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 |
| 4.382 | 0.049 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.734 |
| Model: | OLS | Adj. R-squared: | 0.692 |
| Method: | Least Squares | F-statistic: | 17.45 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.09e-05 |
| Time: | 04:40:46 | Log-Likelihood: | -97.887 |
| No. Observations: | 23 | AIC: | 203.8 |
| Df Residuals: | 19 | BIC: | 208.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 258.0013 | 93.904 | 2.748 | 0.013 | 61.459 454.544 |
| C(dose)[T.1] | -91.3103 | 107.261 | -0.851 | 0.405 | -315.809 133.189 |
| expression | -23.2289 | 10.686 | -2.174 | 0.043 | -45.594 -0.864 |
| expression:C(dose)[T.1] | 15.6180 | 12.576 | 1.242 | 0.229 | -10.703 41.939 |
| Omnibus: | 0.606 | Durbin-Watson: | 1.921 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.229 |
| Skew: | -0.244 | Prob(JB): | 0.892 |
| Kurtosis: | 2.964 | Cond. No. | 330. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.712 |
| Model: | OLS | Adj. R-squared: | 0.683 |
| Method: | Least Squares | F-statistic: | 24.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.91e-06 |
| Time: | 04:40:46 | Log-Likelihood: | -98.784 |
| No. Observations: | 23 | AIC: | 203.6 |
| Df Residuals: | 20 | BIC: | 207.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.0713 | 50.394 | 3.157 | 0.005 | 53.951 264.191 |
| C(dose)[T.1] | 41.3592 | 9.789 | 4.225 | 0.000 | 20.939 61.779 |
| expression | -11.9526 | 5.710 | -2.093 | 0.049 | -23.863 -0.042 |
| Omnibus: | 0.887 | Durbin-Watson: | 2.090 |
| Prob(Omnibus): | 0.642 | Jarque-Bera (JB): | 0.325 |
| Skew: | -0.289 | Prob(JB): | 0.850 |
| Kurtosis: | 3.066 | Cond. No. | 108. |
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: | 04:40:46 | 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: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.429 |
| Method: | Least Squares | F-statistic: | 17.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000414 |
| Time: | 04:40:46 | Log-Likelihood: | -106.12 |
| No. Observations: | 23 | AIC: | 216.2 |
| Df Residuals: | 21 | BIC: | 218.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 295.8037 | 51.861 | 5.704 | 0.000 | 187.953 403.654 |
| expression | -26.0534 | 6.220 | -4.189 | 0.000 | -38.988 -13.119 |
| Omnibus: | 2.461 | Durbin-Watson: | 2.322 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 1.751 |
| Skew: | 0.487 | Prob(JB): | 0.417 |
| Kurtosis: | 2.062 | Cond. No. | 82.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.540 | 0.477 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.507 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 3.776 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0438 |
| Time: | 04:40:46 | Log-Likelihood: | -69.990 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 290.3223 | 195.194 | 1.487 | 0.165 | -139.296 719.941 |
| C(dose)[T.1] | -174.0151 | 248.658 | -0.700 | 0.499 | -721.308 373.278 |
| expression | -27.1344 | 23.722 | -1.144 | 0.277 | -79.346 25.078 |
| expression:C(dose)[T.1] | 27.1750 | 30.794 | 0.882 | 0.396 | -40.603 94.953 |
| Omnibus: | 2.007 | Durbin-Watson: | 1.149 |
| Prob(Omnibus): | 0.367 | Jarque-Bera (JB): | 1.239 |
| Skew: | -0.693 | Prob(JB): | 0.538 |
| Kurtosis: | 2.750 | Cond. No. | 362. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.472 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 5.374 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0215 |
| Time: | 04:40:46 | Log-Likelihood: | -70.503 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 157.8533 | 123.613 | 1.277 | 0.226 | -111.476 427.182 |
| C(dose)[T.1] | 44.9283 | 16.457 | 2.730 | 0.018 | 9.071 80.785 |
| expression | -11.0080 | 14.986 | -0.735 | 0.477 | -43.659 21.643 |
| Omnibus: | 3.549 | Durbin-Watson: | 0.852 |
| Prob(Omnibus): | 0.170 | Jarque-Bera (JB): | 2.064 |
| Skew: | -0.909 | Prob(JB): | 0.356 |
| Kurtosis: | 3.011 | Cond. No. | 132. |
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: | 04:40:46 | 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: | 0.145 |
| Model: | OLS | Adj. R-squared: | 0.079 |
| Method: | Least Squares | F-statistic: | 2.202 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.162 |
| Time: | 04:40:46 | Log-Likelihood: | -74.126 |
| No. Observations: | 15 | AIC: | 152.3 |
| Df Residuals: | 13 | BIC: | 153.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 297.4816 | 137.661 | 2.161 | 0.050 | 0.083 594.880 |
| expression | -25.4526 | 17.151 | -1.484 | 0.162 | -62.505 11.600 |
| Omnibus: | 2.151 | Durbin-Watson: | 1.387 |
| Prob(Omnibus): | 0.341 | Jarque-Bera (JB): | 1.055 |
| Skew: | 0.219 | Prob(JB): | 0.590 |
| Kurtosis: | 1.777 | Cond. No. | 119. |