
| Method: | Least Squares | F-statistic: | 0.1546 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.698 |
| Time: | 04:40:08 | Log-Likelihood: | -113.02 |
| No. Observations: | 23 | AIC: | 230.0 |
| Df Residuals: | 21 | BIC: | 232.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 56.4054 | 59.729 | 0.944 | 0.356 | -67.808 180.619 |
| expression | 6.4042 | 16.289 | 0.393 | 0.698 | -27.471 40.280 |
| Omnibus: | 2.516 | Durbin-Watson: | 2.561 |
| Prob(Omnibus): | 0.284 | Jarque-Bera (JB): | 1.360 |
| Skew: | 0.262 | Prob(JB): | 0.507 |
| Kurtosis: | 1.930 | Cond. No. | 32.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.814 | 0.203 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.596 |
| Model: | OLS | Adj. R-squared: | 0.486 |
| Method: | Least Squares | F-statistic: | 5.404 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0157 |
| Time: | 04:40:08 | Log-Likelihood: | -68.507 |
| No. Observations: | 15 | AIC: | 145.0 |
| Df Residuals: | 11 | BIC: | 147.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 110.5441 | 74.743 | 1.479 | 0.167 | -53.964 275.052 |
| C(dose)[T.1] | 264.4307 | 154.629 | 1.710 | 0.115 | -75.905 604.766 |
| expression | -8.8093 | 15.126 | -0.582 | 0.572 | -42.102 24.483 |
| expression:C(dose)[T.1] | -46.4732 | 32.613 | -1.425 | 0.182 | -118.253 25.307 |
| Omnibus: | 0.503 | Durbin-Watson: | 0.705 |
| Prob(Omnibus): | 0.778 | Jarque-Bera (JB): | 0.571 |
| Skew: | -0.206 | Prob(JB): | 0.752 |
| Kurtosis: | 2.137 | Cond. No. | 131. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.521 |
| Model: | OLS | Adj. R-squared: | 0.441 |
| Method: | Least Squares | F-statistic: | 6.530 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0121 |
| Time: | 04:40:08 | Log-Likelihood: | -69.777 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 12 | BIC: | 147.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.4753 | 69.181 | 2.305 | 0.040 | 8.743 310.207 |
| C(dose)[T.1] | 45.0395 | 14.991 | 3.004 | 0.011 | 12.377 77.702 |
| expression | -18.8069 | 13.964 | -1.347 | 0.203 | -49.233 11.619 |
| Omnibus: | 2.717 | Durbin-Watson: | 0.861 |
| Prob(Omnibus): | 0.257 | Jarque-Bera (JB): | 1.458 |
| Skew: | -0.467 | Prob(JB): | 0.482 |
| Kurtosis: | 1.792 | Cond. No. | 47.7 |
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: | 04:40:08 | 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: | 0.161 |
| Model: | OLS | Adj. R-squared: | 0.096 |
| Method: | Least Squares | F-statistic: | 2.494 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.138 |
| Time: | 04:40:08 | Log-Likelihood: | -73.984 |
| No. Observations: | 15 | AIC: | 152.0 |
| Df Residuals: | 13 | BIC: | 153.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 224.7560 | 83.530 | 2.691 | 0.019 | 44.300 405.212 |
| expression | -27.4451 | 17.379 | -1.579 | 0.138 | -64.991 10.100 |
| Omnibus: | 2.733 | Durbin-Watson: | 1.706 |
| Prob(Omnibus): | 0.255 | Jarque-Bera (JB): | 1.085 |
| Skew: | 0.062 | Prob(JB): | 0.581 |
| Kurtosis: | 1.688 | Cond. No. | 45.0 |