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 |
| 3.477 | 0.077 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.705 |
| Model: | OLS | Adj. R-squared: | 0.658 |
| Method: | Least Squares | F-statistic: | 15.12 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.86e-05 |
| Time: | 03:35:16 | Log-Likelihood: | -99.075 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 19 | BIC: | 210.7 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -7.7928 | 37.228 | -0.209 | 0.836 | -85.711 70.126 |
| C(dose)[T.1] | 77.5134 | 57.820 | 1.341 | 0.196 | -43.505 198.532 |
| expression | 13.9796 | 8.295 | 1.685 | 0.108 | -3.381 31.340 |
| expression:C(dose)[T.1] | -6.0614 | 12.370 | -0.490 | 0.630 | -31.952 19.829 |
| Omnibus: | 0.034 | Durbin-Watson: | 1.794 |
| Prob(Omnibus): | 0.983 | Jarque-Bera (JB): | 0.123 |
| Skew: | -0.064 | Prob(JB): | 0.940 |
| Kurtosis: | 2.665 | Cond. No. | 86.5 |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.701 |
| Model: | OLS | Adj. R-squared: | 0.671 |
| Method: | Least Squares | F-statistic: | 23.45 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.70e-06 |
| Time: | 03:35:16 | Log-Likelihood: | -99.219 |
| No. Observations: | 23 | AIC: | 204.4 |
| Df Residuals: | 20 | BIC: | 207.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 4.2947 | 27.347 | 0.157 | 0.877 | -52.750 61.340 |
| C(dose)[T.1] | 49.4901 | 8.353 | 5.925 | 0.000 | 32.066 66.915 |
| expression | 11.2542 | 6.035 | 1.865 | 0.077 | -1.336 23.844 |
| Omnibus: | 0.039 | Durbin-Watson: | 1.912 |
| Prob(Omnibus): | 0.980 | Jarque-Bera (JB): | 0.078 |
| Skew: | 0.026 | Prob(JB): | 0.962 |
| Kurtosis: | 2.720 | Cond. No. | 33.1 |
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: | 03:35:16 | 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: | 0.176 |
| Model: | OLS | Adj. R-squared: | 0.137 |
| Method: | Least Squares | F-statistic: | 4.495 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0461 |
| Time: | 03:35:16 | Log-Likelihood: | -110.87 |
| No. Observations: | 23 | AIC: | 225.7 |
| Df Residuals: | 21 | BIC: | 228.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -12.6496 | 44.055 | -0.287 | 0.777 | -104.267 78.968 |
| expression | 20.0859 | 9.474 | 2.120 | 0.046 | 0.384 39.787 |
| Omnibus: | 1.403 | Durbin-Watson: | 2.090 |
| Prob(Omnibus): | 0.496 | Jarque-Bera (JB): | 1.262 |
| Skew: | 0.458 | Prob(JB): | 0.532 |
| Kurtosis: | 2.309 | Cond. No. | 32.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.565 | 0.467 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.330 |
| Method: | Least Squares | F-statistic: | 3.303 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0613 |
| Time: | 03:35:16 | Log-Likelihood: | -70.483 |
| No. Observations: | 15 | AIC: | 149.0 |
| Df Residuals: | 11 | BIC: | 151.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 22.7529 | 79.576 | 0.286 | 0.780 | -152.392 197.898 |
| C(dose)[T.1] | 37.0734 | 149.494 | 0.248 | 0.809 | -291.960 366.107 |
| expression | 10.1173 | 17.824 | 0.568 | 0.582 | -29.113 49.348 |
| expression:C(dose)[T.1] | 2.8253 | 33.809 | 0.084 | 0.935 | -71.588 77.239 |
| Omnibus: | 3.803 | Durbin-Watson: | 0.885 |
| Prob(Omnibus): | 0.149 | Jarque-Bera (JB): | 2.231 |
| Skew: | -0.945 | Prob(JB): | 0.328 |
| Kurtosis: | 3.031 | Cond. No. | 105. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.474 |
| Model: | OLS | Adj. R-squared: | 0.386 |
| Method: | Least Squares | F-statistic: | 5.397 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0213 |
| Time: | 03:35:16 | Log-Likelihood: | -70.488 |
| No. Observations: | 15 | AIC: | 147.0 |
| Df Residuals: | 12 | BIC: | 149.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 19.2855 | 65.031 | 0.297 | 0.772 | -122.405 160.976 |
| C(dose)[T.1] | 49.4936 | 15.387 | 3.217 | 0.007 | 15.969 83.019 |
| expression | 10.9026 | 14.506 | 0.752 | 0.467 | -20.703 42.508 |
| Omnibus: | 3.515 | Durbin-Watson: | 0.898 |
| Prob(Omnibus): | 0.172 | Jarque-Bera (JB): | 2.114 |
| Skew: | -0.919 | Prob(JB): | 0.347 |
| Kurtosis: | 2.945 | Cond. No. | 39.7 |
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: | 03:35:16 | 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: | 0.020 |
| Model: | OLS | Adj. R-squared: | -0.056 |
| Method: | Least Squares | F-statistic: | 0.2605 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.618 |
| Time: | 03:35:16 | Log-Likelihood: | -75.151 |
| No. Observations: | 15 | AIC: | 154.3 |
| Df Residuals: | 13 | BIC: | 155.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 50.9592 | 84.279 | 0.605 | 0.556 | -131.114 233.033 |
| expression | 9.7036 | 19.012 | 0.510 | 0.618 | -31.370 50.777 |
| Omnibus: | 1.218 | Durbin-Watson: | 1.591 |
| Prob(Omnibus): | 0.544 | Jarque-Bera (JB): | 0.772 |
| Skew: | 0.061 | Prob(JB): | 0.680 |
| Kurtosis: | 1.895 | Cond. No. | 39.0 |