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 |
| 1.743 | 0.202 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.678 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 13.36 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.35e-05 |
| Time: | 04:42:58 | Log-Likelihood: | -100.06 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 19 | BIC: | 212.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 89.9870 | 53.058 | 1.696 | 0.106 | -21.065 201.039 |
| C(dose)[T.1] | 69.6458 | 70.633 | 0.986 | 0.337 | -78.190 217.482 |
| expression | -6.3077 | 9.295 | -0.679 | 0.506 | -25.762 13.147 |
| expression:C(dose)[T.1] | -3.3257 | 12.627 | -0.263 | 0.795 | -29.753 23.102 |
| Omnibus: | 0.034 | Durbin-Watson: | 2.080 |
| Prob(Omnibus): | 0.983 | Jarque-Bera (JB): | 0.247 |
| Skew: | -0.024 | Prob(JB): | 0.884 |
| Kurtosis: | 2.495 | Cond. No. | 125. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.677 |
| Model: | OLS | Adj. R-squared: | 0.645 |
| Method: | Least Squares | F-statistic: | 20.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.23e-05 |
| Time: | 04:42:58 | Log-Likelihood: | -100.10 |
| No. Observations: | 23 | AIC: | 206.2 |
| Df Residuals: | 20 | BIC: | 209.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 100.2095 | 35.326 | 2.837 | 0.010 | 26.521 173.898 |
| C(dose)[T.1] | 51.1859 | 8.567 | 5.975 | 0.000 | 33.315 69.057 |
| expression | -8.1099 | 6.143 | -1.320 | 0.202 | -20.924 4.704 |
| Omnibus: | 0.086 | Durbin-Watson: | 2.056 |
| Prob(Omnibus): | 0.958 | Jarque-Bera (JB): | 0.311 |
| Skew: | -0.014 | Prob(JB): | 0.856 |
| Kurtosis: | 2.431 | Cond. No. | 48.7 |
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: | 04:42:58 | 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: | 0.101 |
| Model: | OLS | Adj. R-squared: | 0.058 |
| Method: | Least Squares | F-statistic: | 2.361 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.139 |
| Time: | 04:42:58 | Log-Likelihood: | -111.88 |
| No. Observations: | 23 | AIC: | 227.8 |
| Df Residuals: | 21 | BIC: | 230.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 163.3970 | 54.891 | 2.977 | 0.007 | 49.245 277.549 |
| expression | -15.0901 | 9.821 | -1.536 | 0.139 | -35.515 5.335 |
| Omnibus: | 3.187 | Durbin-Watson: | 2.545 |
| Prob(Omnibus): | 0.203 | Jarque-Bera (JB): | 1.405 |
| Skew: | 0.180 | Prob(JB): | 0.495 |
| Kurtosis: | 1.844 | Cond. No. | 46.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.341 | 0.039 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.711 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 9.008 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00267 |
| Time: | 04:42:58 | Log-Likelihood: | -65.998 |
| No. Observations: | 15 | AIC: | 140.0 |
| Df Residuals: | 11 | BIC: | 142.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 137.3190 | 62.433 | 2.199 | 0.050 | -0.094 274.732 |
| C(dose)[T.1] | 268.9596 | 116.740 | 2.304 | 0.042 | 12.017 525.902 |
| expression | -11.3035 | 9.999 | -1.130 | 0.282 | -33.311 10.704 |
| expression:C(dose)[T.1] | -34.7086 | 18.543 | -1.872 | 0.088 | -75.521 6.104 |
| Omnibus: | 0.879 | Durbin-Watson: | 0.622 |
| Prob(Omnibus): | 0.644 | Jarque-Bera (JB): | 0.816 |
| Skew: | -0.402 | Prob(JB): | 0.665 |
| Kurtosis: | 2.189 | Cond. No. | 155. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.619 |
| Model: | OLS | Adj. R-squared: | 0.555 |
| Method: | Least Squares | F-statistic: | 9.730 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00308 |
| Time: | 04:42:58 | Log-Likelihood: | -68.072 |
| No. Observations: | 15 | AIC: | 142.1 |
| Df Residuals: | 12 | BIC: | 144.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 199.7191 | 58.033 | 3.441 | 0.005 | 73.276 326.162 |
| C(dose)[T.1] | 51.5941 | 13.134 | 3.928 | 0.002 | 22.977 80.211 |
| expression | -21.3955 | 9.257 | -2.311 | 0.039 | -41.566 -1.225 |
| Omnibus: | 4.899 | Durbin-Watson: | 1.190 |
| Prob(Omnibus): | 0.086 | Jarque-Bera (JB): | 1.738 |
| Skew: | -0.423 | Prob(JB): | 0.419 |
| Kurtosis: | 1.563 | Cond. No. | 57.4 |
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: | 04:42:58 | 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: | 0.128 |
| Model: | OLS | Adj. R-squared: | 0.061 |
| Method: | Least Squares | F-statistic: | 1.909 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.190 |
| Time: | 04:42:58 | Log-Likelihood: | -74.272 |
| No. Observations: | 15 | AIC: | 152.5 |
| Df Residuals: | 13 | BIC: | 154.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 209.3035 | 84.225 | 2.485 | 0.027 | 27.346 391.261 |
| expression | -18.5230 | 13.406 | -1.382 | 0.190 | -47.484 10.438 |
| Omnibus: | 1.635 | Durbin-Watson: | 2.304 |
| Prob(Omnibus): | 0.442 | Jarque-Bera (JB): | 0.871 |
| Skew: | 0.049 | Prob(JB): | 0.647 |
| Kurtosis: | 1.824 | Cond. No. | 57.2 |