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 |
| 0.245 | 0.626 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.681 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.94e-05 |
| Time: | 04:55:27 | Log-Likelihood: | -99.977 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 137.4762 | 119.556 | 1.150 | 0.264 | -112.757 387.710 |
| C(dose)[T.1] | -139.1049 | 151.257 | -0.920 | 0.369 | -455.689 177.479 |
| expression | -12.2276 | 17.535 | -0.697 | 0.494 | -48.928 24.473 |
| expression:C(dose)[T.1] | 28.3781 | 22.237 | 1.276 | 0.217 | -18.165 74.921 |
| Omnibus: | 1.639 | Durbin-Watson: | 1.715 |
| Prob(Omnibus): | 0.441 | Jarque-Bera (JB): | 0.990 |
| Skew: | -0.090 | Prob(JB): | 0.610 |
| Kurtosis: | 2.000 | Cond. No. | 335. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.84 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.51e-05 |
| Time: | 04:55:27 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 17.3189 | 74.825 | 0.231 | 0.819 | -138.763 173.401 |
| C(dose)[T.1] | 53.6082 | 8.734 | 6.138 | 0.000 | 35.390 71.827 |
| expression | 5.4171 | 10.952 | 0.495 | 0.626 | -17.428 28.263 |
| Omnibus: | 0.321 | Durbin-Watson: | 1.760 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.488 |
| Skew: | 0.107 | Prob(JB): | 0.783 |
| Kurtosis: | 2.319 | Cond. No. | 120. |
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: | 04:55:27 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.047 |
| Method: | Least Squares | F-statistic: | 0.004380 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.948 |
| Time: | 04:55:27 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 71.5819 | 123.133 | 0.581 | 0.567 | -184.487 327.651 |
| expression | 1.1989 | 18.114 | 0.066 | 0.948 | -36.472 38.870 |
| Omnibus: | 3.434 | Durbin-Watson: | 2.481 |
| Prob(Omnibus): | 0.180 | Jarque-Bera (JB): | 1.580 |
| Skew: | 0.279 | Prob(JB): | 0.454 |
| Kurtosis: | 1.844 | Cond. No. | 118. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.132 | 0.723 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.589 |
| Model: | OLS | Adj. R-squared: | 0.476 |
| Method: | Least Squares | F-statistic: | 5.248 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0172 |
| Time: | 04:55:27 | Log-Likelihood: | -68.637 |
| No. Observations: | 15 | AIC: | 145.3 |
| Df Residuals: | 11 | BIC: | 148.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 364.7622 | 372.940 | 0.978 | 0.349 | -456.074 1185.598 |
| C(dose)[T.1] | -1179.9318 | 647.144 | -1.823 | 0.096 | -2604.287 244.423 |
| expression | -36.1957 | 45.382 | -0.798 | 0.442 | -136.081 63.690 |
| expression:C(dose)[T.1] | 145.0478 | 76.651 | 1.892 | 0.085 | -23.661 313.756 |
| Omnibus: | 8.826 | Durbin-Watson: | 0.888 |
| Prob(Omnibus): | 0.012 | Jarque-Bera (JB): | 5.242 |
| Skew: | -1.307 | Prob(JB): | 0.0727 |
| Kurtosis: | 4.246 | Cond. No. | 973. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.455 |
| Model: | OLS | Adj. R-squared: | 0.364 |
| Method: | Least Squares | F-statistic: | 5.005 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0263 |
| Time: | 04:55:27 | Log-Likelihood: | -70.751 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -52.9022 | 331.367 | -0.160 | 0.876 | -774.888 669.084 |
| C(dose)[T.1] | 44.1342 | 20.956 | 2.106 | 0.057 | -1.524 89.793 |
| expression | 14.6484 | 40.315 | 0.363 | 0.723 | -73.190 102.487 |
| Omnibus: | 2.894 | Durbin-Watson: | 0.779 |
| Prob(Omnibus): | 0.235 | Jarque-Bera (JB): | 1.924 |
| Skew: | -0.864 | Prob(JB): | 0.382 |
| Kurtosis: | 2.698 | Cond. No. | 363. |
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: | 04:55:27 | 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: | 0.253 |
| Model: | OLS | Adj. R-squared: | 0.196 |
| Method: | Least Squares | F-statistic: | 4.409 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0558 |
| Time: | 04:55:27 | Log-Likelihood: | -73.110 |
| No. Observations: | 15 | AIC: | 150.2 |
| Df Residuals: | 13 | BIC: | 151.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -503.4658 | 284.532 | -1.769 | 0.100 | -1118.160 111.228 |
| expression | 71.0964 | 33.861 | 2.100 | 0.056 | -2.056 144.249 |
| Omnibus: | 1.414 | Durbin-Watson: | 1.223 |
| Prob(Omnibus): | 0.493 | Jarque-Bera (JB): | 1.150 |
| Skew: | -0.526 | Prob(JB): | 0.563 |
| Kurtosis: | 2.143 | Cond. No. | 276. |