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 |
| 1.340 | 0.261 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.96 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.68e-05 |
| Time: | 05:04:03 | Log-Likelihood: | -100.29 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 199.1921 | 139.743 | 1.425 | 0.170 | -93.293 491.677 |
| C(dose)[T.1] | -1.4988 | 232.523 | -0.006 | 0.995 | -488.175 485.177 |
| expression | -20.3641 | 19.610 | -1.038 | 0.312 | -61.408 20.680 |
| expression:C(dose)[T.1] | 6.7087 | 34.295 | 0.196 | 0.847 | -65.072 78.489 |
| Omnibus: | 1.152 | Durbin-Watson: | 2.039 |
| Prob(Omnibus): | 0.562 | Jarque-Bera (JB): | 0.876 |
| Skew: | -0.157 | Prob(JB): | 0.645 |
| Kurtosis: | 2.097 | Cond. No. | 452. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.671 |
| Model: | OLS | Adj. R-squared: | 0.638 |
| Method: | Least Squares | F-statistic: | 20.40 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.48e-05 |
| Time: | 05:04:03 | Log-Likelihood: | -100.32 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 20 | BIC: | 210.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 183.5761 | 111.905 | 1.640 | 0.117 | -49.853 417.005 |
| C(dose)[T.1] | 43.9250 | 11.755 | 3.737 | 0.001 | 19.404 68.446 |
| expression | -18.1707 | 15.696 | -1.158 | 0.261 | -50.913 14.571 |
| Omnibus: | 1.189 | Durbin-Watson: | 2.048 |
| Prob(Omnibus): | 0.552 | Jarque-Bera (JB): | 0.876 |
| Skew: | -0.135 | Prob(JB): | 0.645 |
| Kurtosis: | 2.083 | Cond. No. | 186. |
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: | 05:04:03 | 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: | 0.441 |
| Model: | OLS | Adj. R-squared: | 0.415 |
| Method: | Least Squares | F-statistic: | 16.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000544 |
| Time: | 05:04:03 | Log-Likelihood: | -106.41 |
| No. Observations: | 23 | AIC: | 216.8 |
| Df Residuals: | 21 | BIC: | 219.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 483.3469 | 99.216 | 4.872 | 0.000 | 277.015 689.679 |
| expression | -58.7368 | 14.417 | -4.074 | 0.001 | -88.718 -28.755 |
| Omnibus: | 0.619 | Durbin-Watson: | 2.074 |
| Prob(Omnibus): | 0.734 | Jarque-Bera (JB): | 0.666 |
| Skew: | 0.170 | Prob(JB): | 0.717 |
| Kurtosis: | 2.239 | Cond. No. | 129. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.263 | 0.158 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.560 |
| Model: | OLS | Adj. R-squared: | 0.439 |
| Method: | Least Squares | F-statistic: | 4.657 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0246 |
| Time: | 05:04:03 | Log-Likelihood: | -69.151 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.1839 | 244.051 | 0.419 | 0.683 | -434.968 639.336 |
| C(dose)[T.1] | 255.7888 | 284.399 | 0.899 | 0.388 | -370.168 881.746 |
| expression | -5.4199 | 38.022 | -0.143 | 0.889 | -89.105 78.265 |
| expression:C(dose)[T.1] | -34.2636 | 44.938 | -0.762 | 0.462 | -133.170 64.643 |
| Omnibus: | 1.597 | Durbin-Watson: | 0.642 |
| Prob(Omnibus): | 0.450 | Jarque-Bera (JB): | 1.153 |
| Skew: | -0.455 | Prob(JB): | 0.562 |
| Kurtosis: | 1.991 | Cond. No. | 363. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.536 |
| Model: | OLS | Adj. R-squared: | 0.459 |
| Method: | Least Squares | F-statistic: | 6.937 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00995 |
| Time: | 05:04:03 | Log-Likelihood: | -69.537 |
| No. Observations: | 15 | AIC: | 145.1 |
| Df Residuals: | 12 | BIC: | 147.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 259.4760 | 128.105 | 2.025 | 0.066 | -19.642 538.594 |
| C(dose)[T.1] | 39.2928 | 15.868 | 2.476 | 0.029 | 4.720 73.865 |
| expression | -29.9489 | 19.910 | -1.504 | 0.158 | -73.328 13.431 |
| Omnibus: | 2.851 | Durbin-Watson: | 0.556 |
| Prob(Omnibus): | 0.240 | Jarque-Bera (JB): | 1.515 |
| Skew: | -0.485 | Prob(JB): | 0.469 |
| Kurtosis: | 1.783 | Cond. No. | 114. |
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: | 05:04:03 | 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: | 0.299 |
| Model: | OLS | Adj. R-squared: | 0.245 |
| Method: | Least Squares | F-statistic: | 5.551 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0348 |
| Time: | 05:04:03 | Log-Likelihood: | -72.633 |
| No. Observations: | 15 | AIC: | 149.3 |
| Df Residuals: | 13 | BIC: | 150.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 408.0021 | 133.686 | 3.052 | 0.009 | 119.192 696.812 |
| expression | -50.4055 | 21.394 | -2.356 | 0.035 | -96.624 -4.187 |
| Omnibus: | 0.340 | Durbin-Watson: | 1.220 |
| Prob(Omnibus): | 0.844 | Jarque-Bera (JB): | 0.479 |
| Skew: | 0.149 | Prob(JB): | 0.787 |
| Kurtosis: | 2.176 | Cond. No. | 101. |