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 |
| 1.146 | 0.297 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.675 |
| Model: | OLS | Adj. R-squared: | 0.624 |
| Method: | Least Squares | F-statistic: | 13.16 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 6.96e-05 |
| Time: | 05:07:50 | Log-Likelihood: | -100.17 |
| No. Observations: | 23 | AIC: | 208.3 |
| Df Residuals: | 19 | BIC: | 212.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.9029 | 80.033 | 0.324 | 0.750 | -141.609 193.415 |
| C(dose)[T.1] | -27.7169 | 122.373 | -0.226 | 0.823 | -283.846 228.412 |
| expression | 3.7321 | 10.523 | 0.355 | 0.727 | -18.292 25.757 |
| expression:C(dose)[T.1] | 10.1590 | 15.760 | 0.645 | 0.527 | -22.826 43.144 |
| Omnibus: | 0.806 | Durbin-Watson: | 1.836 |
| Prob(Omnibus): | 0.668 | Jarque-Bera (JB): | 0.428 |
| Skew: | -0.331 | Prob(JB): | 0.807 |
| Kurtosis: | 2.909 | Cond. No. | 282. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.668 |
| Model: | OLS | Adj. R-squared: | 0.635 |
| Method: | Least Squares | F-statistic: | 20.13 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.62e-05 |
| Time: | 05:07:50 | Log-Likelihood: | -100.42 |
| No. Observations: | 23 | AIC: | 206.8 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -8.4482 | 58.834 | -0.144 | 0.887 | -131.174 114.277 |
| C(dose)[T.1] | 50.9558 | 8.814 | 5.781 | 0.000 | 32.569 69.342 |
| expression | 8.2612 | 7.718 | 1.070 | 0.297 | -7.839 24.361 |
| Omnibus: | 0.464 | Durbin-Watson: | 1.828 |
| Prob(Omnibus): | 0.793 | Jarque-Bera (JB): | 0.533 |
| Skew: | -0.285 | Prob(JB): | 0.766 |
| Kurtosis: | 2.518 | Cond. No. | 109. |
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: | 05:07:50 | 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: | 0.113 |
| Model: | OLS | Adj. R-squared: | 0.071 |
| Method: | Least Squares | F-statistic: | 2.686 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.116 |
| Time: | 05:07:50 | Log-Likelihood: | -111.72 |
| No. Observations: | 23 | AIC: | 227.4 |
| Df Residuals: | 21 | BIC: | 229.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -71.0462 | 92.233 | -0.770 | 0.450 | -262.855 120.763 |
| expression | 19.5232 | 11.911 | 1.639 | 0.116 | -5.248 44.294 |
| Omnibus: | 2.324 | Durbin-Watson: | 2.380 |
| Prob(Omnibus): | 0.313 | Jarque-Bera (JB): | 1.141 |
| Skew: | -0.027 | Prob(JB): | 0.565 |
| Kurtosis: | 1.910 | Cond. No. | 107. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.740 | 0.212 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.519 |
| Model: | OLS | Adj. R-squared: | 0.388 |
| Method: | Least Squares | F-statistic: | 3.959 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0387 |
| Time: | 05:07:50 | Log-Likelihood: | -69.809 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 11 | BIC: | 150.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 186.8639 | 137.046 | 1.364 | 0.200 | -114.771 488.499 |
| C(dose)[T.1] | 76.7928 | 210.863 | 0.364 | 0.723 | -387.314 540.900 |
| expression | -14.0021 | 16.013 | -0.874 | 0.401 | -49.246 21.242 |
| expression:C(dose)[T.1] | -2.7556 | 24.261 | -0.114 | 0.912 | -56.153 50.642 |
| Omnibus: | 1.656 | Durbin-Watson: | 0.896 |
| Prob(Omnibus): | 0.437 | Jarque-Bera (JB): | 1.132 |
| Skew: | -0.644 | Prob(JB): | 0.568 |
| Kurtosis: | 2.608 | Cond. No. | 313. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.519 |
| Model: | OLS | Adj. R-squared: | 0.438 |
| Method: | Least Squares | F-statistic: | 6.463 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0124 |
| Time: | 05:07:50 | Log-Likelihood: | -69.817 |
| No. Observations: | 15 | AIC: | 145.6 |
| Df Residuals: | 12 | BIC: | 147.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 197.1034 | 98.883 | 1.993 | 0.069 | -18.345 412.552 |
| C(dose)[T.1] | 52.9085 | 14.976 | 3.533 | 0.004 | 20.279 85.538 |
| expression | -15.2025 | 11.524 | -1.319 | 0.212 | -40.311 9.906 |
| Omnibus: | 1.679 | Durbin-Watson: | 0.932 |
| Prob(Omnibus): | 0.432 | Jarque-Bera (JB): | 1.173 |
| Skew: | -0.652 | Prob(JB): | 0.556 |
| Kurtosis: | 2.579 | Cond. No. | 119. |
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: | 05:07:50 | 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: | 0.018 |
| Model: | OLS | Adj. R-squared: | -0.058 |
| Method: | Least Squares | F-statistic: | 0.2364 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.635 |
| Time: | 05:07:50 | Log-Likelihood: | -75.165 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.0725 | 134.891 | 1.179 | 0.259 | -132.341 450.486 |
| expression | -7.5526 | 15.533 | -0.486 | 0.635 | -41.109 26.004 |
| Omnibus: | 1.276 | Durbin-Watson: | 1.771 |
| Prob(Omnibus): | 0.528 | Jarque-Bera (JB): | 0.806 |
| Skew: | 0.131 | Prob(JB): | 0.668 |
| Kurtosis: | 1.895 | Cond. No. | 118. |