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 |
| 0.666 | 0.424 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.664 |
| Model: | OLS | Adj. R-squared: | 0.611 |
| Method: | Least Squares | F-statistic: | 12.50 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.62e-05 |
| Time: | 05:03:54 | Log-Likelihood: | -100.57 |
| No. Observations: | 23 | AIC: | 209.1 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 90.3059 | 106.852 | 0.845 | 0.409 | -133.338 313.950 |
| C(dose)[T.1] | 117.1027 | 159.624 | 0.734 | 0.472 | -216.993 451.199 |
| expression | -5.4770 | 16.186 | -0.338 | 0.739 | -39.355 28.401 |
| expression:C(dose)[T.1] | -10.9642 | 25.339 | -0.433 | 0.670 | -63.999 42.071 |
| Omnibus: | 1.236 | Durbin-Watson: | 1.841 |
| Prob(Omnibus): | 0.539 | Jarque-Bera (JB): | 0.894 |
| Skew: | 0.141 | Prob(JB): | 0.639 |
| Kurtosis: | 2.076 | Cond. No. | 294. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.660 |
| Model: | OLS | Adj. R-squared: | 0.626 |
| Method: | Least Squares | F-statistic: | 19.44 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.04e-05 |
| Time: | 05:03:54 | Log-Likelihood: | -100.69 |
| No. Observations: | 23 | AIC: | 207.4 |
| Df Residuals: | 20 | BIC: | 210.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 119.7922 | 80.613 | 1.486 | 0.153 | -48.363 287.947 |
| C(dose)[T.1] | 48.1947 | 10.685 | 4.511 | 0.000 | 25.906 70.483 |
| expression | -9.9510 | 12.198 | -0.816 | 0.424 | -35.395 15.493 |
| Omnibus: | 1.012 | Durbin-Watson: | 1.924 |
| Prob(Omnibus): | 0.603 | Jarque-Bera (JB): | 0.781 |
| Skew: | 0.004 | Prob(JB): | 0.677 |
| Kurtosis: | 2.098 | Cond. No. | 122. |
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: | 05:03:54 | 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: | 0.315 |
| Model: | OLS | Adj. R-squared: | 0.282 |
| Method: | Least Squares | F-statistic: | 9.651 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00534 |
| Time: | 05:03:54 | Log-Likelihood: | -108.76 |
| No. Observations: | 23 | AIC: | 221.5 |
| Df Residuals: | 21 | BIC: | 223.8 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 348.7387 | 86.804 | 4.018 | 0.001 | 168.219 529.258 |
| expression | -42.4086 | 13.651 | -3.107 | 0.005 | -70.798 -14.019 |
| Omnibus: | 0.278 | Durbin-Watson: | 2.674 |
| Prob(Omnibus): | 0.870 | Jarque-Bera (JB): | 0.048 |
| Skew: | 0.104 | Prob(JB): | 0.976 |
| Kurtosis: | 2.918 | Cond. No. | 94.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.318 | 0.583 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.560 |
| Model: | OLS | Adj. R-squared: | 0.440 |
| Method: | Least Squares | F-statistic: | 4.660 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0245 |
| Time: | 05:03:54 | Log-Likelihood: | -69.149 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 253.9433 | 135.689 | 1.872 | 0.088 | -44.705 552.592 |
| C(dose)[T.1] | -316.4172 | 235.455 | -1.344 | 0.206 | -834.651 201.816 |
| expression | -25.5360 | 18.519 | -1.379 | 0.195 | -66.296 15.224 |
| expression:C(dose)[T.1] | 50.2197 | 32.317 | 1.554 | 0.148 | -20.909 121.349 |
| Omnibus: | 2.650 | Durbin-Watson: | 1.052 |
| Prob(Omnibus): | 0.266 | Jarque-Bera (JB): | 1.144 |
| Skew: | -0.664 | Prob(JB): | 0.564 |
| Kurtosis: | 3.264 | Cond. No. | 295. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.463 |
| Model: | OLS | Adj. R-squared: | 0.373 |
| Method: | Least Squares | F-statistic: | 5.173 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0240 |
| Time: | 05:03:54 | Log-Likelihood: | -70.637 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 133.4903 | 117.752 | 1.134 | 0.279 | -123.070 390.050 |
| C(dose)[T.1] | 48.7602 | 15.555 | 3.135 | 0.009 | 14.870 82.651 |
| expression | -9.0446 | 16.047 | -0.564 | 0.583 | -44.007 25.918 |
| Omnibus: | 2.577 | Durbin-Watson: | 0.848 |
| Prob(Omnibus): | 0.276 | Jarque-Bera (JB): | 1.805 |
| Skew: | -0.823 | Prob(JB): | 0.405 |
| Kurtosis: | 2.578 | Cond. No. | 113. |
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: | 05:03:54 | 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: | 0.023 |
| Model: | OLS | Adj. R-squared: | -0.052 |
| Method: | Least Squares | F-statistic: | 0.3092 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.588 |
| Time: | 05:03:54 | Log-Likelihood: | -75.124 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 177.7118 | 151.479 | 1.173 | 0.262 | -149.539 504.963 |
| expression | -11.5474 | 20.767 | -0.556 | 0.588 | -56.411 33.316 |
| Omnibus: | 2.277 | Durbin-Watson: | 1.750 |
| Prob(Omnibus): | 0.320 | Jarque-Bera (JB): | 1.117 |
| Skew: | 0.265 | Prob(JB): | 0.572 |
| Kurtosis: | 1.772 | Cond. No. | 112. |