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 |
| 0.777 | 0.388 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.614 |
| Method: | Least Squares | F-statistic: | 12.65 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 8.94e-05 |
| Time: | 03:44:50 | Log-Likelihood: | -100.48 |
| No. Observations: | 23 | AIC: | 209.0 |
| Df Residuals: | 19 | BIC: | 213.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.1852 | 38.368 | 1.986 | 0.062 | -4.121 156.491 |
| C(dose)[T.1] | 93.9628 | 87.022 | 1.080 | 0.294 | -88.177 276.103 |
| expression | -6.6834 | 11.521 | -0.580 | 0.569 | -30.798 17.431 |
| expression:C(dose)[T.1] | -13.2949 | 27.386 | -0.485 | 0.633 | -70.614 44.024 |
| Omnibus: | 1.776 | Durbin-Watson: | 1.666 |
| Prob(Omnibus): | 0.411 | Jarque-Bera (JB): | 1.050 |
| Skew: | 0.135 | Prob(JB): | 0.591 |
| Kurtosis: | 1.989 | Cond. No. | 80.7 |
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: | 19.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.94e-05 |
| Time: | 03:44:50 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 207.2 |
| Df Residuals: | 20 | BIC: | 210.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 83.9230 | 34.228 | 2.452 | 0.024 | 12.525 155.321 |
| C(dose)[T.1] | 51.9386 | 8.749 | 5.936 | 0.000 | 33.688 70.189 |
| expression | -9.0365 | 10.250 | -0.882 | 0.388 | -30.419 12.346 |
| Omnibus: | 0.600 | Durbin-Watson: | 1.699 |
| Prob(Omnibus): | 0.741 | Jarque-Bera (JB): | 0.621 |
| Skew: | 0.007 | Prob(JB): | 0.733 |
| Kurtosis: | 2.195 | Cond. No. | 28.5 |
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: | 03:44:50 | 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: | 0.067 |
| Model: | OLS | Adj. R-squared: | 0.023 |
| Method: | Least Squares | F-statistic: | 1.507 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.233 |
| Time: | 03:44:50 | Log-Likelihood: | -112.31 |
| No. Observations: | 23 | AIC: | 228.6 |
| Df Residuals: | 21 | BIC: | 230.9 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 144.2265 | 53.012 | 2.721 | 0.013 | 33.983 254.470 |
| expression | -20.0696 | 16.349 | -1.228 | 0.233 | -54.070 13.931 |
| Omnibus: | 0.663 | Durbin-Watson: | 2.099 |
| Prob(Omnibus): | 0.718 | Jarque-Bera (JB): | 0.690 |
| Skew: | 0.176 | Prob(JB): | 0.708 |
| Kurtosis: | 2.228 | Cond. No. | 27.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.038 | 0.848 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.466 |
| Model: | OLS | Adj. R-squared: | 0.320 |
| Method: | Least Squares | F-statistic: | 3.200 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0662 |
| Time: | 03:44:50 | Log-Likelihood: | -70.595 |
| No. Observations: | 15 | AIC: | 149.2 |
| Df Residuals: | 11 | BIC: | 152.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 62.4119 | 64.041 | 0.975 | 0.351 | -78.541 203.365 |
| C(dose)[T.1] | 118.1676 | 126.350 | 0.935 | 0.370 | -159.927 396.262 |
| expression | 1.3789 | 17.300 | 0.080 | 0.938 | -36.698 39.455 |
| expression:C(dose)[T.1] | -22.0196 | 39.016 | -0.564 | 0.584 | -107.893 63.854 |
| Omnibus: | 5.622 | Durbin-Watson: | 0.749 |
| Prob(Omnibus): | 0.060 | Jarque-Bera (JB): | 3.214 |
| Skew: | -1.115 | Prob(JB): | 0.200 |
| Kurtosis: | 3.417 | Cond. No. | 67.4 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.920 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0275 |
| Time: | 03:44:50 | Log-Likelihood: | -70.809 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 78.1626 | 55.979 | 1.396 | 0.188 | -43.806 200.131 |
| C(dose)[T.1] | 47.6038 | 17.693 | 2.691 | 0.020 | 9.055 86.153 |
| expression | -2.9503 | 15.059 | -0.196 | 0.848 | -35.762 29.861 |
| Omnibus: | 3.337 | Durbin-Watson: | 0.800 |
| Prob(Omnibus): | 0.189 | Jarque-Bera (JB): | 2.179 |
| Skew: | -0.927 | Prob(JB): | 0.336 |
| Kurtosis: | 2.770 | 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: | 03:44:50 | 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: | 0.119 |
| Model: | OLS | Adj. R-squared: | 0.051 |
| Method: | Least Squares | F-statistic: | 1.757 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.208 |
| Time: | 03:44:50 | Log-Likelihood: | -74.349 |
| No. Observations: | 15 | AIC: | 152.7 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 165.9261 | 55.345 | 2.998 | 0.010 | 46.361 285.491 |
| expression | -21.5676 | 16.272 | -1.325 | 0.208 | -56.721 13.586 |
| Omnibus: | 1.214 | Durbin-Watson: | 1.599 |
| Prob(Omnibus): | 0.545 | Jarque-Bera (JB): | 0.741 |
| Skew: | -0.525 | Prob(JB): | 0.690 |
| Kurtosis: | 2.714 | Cond. No. | 21.4 |