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 |
| 0.001 | 0.973 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.71 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000143 |
| Time: | 03:35:16 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 56.1963 | 54.263 | 1.036 | 0.313 | -57.377 169.769 |
| C(dose)[T.1] | 51.8881 | 78.130 | 0.664 | 0.515 | -111.640 215.416 |
| expression | -0.3786 | 10.266 | -0.037 | 0.971 | -21.866 21.109 |
| expression:C(dose)[T.1] | 0.2697 | 15.258 | 0.018 | 0.986 | -31.665 32.204 |
| Omnibus: | 0.294 | Durbin-Watson: | 1.884 |
| Prob(Omnibus): | 0.863 | Jarque-Bera (JB): | 0.468 |
| Skew: | 0.056 | Prob(JB): | 0.791 |
| Kurtosis: | 2.310 | Cond. No. | 118. |
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: | 18.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.83e-05 |
| Time: | 03:35:16 | 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 | 55.5552 | 39.338 | 1.412 | 0.173 | -26.503 137.613 |
| C(dose)[T.1] | 53.2592 | 9.053 | 5.883 | 0.000 | 34.374 72.144 |
| expression | -0.2565 | 7.402 | -0.035 | 0.973 | -15.698 15.185 |
| Omnibus: | 0.279 | Durbin-Watson: | 1.882 |
| Prob(Omnibus): | 0.870 | Jarque-Bera (JB): | 0.459 |
| Skew: | 0.053 | Prob(JB): | 0.795 |
| Kurtosis: | 2.316 | Cond. No. | 48.2 |
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: | 03:35:16 | 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: | 0.042 |
| Model: | OLS | Adj. R-squared: | -0.004 |
| Method: | Least Squares | F-statistic: | 0.9176 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.349 |
| Time: | 03:35:16 | Log-Likelihood: | -112.61 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 136.2640 | 59.452 | 2.292 | 0.032 | 12.627 259.901 |
| expression | -11.0759 | 11.562 | -0.958 | 0.349 | -35.121 12.969 |
| Omnibus: | 3.757 | Durbin-Watson: | 2.384 |
| Prob(Omnibus): | 0.153 | Jarque-Bera (JB): | 1.525 |
| Skew: | 0.195 | Prob(JB): | 0.466 |
| Kurtosis: | 1.800 | Cond. No. | 44.9 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.384 | 0.547 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.518 |
| Model: | OLS | Adj. R-squared: | 0.387 |
| Method: | Least Squares | F-statistic: | 3.946 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0390 |
| Time: | 03:35:16 | Log-Likelihood: | -69.821 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 11 | BIC: | 150.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.8735 | 50.990 | 1.312 | 0.216 | -45.354 179.101 |
| C(dose)[T.1] | 146.3604 | 92.648 | 1.580 | 0.142 | -57.556 350.277 |
| expression | 0.0991 | 8.878 | 0.011 | 0.991 | -19.441 19.639 |
| expression:C(dose)[T.1] | -19.3249 | 17.647 | -1.095 | 0.297 | -58.166 19.516 |
| Omnibus: | 8.892 | Durbin-Watson: | 0.919 |
| Prob(Omnibus): | 0.012 | Jarque-Bera (JB): | 5.358 |
| Skew: | -1.346 | Prob(JB): | 0.0686 |
| Kurtosis: | 4.149 | Cond. No. | 80.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.466 |
| Model: | OLS | Adj. R-squared: | 0.377 |
| Method: | Least Squares | F-statistic: | 5.233 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0232 |
| Time: | 03:35:16 | Log-Likelihood: | -70.597 |
| No. Observations: | 15 | AIC: | 147.2 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 94.2746 | 44.795 | 2.105 | 0.057 | -3.326 191.875 |
| C(dose)[T.1] | 46.4279 | 16.126 | 2.879 | 0.014 | 11.293 81.563 |
| expression | -4.7916 | 7.736 | -0.619 | 0.547 | -21.647 12.064 |
| Omnibus: | 3.065 | Durbin-Watson: | 0.811 |
| Prob(Omnibus): | 0.216 | Jarque-Bera (JB): | 1.994 |
| Skew: | -0.884 | Prob(JB): | 0.369 |
| Kurtosis: | 2.753 | Cond. No. | 32.8 |
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: | 03:35:16 | 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: | 0.097 |
| Model: | OLS | Adj. R-squared: | 0.027 |
| Method: | Least Squares | F-statistic: | 1.394 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.259 |
| Time: | 03:35:16 | Log-Likelihood: | -74.536 |
| No. Observations: | 15 | AIC: | 153.1 |
| Df Residuals: | 13 | BIC: | 154.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 151.7230 | 50.103 | 3.028 | 0.010 | 43.481 259.965 |
| expression | -10.9652 | 9.286 | -1.181 | 0.259 | -31.026 9.095 |
| Omnibus: | 2.391 | Durbin-Watson: | 1.352 |
| Prob(Omnibus): | 0.303 | Jarque-Bera (JB): | 1.095 |
| Skew: | -0.209 | Prob(JB): | 0.579 |
| Kurtosis: | 1.744 | Cond. No. | 28.9 |