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 |
| 0.036 | 0.851 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.665 |
| Model: | OLS | Adj. R-squared: | 0.612 |
| Method: | Least Squares | F-statistic: | 12.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.38e-05 |
| Time: | 03:37:18 | Log-Likelihood: | -100.54 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 32.1819 | 38.878 | 0.828 | 0.418 | -49.190 113.554 |
| C(dose)[T.1] | 132.3230 | 86.220 | 1.535 | 0.141 | -48.138 312.784 |
| expression | 4.7970 | 8.363 | 0.574 | 0.573 | -12.706 22.300 |
| expression:C(dose)[T.1] | -17.5166 | 19.059 | -0.919 | 0.370 | -57.408 22.375 |
| Omnibus: | 2.091 | Durbin-Watson: | 1.906 |
| Prob(Omnibus): | 0.352 | Jarque-Bera (JB): | 1.214 |
| Skew: | 0.229 | Prob(JB): | 0.545 |
| Kurtosis: | 1.971 | Cond. No. | 108. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.55 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.78e-05 |
| Time: | 03:37:18 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 208.1 |
| Df Residuals: | 20 | BIC: | 211.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.6665 | 34.901 | 1.366 | 0.187 | -25.136 120.469 |
| C(dose)[T.1] | 53.4990 | 8.803 | 6.077 | 0.000 | 35.136 71.862 |
| expression | 1.4247 | 7.485 | 0.190 | 0.851 | -14.190 17.039 |
| Omnibus: | 0.196 | Durbin-Watson: | 1.925 |
| Prob(Omnibus): | 0.907 | Jarque-Bera (JB): | 0.403 |
| Skew: | 0.018 | Prob(JB): | 0.818 |
| Kurtosis: | 2.353 | Cond. No. | 38.4 |
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: | 03:37:18 | 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: | 0.003 |
| Model: | OLS | Adj. R-squared: | -0.045 |
| Method: | Least Squares | F-statistic: | 0.05866 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.811 |
| Time: | 03:37:18 | Log-Likelihood: | -113.07 |
| No. Observations: | 23 | AIC: | 230.1 |
| Df Residuals: | 21 | BIC: | 232.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.1981 | 56.127 | 1.660 | 0.112 | -23.523 209.920 |
| expression | -2.9710 | 12.267 | -0.242 | 0.811 | -28.483 22.541 |
| Omnibus: | 3.081 | Durbin-Watson: | 2.489 |
| Prob(Omnibus): | 0.214 | Jarque-Bera (JB): | 1.499 |
| Skew: | 0.272 | Prob(JB): | 0.473 |
| Kurtosis: | 1.874 | Cond. No. | 37.3 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.017 | 0.898 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.493 |
| Model: | OLS | Adj. R-squared: | 0.354 |
| Method: | Least Squares | F-statistic: | 3.559 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0510 |
| Time: | 03:37:18 | Log-Likelihood: | -70.213 |
| No. Observations: | 15 | AIC: | 148.4 |
| Df Residuals: | 11 | BIC: | 151.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -52.2033 | 141.009 | -0.370 | 0.718 | -362.562 258.156 |
| C(dose)[T.1] | 197.4054 | 153.774 | 1.284 | 0.226 | -141.048 535.859 |
| expression | 31.4291 | 36.921 | 0.851 | 0.413 | -49.834 112.693 |
| expression:C(dose)[T.1] | -38.2599 | 39.643 | -0.965 | 0.355 | -125.513 48.993 |
| Omnibus: | 2.418 | Durbin-Watson: | 0.636 |
| Prob(Omnibus): | 0.298 | Jarque-Bera (JB): | 1.836 |
| Skew: | -0.793 | Prob(JB): | 0.399 |
| Kurtosis: | 2.349 | Cond. No. | 133. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.450 |
| Model: | OLS | Adj. R-squared: | 0.358 |
| Method: | Least Squares | F-statistic: | 4.900 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0278 |
| Time: | 03:37:18 | Log-Likelihood: | -70.822 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 74.1219 | 52.302 | 1.417 | 0.182 | -39.834 188.078 |
| C(dose)[T.1] | 49.8596 | 16.521 | 3.018 | 0.011 | 13.863 85.856 |
| expression | -1.7584 | 13.405 | -0.131 | 0.898 | -30.966 27.449 |
| Omnibus: | 2.749 | Durbin-Watson: | 0.808 |
| Prob(Omnibus): | 0.253 | Jarque-Bera (JB): | 1.937 |
| Skew: | -0.854 | Prob(JB): | 0.380 |
| Kurtosis: | 2.571 | Cond. No. | 29.0 |
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: | 03:37:18 | 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: | 0.032 |
| Model: | OLS | Adj. R-squared: | -0.043 |
| Method: | Least Squares | F-statistic: | 0.4267 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.525 |
| Time: | 03:37:18 | Log-Likelihood: | -75.058 |
| No. Observations: | 15 | AIC: | 154.1 |
| Df Residuals: | 13 | BIC: | 155.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 51.0982 | 65.933 | 0.775 | 0.452 | -91.341 193.537 |
| expression | 10.6221 | 16.262 | 0.653 | 0.525 | -24.509 45.754 |
| Omnibus: | 0.365 | Durbin-Watson: | 1.535 |
| Prob(Omnibus): | 0.833 | Jarque-Bera (JB): | 0.497 |
| Skew: | 0.213 | Prob(JB): | 0.780 |
| Kurtosis: | 2.217 | Cond. No. | 28.3 |