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 |
| 0.132 | 0.720 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.695 |
| Model: | OLS | Adj. R-squared: | 0.647 |
| Method: | Least Squares | F-statistic: | 14.44 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.87e-05 |
| Time: | 05:10:34 | Log-Likelihood: | -99.446 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 19 | BIC: | 211.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 215.8569 | 167.374 | 1.290 | 0.213 | -134.461 566.175 |
| C(dose)[T.1] | -320.8588 | 226.731 | -1.415 | 0.173 | -795.413 153.695 |
| expression | -18.3165 | 18.954 | -0.966 | 0.346 | -57.988 21.354 |
| expression:C(dose)[T.1] | 42.2858 | 25.618 | 1.651 | 0.115 | -11.333 95.905 |
| Omnibus: | 0.022 | Durbin-Watson: | 1.455 |
| Prob(Omnibus): | 0.989 | Jarque-Bera (JB): | 0.225 |
| Skew: | -0.025 | Prob(JB): | 0.894 |
| Kurtosis: | 2.518 | Cond. No. | 642. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.68 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.65e-05 |
| Time: | 05:10:34 | Log-Likelihood: | -100.99 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 11.5751 | 117.441 | 0.099 | 0.922 | -233.403 256.553 |
| C(dose)[T.1] | 53.1333 | 8.759 | 6.066 | 0.000 | 34.862 71.404 |
| expression | 4.8308 | 13.290 | 0.363 | 0.720 | -22.891 32.553 |
| Omnibus: | 0.294 | Durbin-Watson: | 1.904 |
| Prob(Omnibus): | 0.863 | Jarque-Bera (JB): | 0.467 |
| Skew: | 0.036 | Prob(JB): | 0.792 |
| Kurtosis: | 2.305 | Cond. No. | 241. |
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: | 05:10:34 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.037 |
| Method: | Least Squares | F-statistic: | 0.2099 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.652 |
| Time: | 05:10:34 | Log-Likelihood: | -112.99 |
| 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 | -8.6737 | 193.064 | -0.045 | 0.965 | -410.173 392.825 |
| expression | 9.9928 | 21.811 | 0.458 | 0.652 | -35.366 55.352 |
| Omnibus: | 3.045 | Durbin-Watson: | 2.473 |
| Prob(Omnibus): | 0.218 | Jarque-Bera (JB): | 1.373 |
| Skew: | 0.176 | Prob(JB): | 0.503 |
| Kurtosis: | 1.856 | Cond. No. | 241. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.299 | 0.595 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.316 |
| Method: | Least Squares | F-statistic: | 3.156 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0683 |
| Time: | 05:10:34 | Log-Likelihood: | -70.642 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 11 | BIC: | 152.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -25.7794 | 183.459 | -0.141 | 0.891 | -429.571 378.012 |
| C(dose)[T.1] | 89.1331 | 390.091 | 0.228 | 0.823 | -769.452 947.719 |
| expression | 11.6679 | 22.918 | 0.509 | 0.621 | -38.774 62.109 |
| expression:C(dose)[T.1] | -4.8119 | 49.862 | -0.097 | 0.925 | -114.557 104.934 |
| Omnibus: | 2.211 | Durbin-Watson: | 0.776 |
| Prob(Omnibus): | 0.331 | Jarque-Bera (JB): | 1.536 |
| Skew: | -0.756 | Prob(JB): | 0.464 |
| Kurtosis: | 2.588 | Cond. No. | 458. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.462 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 5.156 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0242 |
| Time: | 05:10:34 | Log-Likelihood: | -70.649 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -17.6590 | 156.150 | -0.113 | 0.912 | -357.880 322.562 |
| C(dose)[T.1] | 51.5229 | 16.120 | 3.196 | 0.008 | 16.400 86.645 |
| expression | 10.6514 | 19.495 | 0.546 | 0.595 | -31.825 53.128 |
| Omnibus: | 2.235 | Durbin-Watson: | 0.775 |
| Prob(Omnibus): | 0.327 | Jarque-Bera (JB): | 1.505 |
| Skew: | -0.755 | Prob(JB): | 0.471 |
| Kurtosis: | 2.641 | Cond. No. | 162. |
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: | 05:10:34 | 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: | 0.004 |
| Model: | OLS | Adj. R-squared: | -0.072 |
| Method: | Least Squares | F-statistic: | 0.05583 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.817 |
| Time: | 05:10:34 | Log-Likelihood: | -75.268 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 139.3871 | 193.757 | 0.719 | 0.485 | -279.201 557.975 |
| expression | -5.8080 | 24.580 | -0.236 | 0.817 | -58.910 47.294 |
| Omnibus: | 0.329 | Durbin-Watson: | 1.549 |
| Prob(Omnibus): | 0.848 | Jarque-Bera (JB): | 0.465 |
| Skew: | -0.015 | Prob(JB): | 0.793 |
| Kurtosis: | 2.138 | Cond. No. | 153. |