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 |
| 0.110 | 0.744 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.672 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 12.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 7.62e-05 |
| Time: | 04:51:03 | Log-Likelihood: | -100.29 |
| No. Observations: | 23 | AIC: | 208.6 |
| Df Residuals: | 19 | BIC: | 213.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 96.5380 | 39.894 | 2.420 | 0.026 | 13.039 180.037 |
| C(dose)[T.1] | -0.9001 | 49.312 | -0.018 | 0.986 | -104.111 102.311 |
| expression | -9.9655 | 9.285 | -1.073 | 0.297 | -29.399 9.468 |
| expression:C(dose)[T.1] | 13.0422 | 11.818 | 1.104 | 0.284 | -11.692 37.777 |
| Omnibus: | 0.857 | Durbin-Watson: | 1.877 |
| Prob(Omnibus): | 0.651 | Jarque-Bera (JB): | 0.805 |
| Skew: | 0.225 | Prob(JB): | 0.669 |
| Kurtosis: | 2.201 | Cond. No. | 67.8 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.616 |
| Method: | Least Squares | F-statistic: | 18.65 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.68e-05 |
| Time: | 04:51:03 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 208.0 |
| Df Residuals: | 20 | BIC: | 211.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.3423 | 25.266 | 2.467 | 0.023 | 9.639 115.045 |
| C(dose)[T.1] | 52.6144 | 9.013 | 5.837 | 0.000 | 33.813 71.416 |
| expression | -1.9150 | 5.775 | -0.332 | 0.744 | -13.962 10.132 |
| Omnibus: | 0.097 | Durbin-Watson: | 1.881 |
| Prob(Omnibus): | 0.953 | Jarque-Bera (JB): | 0.321 |
| Skew: | 0.030 | Prob(JB): | 0.852 |
| Kurtosis: | 2.424 | Cond. No. | 25.6 |
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: | 04:51:03 | 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: | 0.056 |
| Model: | OLS | Adj. R-squared: | 0.011 |
| Method: | Least Squares | F-statistic: | 1.253 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.276 |
| Time: | 04:51:03 | Log-Likelihood: | -112.44 |
| No. Observations: | 23 | AIC: | 228.9 |
| Df Residuals: | 21 | BIC: | 231.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 120.6607 | 37.239 | 3.240 | 0.004 | 43.218 198.104 |
| expression | -10.0669 | 8.992 | -1.119 | 0.276 | -28.768 8.634 |
| Omnibus: | 3.812 | Durbin-Watson: | 2.528 |
| Prob(Omnibus): | 0.149 | Jarque-Bera (JB): | 1.956 |
| Skew: | 0.432 | Prob(JB): | 0.376 |
| Kurtosis: | 1.861 | Cond. No. | 23.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.955 | 0.348 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.598 |
| Model: | OLS | Adj. R-squared: | 0.488 |
| Method: | Least Squares | F-statistic: | 5.453 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0153 |
| Time: | 04:51:03 | Log-Likelihood: | -68.466 |
| No. Observations: | 15 | AIC: | 144.9 |
| Df Residuals: | 11 | BIC: | 147.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 34.8210 | 65.160 | 0.534 | 0.604 | -108.595 178.237 |
| C(dose)[T.1] | 187.2956 | 85.135 | 2.200 | 0.050 | -0.084 374.676 |
| expression | 6.5703 | 12.966 | 0.507 | 0.622 | -21.968 35.108 |
| expression:C(dose)[T.1] | -31.0602 | 18.026 | -1.723 | 0.113 | -70.736 8.616 |
| Omnibus: | 0.036 | Durbin-Watson: | 1.045 |
| Prob(Omnibus): | 0.982 | Jarque-Bera (JB): | 0.230 |
| Skew: | -0.084 | Prob(JB): | 0.891 |
| Kurtosis: | 2.417 | Cond. No. | 79.7 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.489 |
| Model: | OLS | Adj. R-squared: | 0.404 |
| Method: | Least Squares | F-statistic: | 5.751 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0177 |
| Time: | 04:51:03 | Log-Likelihood: | -70.259 |
| No. Observations: | 15 | AIC: | 146.5 |
| Df Residuals: | 12 | BIC: | 148.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 114.5708 | 49.485 | 2.315 | 0.039 | 6.753 222.389 |
| C(dose)[T.1] | 42.9718 | 16.433 | 2.615 | 0.023 | 7.168 78.775 |
| expression | -9.4990 | 9.719 | -0.977 | 0.348 | -30.674 11.676 |
| Omnibus: | 1.539 | Durbin-Watson: | 0.886 |
| Prob(Omnibus): | 0.463 | Jarque-Bera (JB): | 1.185 |
| Skew: | -0.504 | Prob(JB): | 0.553 |
| Kurtosis: | 2.061 | Cond. No. | 32.6 |
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: | 04:51:03 | 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: | 0.198 |
| Model: | OLS | Adj. R-squared: | 0.137 |
| Method: | Least Squares | F-statistic: | 3.219 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0961 |
| Time: | 04:51:03 | Log-Likelihood: | -73.641 |
| No. Observations: | 15 | AIC: | 151.3 |
| Df Residuals: | 13 | BIC: | 152.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 182.9295 | 50.580 | 3.617 | 0.003 | 73.659 292.200 |
| expression | -19.3488 | 10.785 | -1.794 | 0.096 | -42.648 3.951 |
| Omnibus: | 0.083 | Durbin-Watson: | 1.290 |
| Prob(Omnibus): | 0.959 | Jarque-Bera (JB): | 0.228 |
| Skew: | -0.142 | Prob(JB): | 0.892 |
| Kurtosis: | 2.466 | Cond. No. | 27.2 |