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 |
| 0.806 | 0.380 | 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, 21 Nov 2024 | Prob (F-statistic): | 9.88e-05 |
| Time: | 05:24:08 | 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 | 160.5724 | 171.654 | 0.935 | 0.361 | -198.704 519.848 |
| C(dose)[T.1] | 65.1977 | 256.580 | 0.254 | 0.802 | -471.830 602.226 |
| expression | -13.8691 | 22.368 | -0.620 | 0.543 | -60.686 32.948 |
| expression:C(dose)[T.1] | -1.8521 | 33.804 | -0.055 | 0.957 | -72.605 68.901 |
| Omnibus: | 0.516 | Durbin-Watson: | 1.896 |
| Prob(Omnibus): | 0.773 | Jarque-Bera (JB): | 0.583 |
| Skew: | 0.009 | Prob(JB): | 0.747 |
| Kurtosis: | 2.220 | Cond. No. | 568. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.663 |
| Model: | OLS | Adj. R-squared: | 0.629 |
| Method: | Least Squares | F-statistic: | 19.64 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.91e-05 |
| Time: | 05:24:08 | Log-Likelihood: | -100.61 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 166.7917 | 125.513 | 1.329 | 0.199 | -95.025 428.608 |
| C(dose)[T.1] | 51.1485 | 8.937 | 5.723 | 0.000 | 32.506 69.791 |
| expression | -14.6800 | 16.348 | -0.898 | 0.380 | -48.781 19.421 |
| Omnibus: | 0.514 | Durbin-Watson: | 1.901 |
| Prob(Omnibus): | 0.773 | Jarque-Bera (JB): | 0.583 |
| Skew: | 0.016 | Prob(JB): | 0.747 |
| Kurtosis: | 2.221 | Cond. No. | 226. |
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: | 05:24:09 | 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: | 0.110 |
| Model: | OLS | Adj. R-squared: | 0.068 |
| Method: | Least Squares | F-statistic: | 2.600 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.122 |
| Time: | 05:24:09 | Log-Likelihood: | -111.76 |
| No. Observations: | 23 | AIC: | 227.5 |
| Df Residuals: | 21 | BIC: | 229.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 385.1140 | 189.526 | 2.032 | 0.055 | -9.027 779.256 |
| expression | -40.1951 | 24.929 | -1.612 | 0.122 | -92.037 11.647 |
| Omnibus: | 4.766 | Durbin-Watson: | 2.326 |
| Prob(Omnibus): | 0.092 | Jarque-Bera (JB): | 1.725 |
| Skew: | 0.224 | Prob(JB): | 0.422 |
| Kurtosis: | 1.736 | Cond. No. | 215. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.433 | 0.523 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.323 |
| Method: | Least Squares | F-statistic: | 3.226 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0649 |
| Time: | 05:24:09 | Log-Likelihood: | -70.566 |
| No. Observations: | 15 | AIC: | 149.1 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 150.5988 | 204.224 | 0.737 | 0.476 | -298.895 600.092 |
| C(dose)[T.1] | 61.1081 | 284.279 | 0.215 | 0.834 | -564.586 686.802 |
| expression | -12.1206 | 29.712 | -0.408 | 0.691 | -77.517 53.276 |
| expression:C(dose)[T.1] | -1.6505 | 41.238 | -0.040 | 0.969 | -92.416 89.115 |
| Omnibus: | 2.463 | Durbin-Watson: | 0.946 |
| Prob(Omnibus): | 0.292 | Jarque-Bera (JB): | 1.666 |
| Skew: | -0.626 | Prob(JB): | 0.435 |
| Kurtosis: | 1.951 | Cond. No. | 333. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.277 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0227 |
| Time: | 05:24:09 | Log-Likelihood: | -70.567 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.4781 | 135.845 | 1.152 | 0.272 | -139.502 452.458 |
| C(dose)[T.1] | 49.7488 | 15.486 | 3.212 | 0.007 | 16.007 83.490 |
| expression | -12.9774 | 19.728 | -0.658 | 0.523 | -55.962 30.007 |
| Omnibus: | 2.406 | Durbin-Watson: | 0.957 |
| Prob(Omnibus): | 0.300 | Jarque-Bera (JB): | 1.653 |
| Skew: | -0.628 | Prob(JB): | 0.438 |
| Kurtosis: | 1.967 | Cond. No. | 124. |
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: | 05:24:09 | 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: | 0.010 |
| Model: | OLS | Adj. R-squared: | -0.066 |
| Method: | Least Squares | F-statistic: | 0.1366 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.718 |
| Time: | 05:24:09 | Log-Likelihood: | -75.222 |
| No. Observations: | 15 | AIC: | 154.4 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 159.3530 | 177.995 | 0.895 | 0.387 | -225.183 543.889 |
| expression | -9.5410 | 25.812 | -0.370 | 0.718 | -65.305 46.223 |
| Omnibus: | 0.984 | Durbin-Watson: | 1.685 |
| Prob(Omnibus): | 0.611 | Jarque-Bera (JB): | 0.723 |
| Skew: | 0.127 | Prob(JB): | 0.697 |
| Kurtosis: | 1.955 | Cond. No. | 124. |