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 |
| 0.071 | 0.793 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000116 |
| Time: | 04:44:16 | Log-Likelihood: | -100.81 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -4.3890 | 452.481 | -0.010 | 0.992 | -951.443 942.665 |
| C(dose)[T.1] | 531.0075 | 796.298 | 0.667 | 0.513 | -1135.663 2197.678 |
| expression | 4.7801 | 36.908 | 0.130 | 0.898 | -72.470 82.030 |
| expression:C(dose)[T.1] | -38.3344 | 64.144 | -0.598 | 0.557 | -172.589 95.921 |
| Omnibus: | 0.058 | Durbin-Watson: | 1.787 |
| Prob(Omnibus): | 0.972 | Jarque-Bera (JB): | 0.279 |
| Skew: | -0.024 | Prob(JB): | 0.870 |
| Kurtosis: | 2.462 | Cond. No. | 2.70e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.74e-05 |
| Time: | 04:44:16 | Log-Likelihood: | -101.02 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 151.1931 | 364.093 | 0.415 | 0.682 | -608.293 910.679 |
| C(dose)[T.1] | 55.1641 | 11.121 | 4.960 | 0.000 | 31.967 78.362 |
| expression | -7.9116 | 29.697 | -0.266 | 0.793 | -69.859 54.036 |
| Omnibus: | 0.449 | Durbin-Watson: | 1.856 |
| Prob(Omnibus): | 0.799 | Jarque-Bera (JB): | 0.551 |
| Skew: | 0.018 | Prob(JB): | 0.759 |
| Kurtosis: | 2.242 | Cond. No. | 1.04e+03 |
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: | 04:44:16 | 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: | 0.220 |
| Model: | OLS | Adj. R-squared: | 0.183 |
| Method: | Least Squares | F-statistic: | 5.925 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0239 |
| Time: | 04:44:16 | Log-Likelihood: | -110.25 |
| No. Observations: | 23 | AIC: | 224.5 |
| Df Residuals: | 21 | BIC: | 226.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -946.0870 | 421.481 | -2.245 | 0.036 | -1822.605 -69.569 |
| expression | 82.9340 | 34.072 | 2.434 | 0.024 | 12.078 153.790 |
| Omnibus: | 1.197 | Durbin-Watson: | 2.443 |
| Prob(Omnibus): | 0.550 | Jarque-Bera (JB): | 1.117 |
| Skew: | 0.434 | Prob(JB): | 0.572 |
| Kurtosis: | 2.357 | Cond. No. | 823. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.313 | 0.586 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.540 |
| Model: | OLS | Adj. R-squared: | 0.414 |
| Method: | Least Squares | F-statistic: | 4.299 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0309 |
| Time: | 04:44:16 | Log-Likelihood: | -69.481 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 11 | BIC: | 149.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 592.3935 | 403.329 | 1.469 | 0.170 | -295.328 1480.115 |
| C(dose)[T.1] | -826.7067 | 648.544 | -1.275 | 0.229 | -2254.143 600.729 |
| expression | -46.4801 | 35.697 | -1.302 | 0.220 | -125.049 32.089 |
| expression:C(dose)[T.1] | 77.0376 | 56.825 | 1.356 | 0.202 | -48.034 202.109 |
| Omnibus: | 0.935 | Durbin-Watson: | 1.204 |
| Prob(Omnibus): | 0.627 | Jarque-Bera (JB): | 0.752 |
| Skew: | -0.484 | Prob(JB): | 0.686 |
| Kurtosis: | 2.483 | Cond. No. | 1.26e+03 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 5.169 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0240 |
| Time: | 04:44:16 | Log-Likelihood: | -70.640 |
| 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 | 249.0309 | 324.663 | 0.767 | 0.458 | -458.348 956.410 |
| C(dose)[T.1] | 52.2533 | 16.470 | 3.173 | 0.008 | 16.368 88.139 |
| expression | -16.0790 | 28.728 | -0.560 | 0.586 | -78.672 46.514 |
| Omnibus: | 4.224 | Durbin-Watson: | 0.793 |
| Prob(Omnibus): | 0.121 | Jarque-Bera (JB): | 2.565 |
| Skew: | -1.013 | Prob(JB): | 0.277 |
| Kurtosis: | 3.025 | Cond. No. | 482. |
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: | 04:44:16 | 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: | 0.012 |
| Model: | OLS | Adj. R-squared: | -0.064 |
| Method: | Least Squares | F-statistic: | 0.1605 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.695 |
| Time: | 04:44:16 | Log-Likelihood: | -75.208 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -67.5276 | 402.507 | -0.168 | 0.869 | -937.091 802.036 |
| expression | 14.1451 | 35.310 | 0.401 | 0.695 | -62.137 90.427 |
| Omnibus: | 0.150 | Durbin-Watson: | 1.503 |
| Prob(Omnibus): | 0.928 | Jarque-Bera (JB): | 0.362 |
| Skew: | -0.060 | Prob(JB): | 0.834 |
| Kurtosis: | 2.248 | Cond. No. | 458. |