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 |
| 0.209 | 0.652 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.598 |
| Method: | Least Squares | F-statistic: | 11.90 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000129 |
| Time: | 03:42:29 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 209.9 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 44.3673 | 25.291 | 1.754 | 0.095 | -8.566 97.301 |
| C(dose)[T.1] | 51.5259 | 63.681 | 0.809 | 0.428 | -81.761 184.813 |
| expression | 2.1458 | 5.347 | 0.401 | 0.693 | -9.045 13.336 |
| expression:C(dose)[T.1] | 0.7980 | 15.618 | 0.051 | 0.960 | -31.891 33.487 |
| Omnibus: | 0.525 | Durbin-Watson: | 1.863 |
| Prob(Omnibus): | 0.769 | Jarque-Bera (JB): | 0.599 |
| Skew: | 0.101 | Prob(JB): | 0.741 |
| Kurtosis: | 2.235 | Cond. No. | 73.1 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.618 |
| Method: | Least Squares | F-statistic: | 18.79 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.55e-05 |
| Time: | 03:42:29 | Log-Likelihood: | -100.94 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 20 | BIC: | 211.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 43.9383 | 23.254 | 1.889 | 0.073 | -4.569 92.446 |
| C(dose)[T.1] | 54.7434 | 9.250 | 5.918 | 0.000 | 35.447 74.040 |
| expression | 2.2393 | 4.897 | 0.457 | 0.652 | -7.975 12.454 |
| Omnibus: | 0.484 | Durbin-Watson: | 1.866 |
| Prob(Omnibus): | 0.785 | Jarque-Bera (JB): | 0.577 |
| Skew: | 0.090 | Prob(JB): | 0.750 |
| Kurtosis: | 2.246 | Cond. No. | 25.1 |
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: | 03:42:29 | 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: | 0.045 |
| Model: | OLS | Adj. R-squared: | -0.001 |
| Method: | Least Squares | F-statistic: | 0.9784 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.334 |
| Time: | 03:42:29 | Log-Likelihood: | -112.58 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 111.4086 | 32.806 | 3.396 | 0.003 | 43.184 179.633 |
| expression | -7.3942 | 7.475 | -0.989 | 0.334 | -22.940 8.152 |
| Omnibus: | 1.899 | Durbin-Watson: | 2.492 |
| Prob(Omnibus): | 0.387 | Jarque-Bera (JB): | 1.074 |
| Skew: | 0.121 | Prob(JB): | 0.585 |
| Kurtosis: | 1.969 | Cond. No. | 21.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.001 | 0.975 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.299 |
| Method: | Least Squares | F-statistic: | 2.988 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0776 |
| Time: | 03:42:29 | Log-Likelihood: | -70.830 |
| No. Observations: | 15 | AIC: | 149.7 |
| Df Residuals: | 11 | BIC: | 152.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 66.5722 | 51.370 | 1.296 | 0.222 | -46.493 179.637 |
| C(dose)[T.1] | 39.4075 | 171.403 | 0.230 | 0.822 | -337.848 416.663 |
| expression | 0.1921 | 11.205 | 0.017 | 0.987 | -24.469 24.853 |
| expression:C(dose)[T.1] | 3.0181 | 50.457 | 0.060 | 0.953 | -108.036 114.072 |
| Omnibus: | 2.486 | Durbin-Watson: | 0.814 |
| Prob(Omnibus): | 0.288 | Jarque-Bera (JB): | 1.759 |
| Skew: | -0.809 | Prob(JB): | 0.415 |
| Kurtosis: | 2.556 | Cond. No. | 97.2 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.357 |
| Method: | Least Squares | F-statistic: | 4.886 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0280 |
| Time: | 03:42:29 | Log-Likelihood: | -70.832 |
| No. Observations: | 15 | AIC: | 147.7 |
| Df Residuals: | 12 | BIC: | 149.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 65.9088 | 48.031 | 1.372 | 0.195 | -38.741 170.559 |
| C(dose)[T.1] | 49.5857 | 19.759 | 2.510 | 0.027 | 6.536 92.636 |
| expression | 0.3409 | 10.461 | 0.033 | 0.975 | -22.452 23.134 |
| Omnibus: | 2.683 | Durbin-Watson: | 0.815 |
| Prob(Omnibus): | 0.262 | Jarque-Bera (JB): | 1.849 |
| Skew: | -0.838 | Prob(JB): | 0.397 |
| Kurtosis: | 2.615 | Cond. No. | 26.8 |
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: | 03:42:29 | 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: | 0.160 |
| Model: | OLS | Adj. R-squared: | 0.095 |
| Method: | Least Squares | F-statistic: | 2.468 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.140 |
| Time: | 03:42:29 | Log-Likelihood: | -73.996 |
| No. Observations: | 15 | AIC: | 152.0 |
| Df Residuals: | 13 | BIC: | 153.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 153.4430 | 39.175 | 3.917 | 0.002 | 68.810 238.076 |
| expression | -15.5309 | 9.886 | -1.571 | 0.140 | -36.889 5.827 |
| Omnibus: | 1.898 | Durbin-Watson: | 1.264 |
| Prob(Omnibus): | 0.387 | Jarque-Bera (JB): | 1.003 |
| Skew: | -0.221 | Prob(JB): | 0.606 |
| Kurtosis: | 1.813 | Cond. No. | 17.7 |