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 |
| 0.587 | 0.453 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.44 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 9.88e-05 |
| Time: | 22:54:32 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 80.9071 | 246.387 | 0.328 | 0.746 | -434.787 596.602 |
| C(dose)[T.1] | 193.9974 | 312.569 | 0.621 | 0.542 | -460.217 848.211 |
| expression | -2.9528 | 27.241 | -0.108 | 0.915 | -59.969 54.063 |
| expression:C(dose)[T.1] | -15.7647 | 34.703 | -0.454 | 0.655 | -88.398 56.869 |
| Omnibus: | 0.627 | Durbin-Watson: | 1.673 |
| Prob(Omnibus): | 0.731 | Jarque-Bera (JB): | 0.662 |
| Skew: | 0.147 | Prob(JB): | 0.718 |
| Kurtosis: | 2.223 | Cond. No. | 883. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.659 |
| Model: | OLS | Adj. R-squared: | 0.625 |
| Method: | Least Squares | F-statistic: | 19.33 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.12e-05 |
| Time: | 22:54:32 | Log-Likelihood: | -100.73 |
| No. Observations: | 23 | AIC: | 207.5 |
| Df Residuals: | 20 | BIC: | 210.9 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 168.7422 | 149.656 | 1.128 | 0.273 | -143.434 480.918 |
| C(dose)[T.1] | 52.0627 | 8.803 | 5.914 | 0.000 | 33.701 70.425 |
| expression | -12.6669 | 16.538 | -0.766 | 0.453 | -47.165 21.831 |
| Omnibus: | 0.202 | Durbin-Watson: | 1.691 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.404 |
| Skew: | 0.092 | Prob(JB): | 0.817 |
| Kurtosis: | 2.377 | Cond. No. | 316. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 22:54:33 | 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: | 0.063 |
| Model: | OLS | Adj. R-squared: | 0.018 |
| Method: | Least Squares | F-statistic: | 1.406 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.249 |
| Time: | 22:54:33 | Log-Likelihood: | -112.36 |
| No. Observations: | 23 | AIC: | 228.7 |
| Df Residuals: | 21 | BIC: | 231.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 359.9318 | 236.435 | 1.522 | 0.143 | -131.761 851.624 |
| expression | -31.1562 | 26.277 | -1.186 | 0.249 | -85.802 23.490 |
| Omnibus: | 1.260 | Durbin-Watson: | 2.228 |
| Prob(Omnibus): | 0.533 | Jarque-Bera (JB): | 0.968 |
| Skew: | 0.230 | Prob(JB): | 0.616 |
| Kurtosis: | 2.107 | Cond. No. | 308. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.058 | 0.813 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.452 |
| Model: | OLS | Adj. R-squared: | 0.302 |
| Method: | Least Squares | F-statistic: | 3.022 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0756 |
| Time: | 22:54:33 | Log-Likelihood: | -70.792 |
| No. Observations: | 15 | AIC: | 149.6 |
| Df Residuals: | 11 | BIC: | 152.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 102.5860 | 454.880 | 0.226 | 0.826 | -898.597 1103.769 |
| C(dose)[T.1] | 102.6512 | 592.625 | 0.173 | 0.866 | -1201.708 1407.011 |
| expression | -4.2018 | 54.345 | -0.077 | 0.940 | -123.814 115.411 |
| expression:C(dose)[T.1] | -5.7192 | 68.996 | -0.083 | 0.935 | -157.578 146.140 |
| Omnibus: | 2.690 | Durbin-Watson: | 0.816 |
| Prob(Omnibus): | 0.261 | Jarque-Bera (JB): | 1.901 |
| Skew: | -0.844 | Prob(JB): | 0.387 |
| Kurtosis: | 2.563 | Cond. No. | 894. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.360 |
| Method: | Least Squares | F-statistic: | 4.938 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0272 |
| Time: | 22:54:33 | Log-Likelihood: | -70.797 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 132.2749 | 268.564 | 0.493 | 0.631 | -452.876 717.426 |
| C(dose)[T.1] | 53.5711 | 23.962 | 2.236 | 0.045 | 1.361 105.781 |
| expression | -7.7499 | 32.067 | -0.242 | 0.813 | -77.619 62.119 |
| Omnibus: | 2.612 | Durbin-Watson: | 0.826 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.833 |
| Skew: | -0.830 | Prob(JB): | 0.400 |
| Kurtosis: | 2.577 | Cond. No. | 303. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 22:54:33 | 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: | 0.223 |
| Model: | OLS | Adj. R-squared: | 0.163 |
| Method: | Least Squares | F-statistic: | 3.730 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0755 |
| Time: | 22:54:33 | Log-Likelihood: | -73.408 |
| No. Observations: | 15 | AIC: | 150.8 |
| Df Residuals: | 13 | BIC: | 152.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -308.5991 | 208.469 | -1.480 | 0.163 | -758.968 141.770 |
| expression | 46.4060 | 24.027 | 1.931 | 0.076 | -5.501 98.313 |
| Omnibus: | 0.060 | Durbin-Watson: | 1.019 |
| Prob(Omnibus): | 0.971 | Jarque-Bera (JB): | 0.132 |
| Skew: | -0.091 | Prob(JB): | 0.936 |
| Kurtosis: | 2.578 | Cond. No. | 205. |