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 |
| 0.025 | 0.875 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.594 |
| Method: | Least Squares | F-statistic: | 11.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000141 |
| Time: | 04:56:57 | Log-Likelihood: | -101.04 |
| No. Observations: | 23 | AIC: | 210.1 |
| Df Residuals: | 19 | BIC: | 214.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 47.5855 | 38.978 | 1.221 | 0.237 | -33.996 129.167 |
| C(dose)[T.1] | 58.9931 | 78.425 | 0.752 | 0.461 | -105.153 223.139 |
| expression | 2.7762 | 16.130 | 0.172 | 0.865 | -30.984 36.536 |
| expression:C(dose)[T.1] | -2.3916 | 31.405 | -0.076 | 0.940 | -68.124 63.341 |
| Omnibus: | 0.347 | Durbin-Watson: | 1.923 |
| Prob(Omnibus): | 0.841 | Jarque-Bera (JB): | 0.500 |
| Skew: | 0.058 | Prob(JB): | 0.779 |
| Kurtosis: | 2.287 | Cond. No. | 59.3 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 18.53 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.80e-05 |
| Time: | 04:56:57 | Log-Likelihood: | -101.05 |
| 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 | 49.0905 | 32.751 | 1.499 | 0.150 | -19.226 117.407 |
| C(dose)[T.1] | 53.0617 | 8.934 | 5.939 | 0.000 | 34.426 71.697 |
| expression | 2.1453 | 13.491 | 0.159 | 0.875 | -25.997 30.288 |
| Omnibus: | 0.315 | Durbin-Watson: | 1.904 |
| Prob(Omnibus): | 0.854 | Jarque-Bera (JB): | 0.481 |
| Skew: | 0.055 | Prob(JB): | 0.786 |
| Kurtosis: | 2.300 | Cond. No. | 21.9 |
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: | 04:56:57 | 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: | 0.031 |
| Model: | OLS | Adj. R-squared: | -0.015 |
| Method: | Least Squares | F-statistic: | 0.6779 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.420 |
| Time: | 04:56:57 | Log-Likelihood: | -112.74 |
| No. Observations: | 23 | AIC: | 229.5 |
| Df Residuals: | 21 | BIC: | 231.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 36.4536 | 53.023 | 0.688 | 0.499 | -73.813 146.720 |
| expression | 17.6805 | 21.473 | 0.823 | 0.420 | -26.976 62.337 |
| Omnibus: | 2.021 | Durbin-Watson: | 2.541 |
| Prob(Omnibus): | 0.364 | Jarque-Bera (JB): | 1.223 |
| Skew: | 0.253 | Prob(JB): | 0.542 |
| Kurtosis: | 1.989 | Cond. No. | 21.4 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.119 | 0.736 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.506 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 3.758 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0444 |
| Time: | 04:56:57 | Log-Likelihood: | -70.009 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.0610 | 47.388 | 1.753 | 0.107 | -21.239 187.361 |
| C(dose)[T.1] | -36.9203 | 81.454 | -0.453 | 0.659 | -216.198 142.358 |
| expression | -5.3208 | 15.659 | -0.340 | 0.740 | -39.786 29.144 |
| expression:C(dose)[T.1] | 29.0885 | 27.045 | 1.076 | 0.305 | -30.437 88.614 |
| Omnibus: | 1.644 | Durbin-Watson: | 0.691 |
| Prob(Omnibus): | 0.440 | Jarque-Bera (JB): | 1.107 |
| Skew: | -0.639 | Prob(JB): | 0.575 |
| Kurtosis: | 2.628 | Cond. No. | 43.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.454 |
| Model: | OLS | Adj. R-squared: | 0.363 |
| Method: | Least Squares | F-statistic: | 4.993 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0264 |
| Time: | 04:56:57 | Log-Likelihood: | -70.759 |
| No. Observations: | 15 | AIC: | 147.5 |
| Df Residuals: | 12 | BIC: | 149.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.4114 | 39.448 | 1.379 | 0.193 | -31.539 140.362 |
| C(dose)[T.1] | 49.0742 | 15.666 | 3.132 | 0.009 | 14.940 83.208 |
| expression | 4.4307 | 12.850 | 0.345 | 0.736 | -23.568 32.429 |
| Omnibus: | 2.351 | Durbin-Watson: | 0.810 |
| Prob(Omnibus): | 0.309 | Jarque-Bera (JB): | 1.710 |
| Skew: | -0.787 | Prob(JB): | 0.425 |
| Kurtosis: | 2.493 | Cond. No. | 17.0 |
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: | 04:56:57 | 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: | 0.008 |
| Model: | OLS | Adj. R-squared: | -0.068 |
| Method: | Least Squares | F-statistic: | 0.1030 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.753 |
| Time: | 04:56:57 | Log-Likelihood: | -75.241 |
| No. Observations: | 15 | AIC: | 154.5 |
| Df Residuals: | 13 | BIC: | 155.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 77.8944 | 50.167 | 1.553 | 0.144 | -30.485 186.274 |
| expression | 5.3417 | 16.641 | 0.321 | 0.753 | -30.609 41.292 |
| Omnibus: | 0.740 | Durbin-Watson: | 1.608 |
| Prob(Omnibus): | 0.691 | Jarque-Bera (JB): | 0.627 |
| Skew: | 0.006 | Prob(JB): | 0.731 |
| Kurtosis: | 1.999 | Cond. No. | 16.5 |