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 |
| 3.282 | 0.085 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.739 |
| Model: | OLS | Adj. R-squared: | 0.698 |
| Method: | Least Squares | F-statistic: | 17.95 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.99e-06 |
| Time: | 04:10:17 | Log-Likelihood: | -97.648 |
| No. Observations: | 23 | AIC: | 203.3 |
| Df Residuals: | 19 | BIC: | 207.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.7084 | 107.158 | 1.070 | 0.298 | -109.576 338.993 |
| C(dose)[T.1] | -166.2562 | 124.756 | -1.333 | 0.198 | -427.374 94.861 |
| expression | -10.5763 | 18.709 | -0.565 | 0.578 | -49.735 28.583 |
| expression:C(dose)[T.1] | 37.0050 | 21.490 | 1.722 | 0.101 | -7.973 81.983 |
| Omnibus: | 1.514 | Durbin-Watson: | 2.075 |
| Prob(Omnibus): | 0.469 | Jarque-Bera (JB): | 1.057 |
| Skew: | 0.516 | Prob(JB): | 0.589 |
| Kurtosis: | 2.810 | Cond. No. | 284. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.699 |
| Model: | OLS | Adj. R-squared: | 0.668 |
| Method: | Least Squares | F-statistic: | 23.17 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.20e-06 |
| Time: | 04:10:17 | Log-Likelihood: | -99.316 |
| No. Observations: | 23 | AIC: | 204.6 |
| Df Residuals: | 20 | BIC: | 208.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -45.7433 | 55.462 | -0.825 | 0.419 | -161.434 69.948 |
| C(dose)[T.1] | 48.1057 | 8.626 | 5.577 | 0.000 | 30.112 66.099 |
| expression | 17.4731 | 9.646 | 1.812 | 0.085 | -2.647 37.593 |
| Omnibus: | 1.932 | Durbin-Watson: | 2.089 |
| Prob(Omnibus): | 0.381 | Jarque-Bera (JB): | 1.650 |
| Skew: | 0.605 | Prob(JB): | 0.438 |
| Kurtosis: | 2.493 | Cond. No. | 83.0 |
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: | 04:10:17 | 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: | 0.230 |
| Model: | OLS | Adj. R-squared: | 0.193 |
| Method: | Least Squares | F-statistic: | 6.263 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0207 |
| Time: | 04:10:17 | Log-Likelihood: | -110.10 |
| No. Observations: | 23 | AIC: | 224.2 |
| Df Residuals: | 21 | BIC: | 226.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -128.3290 | 83.375 | -1.539 | 0.139 | -301.716 45.058 |
| expression | 35.4815 | 14.178 | 2.503 | 0.021 | 5.996 64.966 |
| Omnibus: | 1.825 | Durbin-Watson: | 2.905 |
| Prob(Omnibus): | 0.401 | Jarque-Bera (JB): | 1.062 |
| Skew: | 0.134 | Prob(JB): | 0.588 |
| Kurtosis: | 1.982 | Cond. No. | 79.6 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.003 | 0.959 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.620 |
| Model: | OLS | Adj. R-squared: | 0.517 |
| Method: | Least Squares | F-statistic: | 5.995 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0113 |
| Time: | 04:10:17 | Log-Likelihood: | -68.034 |
| No. Observations: | 15 | AIC: | 144.1 |
| Df Residuals: | 11 | BIC: | 146.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 297.7334 | 143.300 | 2.078 | 0.062 | -17.668 613.135 |
| C(dose)[T.1] | -423.6818 | 212.906 | -1.990 | 0.072 | -892.285 44.921 |
| expression | -43.8262 | 27.204 | -1.611 | 0.135 | -103.701 16.048 |
| expression:C(dose)[T.1] | 86.3126 | 38.704 | 2.230 | 0.048 | 1.126 171.499 |
| Omnibus: | 3.199 | Durbin-Watson: | 1.473 |
| Prob(Omnibus): | 0.202 | Jarque-Bera (JB): | 1.732 |
| Skew: | -0.831 | Prob(JB): | 0.421 |
| Kurtosis: | 3.075 | Cond. No. | 236. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.887 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 04:10:17 | Log-Likelihood: | -70.831 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 73.6606 | 117.880 | 0.625 | 0.544 | -183.178 330.500 |
| C(dose)[T.1] | 49.7354 | 18.725 | 2.656 | 0.021 | 8.937 90.534 |
| expression | -1.1859 | 22.325 | -0.053 | 0.959 | -49.829 47.457 |
| Omnibus: | 2.721 | Durbin-Watson: | 0.822 |
| Prob(Omnibus): | 0.257 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.844 | Prob(JB): | 0.393 |
| Kurtosis: | 2.625 | Cond. No. | 86.3 |
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: | 04:10:17 | 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: | 0.125 |
| Model: | OLS | Adj. R-squared: | 0.058 |
| Method: | Least Squares | F-statistic: | 1.856 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.196 |
| Time: | 04:10:17 | Log-Likelihood: | -74.299 |
| No. Observations: | 15 | AIC: | 152.6 |
| Df Residuals: | 13 | BIC: | 154.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -76.4529 | 125.243 | -0.610 | 0.552 | -347.024 194.118 |
| expression | 30.9458 | 22.717 | 1.362 | 0.196 | -18.131 80.022 |
| Omnibus: | 1.138 | Durbin-Watson: | 1.418 |
| Prob(Omnibus): | 0.566 | Jarque-Bera (JB): | 0.765 |
| Skew: | -0.522 | Prob(JB): | 0.682 |
| Kurtosis: | 2.633 | Cond. No. | 75.0 |