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 |
| 3.604 | 0.072 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.713 |
| Model: | OLS | Adj. R-squared: | 0.668 |
| Method: | Least Squares | F-statistic: | 15.75 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.19e-05 |
| Time: | 04:18:07 | Log-Likelihood: | -98.743 |
| No. Observations: | 23 | AIC: | 205.5 |
| Df Residuals: | 19 | BIC: | 210.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 82.5154 | 44.300 | 1.863 | 0.078 | -10.206 175.237 |
| C(dose)[T.1] | 105.3671 | 60.678 | 1.737 | 0.099 | -21.633 232.367 |
| expression | -6.0798 | 9.438 | -0.644 | 0.527 | -25.833 13.674 |
| expression:C(dose)[T.1] | -10.6137 | 12.721 | -0.834 | 0.414 | -37.238 16.011 |
| Omnibus: | 2.584 | Durbin-Watson: | 2.044 |
| Prob(Omnibus): | 0.275 | Jarque-Bera (JB): | 1.335 |
| Skew: | 0.230 | Prob(JB): | 0.513 |
| Kurtosis: | 1.913 | Cond. No. | 98.6 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.703 |
| Model: | OLS | Adj. R-squared: | 0.673 |
| Method: | Least Squares | F-statistic: | 23.63 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.40e-06 |
| Time: | 04:18:07 | Log-Likelihood: | -99.157 |
| No. Observations: | 23 | AIC: | 204.3 |
| Df Residuals: | 20 | BIC: | 207.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 109.7167 | 29.766 | 3.686 | 0.001 | 47.627 171.807 |
| C(dose)[T.1] | 55.2039 | 8.132 | 6.788 | 0.000 | 38.240 72.167 |
| expression | -11.9222 | 6.280 | -1.899 | 0.072 | -25.021 1.177 |
| Omnibus: | 4.358 | Durbin-Watson: | 1.967 |
| Prob(Omnibus): | 0.113 | Jarque-Bera (JB): | 1.584 |
| Skew: | 0.155 | Prob(JB): | 0.453 |
| Kurtosis: | 1.752 | Cond. No. | 36.9 |
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: | 04:18:07 | 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: | 0.018 |
| Model: | OLS | Adj. R-squared: | -0.029 |
| Method: | Least Squares | F-statistic: | 0.3746 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.547 |
| Time: | 04:18:07 | Log-Likelihood: | -112.90 |
| No. Observations: | 23 | AIC: | 229.8 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.7354 | 52.799 | 2.116 | 0.046 | 1.935 221.536 |
| expression | -6.7680 | 11.058 | -0.612 | 0.547 | -29.764 16.228 |
| Omnibus: | 2.998 | Durbin-Watson: | 2.546 |
| Prob(Omnibus): | 0.223 | Jarque-Bera (JB): | 1.423 |
| Skew: | 0.230 | Prob(JB): | 0.491 |
| Kurtosis: | 1.871 | Cond. No. | 36.8 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.516 | 0.242 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.524 |
| Model: | OLS | Adj. R-squared: | 0.394 |
| Method: | Least Squares | F-statistic: | 4.031 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0369 |
| Time: | 04:18:07 | Log-Likelihood: | -69.738 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 11 | BIC: | 150.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 58.6093 | 174.547 | 0.336 | 0.743 | -325.567 442.785 |
| C(dose)[T.1] | -37.4881 | 189.363 | -0.198 | 0.847 | -454.274 379.297 |
| expression | 1.4088 | 27.824 | 0.051 | 0.961 | -59.832 62.650 |
| expression:C(dose)[T.1] | 17.1392 | 31.200 | 0.549 | 0.594 | -51.532 85.810 |
| Omnibus: | 1.374 | Durbin-Watson: | 0.872 |
| Prob(Omnibus): | 0.503 | Jarque-Bera (JB): | 1.008 |
| Skew: | -0.384 | Prob(JB): | 0.604 |
| Kurtosis: | 1.988 | Cond. No. | 217. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.511 |
| Model: | OLS | Adj. R-squared: | 0.429 |
| Method: | Least Squares | F-statistic: | 6.260 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0137 |
| Time: | 04:18:07 | Log-Likelihood: | -69.941 |
| No. Observations: | 15 | AIC: | 145.9 |
| Df Residuals: | 12 | BIC: | 148.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -26.7259 | 77.244 | -0.346 | 0.735 | -195.025 141.573 |
| C(dose)[T.1] | 65.9106 | 20.107 | 3.278 | 0.007 | 22.102 109.720 |
| expression | 15.0398 | 12.217 | 1.231 | 0.242 | -11.578 41.658 |
| Omnibus: | 2.522 | Durbin-Watson: | 0.712 |
| Prob(Omnibus): | 0.283 | Jarque-Bera (JB): | 1.358 |
| Skew: | -0.428 | Prob(JB): | 0.507 |
| Kurtosis: | 1.800 | Cond. No. | 62.8 |
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: | 04:18:07 | 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: | 0.072 |
| Model: | OLS | Adj. R-squared: | 0.001 |
| Method: | Least Squares | F-statistic: | 1.014 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.332 |
| Time: | 04:18:07 | Log-Likelihood: | -74.737 |
| No. Observations: | 15 | AIC: | 153.5 |
| Df Residuals: | 13 | BIC: | 154.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 161.6827 | 68.260 | 2.369 | 0.034 | 14.216 309.149 |
| expression | -12.0008 | 11.919 | -1.007 | 0.332 | -37.751 13.749 |
| Omnibus: | 0.100 | Durbin-Watson: | 1.376 |
| Prob(Omnibus): | 0.951 | Jarque-Bera (JB): | 0.320 |
| Skew: | -0.083 | Prob(JB): | 0.852 |
| Kurtosis: | 2.305 | Cond. No. | 41.1 |