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 |
| 1.649 | 0.214 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.680 |
| Model: | OLS | Adj. R-squared: | 0.630 |
| Method: | Least Squares | F-statistic: | 13.48 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.99e-05 |
| Time: | 05:20:52 | Log-Likelihood: | -99.987 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 19 | BIC: | 212.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -280.2166 | 277.331 | -1.010 | 0.325 | -860.678 300.245 |
| C(dose)[T.1] | 242.0525 | 359.117 | 0.674 | 0.508 | -509.588 993.693 |
| expression | 39.7938 | 32.993 | 1.206 | 0.243 | -29.260 108.848 |
| expression:C(dose)[T.1] | -22.3989 | 42.776 | -0.524 | 0.607 | -111.930 67.133 |
| Omnibus: | 1.553 | Durbin-Watson: | 1.889 |
| Prob(Omnibus): | 0.460 | Jarque-Bera (JB): | 1.334 |
| Skew: | -0.448 | Prob(JB): | 0.513 |
| Kurtosis: | 2.233 | Cond. No. | 964. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.676 |
| Model: | OLS | Adj. R-squared: | 0.643 |
| Method: | Least Squares | F-statistic: | 20.84 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.28e-05 |
| Time: | 05:20:52 | Log-Likelihood: | -100.15 |
| No. Observations: | 23 | AIC: | 206.3 |
| Df Residuals: | 20 | BIC: | 209.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -168.2370 | 173.345 | -0.971 | 0.343 | -529.828 193.354 |
| C(dose)[T.1] | 54.0619 | 8.448 | 6.399 | 0.000 | 36.439 71.685 |
| expression | 26.4692 | 20.615 | 1.284 | 0.214 | -16.533 69.471 |
| Omnibus: | 1.244 | Durbin-Watson: | 1.824 |
| Prob(Omnibus): | 0.537 | Jarque-Bera (JB): | 1.129 |
| Skew: | -0.401 | Prob(JB): | 0.569 |
| Kurtosis: | 2.269 | Cond. No. | 351. |
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: | 05:20:52 | 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: | 0.012 |
| Model: | OLS | Adj. R-squared: | -0.035 |
| Method: | Least Squares | F-statistic: | 0.2538 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.620 |
| Time: | 05:20:52 | Log-Likelihood: | -112.97 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -68.4163 | 294.119 | -0.233 | 0.818 | -680.070 543.237 |
| expression | 17.6542 | 35.042 | 0.504 | 0.620 | -55.219 90.528 |
| Omnibus: | 3.373 | Durbin-Watson: | 2.518 |
| Prob(Omnibus): | 0.185 | Jarque-Bera (JB): | 1.533 |
| Skew: | 0.256 | Prob(JB): | 0.465 |
| Kurtosis: | 1.844 | Cond. No. | 349. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.883 | 0.195 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.555 |
| Model: | OLS | Adj. R-squared: | 0.434 |
| Method: | Least Squares | F-statistic: | 4.571 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0259 |
| Time: | 05:20:52 | Log-Likelihood: | -69.229 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 11 | BIC: | 149.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 504.8635 | 280.395 | 1.801 | 0.099 | -112.281 1122.008 |
| C(dose)[T.1] | -279.7339 | 377.218 | -0.742 | 0.474 | -1109.986 550.518 |
| expression | -55.8838 | 35.795 | -1.561 | 0.147 | -134.668 22.900 |
| expression:C(dose)[T.1] | 42.1769 | 47.914 | 0.880 | 0.398 | -63.281 147.634 |
| Omnibus: | 2.754 | Durbin-Watson: | 1.470 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 1.611 |
| Skew: | -0.801 | Prob(JB): | 0.447 |
| Kurtosis: | 2.896 | Cond. No. | 559. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.524 |
| Model: | OLS | Adj. R-squared: | 0.444 |
| Method: | Least Squares | F-statistic: | 6.593 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0117 |
| Time: | 05:20:52 | Log-Likelihood: | -69.740 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 12 | BIC: | 147.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 320.6071 | 184.808 | 1.735 | 0.108 | -82.054 723.268 |
| C(dose)[T.1] | 52.0593 | 14.781 | 3.522 | 0.004 | 19.854 84.265 |
| expression | -32.3444 | 23.570 | -1.372 | 0.195 | -83.700 19.011 |
| Omnibus: | 2.372 | Durbin-Watson: | 1.137 |
| Prob(Omnibus): | 0.306 | Jarque-Bera (JB): | 1.635 |
| Skew: | -0.784 | Prob(JB): | 0.442 |
| Kurtosis: | 2.604 | Cond. No. | 203. |
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: | 05:20:52 | 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: | 0.031 |
| Model: | OLS | Adj. R-squared: | -0.044 |
| Method: | Least Squares | F-statistic: | 0.4163 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.530 |
| Time: | 05:20:52 | Log-Likelihood: | -75.064 |
| 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 | 256.1062 | 251.964 | 1.016 | 0.328 | -288.229 800.441 |
| expression | -20.6278 | 31.971 | -0.645 | 0.530 | -89.697 48.441 |
| Omnibus: | 2.178 | Durbin-Watson: | 1.707 |
| Prob(Omnibus): | 0.336 | Jarque-Bera (JB): | 1.086 |
| Skew: | 0.252 | Prob(JB): | 0.581 |
| Kurtosis: | 1.782 | Cond. No. | 202. |