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 |
| 0.180 | 0.676 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.597 |
| Method: | Least Squares | F-statistic: | 11.88 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000131 |
| Time: | 03:36:25 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 101.0400 | 134.831 | 0.749 | 0.463 | -181.165 383.245 |
| C(dose)[T.1] | 54.2614 | 252.699 | 0.215 | 0.832 | -474.644 583.166 |
| expression | -6.1058 | 17.560 | -0.348 | 0.732 | -42.860 30.649 |
| expression:C(dose)[T.1] | -0.1966 | 33.214 | -0.006 | 0.995 | -69.714 69.321 |
| Omnibus: | 0.456 | Durbin-Watson: | 1.831 |
| Prob(Omnibus): | 0.796 | Jarque-Bera (JB): | 0.557 |
| Skew: | 0.046 | Prob(JB): | 0.757 |
| Kurtosis: | 2.243 | Cond. No. | 519. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.652 |
| Model: | OLS | Adj. R-squared: | 0.617 |
| Method: | Least Squares | F-statistic: | 18.75 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.59e-05 |
| Time: | 03:36:26 | Log-Likelihood: | -100.96 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 101.4614 | 111.593 | 0.909 | 0.374 | -131.318 334.241 |
| C(dose)[T.1] | 52.7669 | 8.834 | 5.973 | 0.000 | 34.340 71.194 |
| expression | -6.1607 | 14.528 | -0.424 | 0.676 | -36.466 24.144 |
| Omnibus: | 0.455 | Durbin-Watson: | 1.830 |
| Prob(Omnibus): | 0.796 | Jarque-Bera (JB): | 0.556 |
| Skew: | 0.044 | Prob(JB): | 0.757 |
| Kurtosis: | 2.243 | Cond. No. | 199. |
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: | 03:36:26 | 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: | 0.032 |
| Model: | OLS | Adj. R-squared: | -0.014 |
| Method: | Least Squares | F-statistic: | 0.6863 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.417 |
| Time: | 03:36:26 | Log-Likelihood: | -112.73 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 227.4279 | 178.439 | 1.275 | 0.216 | -143.656 598.512 |
| expression | -19.3699 | 23.381 | -0.828 | 0.417 | -67.993 29.253 |
| Omnibus: | 1.726 | Durbin-Watson: | 2.492 |
| Prob(Omnibus): | 0.422 | Jarque-Bera (JB): | 1.156 |
| Skew: | 0.266 | Prob(JB): | 0.561 |
| Kurtosis: | 2.039 | Cond. No. | 195. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.053 | 0.325 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.643 |
| Model: | OLS | Adj. R-squared: | 0.546 |
| Method: | Least Squares | F-statistic: | 6.607 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00815 |
| Time: | 03:36:26 | Log-Likelihood: | -67.573 |
| No. Observations: | 15 | AIC: | 143.1 |
| Df Residuals: | 11 | BIC: | 146.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 293.7552 | 278.889 | 1.053 | 0.315 | -320.076 907.587 |
| C(dose)[T.1] | -787.7839 | 384.467 | -2.049 | 0.065 | -1633.991 58.423 |
| expression | -27.5721 | 33.955 | -0.812 | 0.434 | -102.307 47.163 |
| expression:C(dose)[T.1] | 98.2654 | 45.722 | 2.149 | 0.055 | -2.367 198.898 |
| Omnibus: | 0.883 | Durbin-Watson: | 0.961 |
| Prob(Omnibus): | 0.643 | Jarque-Bera (JB): | 0.689 |
| Skew: | -0.468 | Prob(JB): | 0.709 |
| Kurtosis: | 2.524 | Cond. No. | 679. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 5.840 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0169 |
| Time: | 03:36:26 | Log-Likelihood: | -70.202 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 12 | BIC: | 148.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -151.1144 | 213.235 | -0.709 | 0.492 | -615.713 313.485 |
| C(dose)[T.1] | 37.7612 | 18.759 | 2.013 | 0.067 | -3.111 78.634 |
| expression | 26.6238 | 25.942 | 1.026 | 0.325 | -29.900 83.148 |
| Omnibus: | 2.014 | Durbin-Watson: | 0.484 |
| Prob(Omnibus): | 0.365 | Jarque-Bera (JB): | 1.487 |
| Skew: | -0.607 | Prob(JB): | 0.475 |
| Kurtosis: | 2.047 | Cond. No. | 243. |
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: | 03:36:26 | 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: | 0.322 |
| Model: | OLS | Adj. R-squared: | 0.270 |
| Method: | Least Squares | F-statistic: | 6.178 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0273 |
| Time: | 03:36:26 | Log-Likelihood: | -72.384 |
| No. Observations: | 15 | AIC: | 148.8 |
| Df Residuals: | 13 | BIC: | 150.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -392.6957 | 195.855 | -2.005 | 0.066 | -815.815 30.424 |
| expression | 57.6421 | 23.191 | 2.486 | 0.027 | 7.541 107.743 |
| Omnibus: | 1.204 | Durbin-Watson: | 0.866 |
| Prob(Omnibus): | 0.548 | Jarque-Bera (JB): | 0.934 |
| Skew: | -0.366 | Prob(JB): | 0.627 |
| Kurtosis: | 2.020 | Cond. No. | 200. |