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 |
| 0.766 | 0.392 | 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: | 0.610 |
| Method: | Least Squares | F-statistic: | 12.48 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.69e-05 |
| Time: | 03:52:26 | Log-Likelihood: | -100.58 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -105.7228 | 194.479 | -0.544 | 0.593 | -512.772 301.326 |
| C(dose)[T.1] | 133.5318 | 292.533 | 0.456 | 0.653 | -478.747 745.811 |
| expression | 16.5738 | 20.144 | 0.823 | 0.421 | -25.588 58.736 |
| expression:C(dose)[T.1] | -8.4588 | 30.000 | -0.282 | 0.781 | -71.249 54.331 |
| Omnibus: | 0.245 | Durbin-Watson: | 1.574 |
| Prob(Omnibus): | 0.885 | Jarque-Bera (JB): | 0.436 |
| Skew: | 0.020 | Prob(JB): | 0.804 |
| Kurtosis: | 2.327 | Cond. No. | 830. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.628 |
| Method: | Least Squares | F-statistic: | 19.59 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.95e-05 |
| Time: | 03:52:26 | Log-Likelihood: | -100.63 |
| No. Observations: | 23 | AIC: | 207.3 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -68.9201 | 140.815 | -0.489 | 0.630 | -362.656 224.815 |
| C(dose)[T.1] | 51.0894 | 8.982 | 5.688 | 0.000 | 32.354 69.825 |
| expression | 12.7599 | 14.580 | 0.875 | 0.392 | -17.653 43.173 |
| Omnibus: | 0.099 | Durbin-Watson: | 1.672 |
| Prob(Omnibus): | 0.952 | Jarque-Bera (JB): | 0.324 |
| Skew: | 0.010 | Prob(JB): | 0.850 |
| Kurtosis: | 2.419 | Cond. No. | 323. |
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: | 03:52:26 | 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: | 0.115 |
| Model: | OLS | Adj. R-squared: | 0.073 |
| Method: | Least Squares | F-statistic: | 2.734 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.113 |
| Time: | 03:52:26 | Log-Likelihood: | -111.70 |
| 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 | -275.3232 | 214.833 | -1.282 | 0.214 | -722.094 171.447 |
| expression | 36.4747 | 22.060 | 1.653 | 0.113 | -9.401 82.350 |
| Omnibus: | 2.583 | Durbin-Watson: | 2.306 |
| Prob(Omnibus): | 0.275 | Jarque-Bera (JB): | 1.472 |
| Skew: | 0.326 | Prob(JB): | 0.479 |
| Kurtosis: | 1.947 | Cond. No. | 311. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.134 | 0.308 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.641 |
| Model: | OLS | Adj. R-squared: | 0.542 |
| Method: | Least Squares | F-statistic: | 6.533 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00847 |
| Time: | 03:52:26 | Log-Likelihood: | -67.627 |
| No. Observations: | 15 | AIC: | 143.3 |
| Df Residuals: | 11 | BIC: | 146.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.9377 | 220.857 | 0.158 | 0.877 | -451.165 521.041 |
| C(dose)[T.1] | 872.6293 | 394.821 | 2.210 | 0.049 | 3.635 1741.624 |
| expression | 3.5084 | 23.825 | 0.147 | 0.886 | -48.931 55.948 |
| expression:C(dose)[T.1] | -90.9586 | 43.312 | -2.100 | 0.060 | -186.289 4.371 |
| Omnibus: | 0.056 | Durbin-Watson: | 1.242 |
| Prob(Omnibus): | 0.972 | Jarque-Bera (JB): | 0.247 |
| Skew: | -0.107 | Prob(JB): | 0.884 |
| Kurtosis: | 2.409 | Cond. No. | 675. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.496 |
| Model: | OLS | Adj. R-squared: | 0.412 |
| Method: | Least Squares | F-statistic: | 5.914 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0163 |
| Time: | 03:52:26 | Log-Likelihood: | -70.156 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 289.8278 | 209.098 | 1.386 | 0.191 | -165.757 745.413 |
| C(dose)[T.1] | 44.0008 | 15.816 | 2.782 | 0.017 | 9.541 78.460 |
| expression | -24.0150 | 22.548 | -1.065 | 0.308 | -73.142 25.112 |
| Omnibus: | 1.021 | Durbin-Watson: | 0.802 |
| Prob(Omnibus): | 0.600 | Jarque-Bera (JB): | 0.908 |
| Skew: | -0.464 | Prob(JB): | 0.635 |
| Kurtosis: | 2.231 | Cond. No. | 258. |
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: | 03:52:26 | 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: | 0.172 |
| Model: | OLS | Adj. R-squared: | 0.108 |
| Method: | Least Squares | F-statistic: | 2.692 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.125 |
| Time: | 03:52:26 | Log-Likelihood: | -73.889 |
| No. Observations: | 15 | AIC: | 151.8 |
| Df Residuals: | 13 | BIC: | 153.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 490.2411 | 241.890 | 2.027 | 0.064 | -32.330 1012.812 |
| expression | -43.3630 | 26.430 | -1.641 | 0.125 | -100.461 13.735 |
| Omnibus: | 0.338 | Durbin-Watson: | 1.482 |
| Prob(Omnibus): | 0.844 | Jarque-Bera (JB): | 0.479 |
| Skew: | -0.151 | Prob(JB): | 0.787 |
| Kurtosis: | 2.179 | Cond. No. | 242. |