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 |
| 0.413 | 0.528 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.699 |
| Model: | OLS | Adj. R-squared: | 0.652 |
| Method: | Least Squares | F-statistic: | 14.74 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.38e-05 |
| Time: | 05:15:23 | Log-Likelihood: | -99.280 |
| No. Observations: | 23 | AIC: | 206.6 |
| Df Residuals: | 19 | BIC: | 211.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -80.0858 | 77.737 | -1.030 | 0.316 | -242.790 82.619 |
| C(dose)[T.1] | 210.1465 | 93.959 | 2.237 | 0.038 | 13.488 406.805 |
| expression | 28.7183 | 16.578 | 1.732 | 0.099 | -5.980 63.417 |
| expression:C(dose)[T.1] | -33.8859 | 20.485 | -1.654 | 0.115 | -76.762 8.990 |
| Omnibus: | 0.353 | Durbin-Watson: | 1.968 |
| Prob(Omnibus): | 0.838 | Jarque-Bera (JB): | 0.502 |
| Skew: | -0.042 | Prob(JB): | 0.778 |
| Kurtosis: | 2.281 | Cond. No. | 149. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.622 |
| Method: | Least Squares | F-statistic: | 19.08 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.31e-05 |
| Time: | 05:15:23 | Log-Likelihood: | -100.83 |
| No. Observations: | 23 | AIC: | 207.7 |
| Df Residuals: | 20 | BIC: | 211.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 23.6902 | 47.854 | 0.495 | 0.626 | -76.132 123.512 |
| C(dose)[T.1] | 55.4204 | 9.266 | 5.981 | 0.000 | 36.092 74.749 |
| expression | 6.5262 | 10.153 | 0.643 | 0.528 | -14.652 27.704 |
| Omnibus: | 0.033 | Durbin-Watson: | 1.968 |
| Prob(Omnibus): | 0.984 | Jarque-Bera (JB): | 0.226 |
| Skew: | 0.059 | Prob(JB): | 0.893 |
| Kurtosis: | 2.529 | Cond. No. | 52.9 |
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: | 05:15:23 | 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: | 0.041 |
| Model: | OLS | Adj. R-squared: | -0.005 |
| Method: | Least Squares | F-statistic: | 0.9010 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.353 |
| Time: | 05:15:23 | Log-Likelihood: | -112.62 |
| No. Observations: | 23 | AIC: | 229.2 |
| Df Residuals: | 21 | BIC: | 231.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 146.2732 | 70.473 | 2.076 | 0.050 | -0.284 292.830 |
| expression | -14.7131 | 15.501 | -0.949 | 0.353 | -46.948 17.522 |
| Omnibus: | 1.382 | Durbin-Watson: | 2.392 |
| Prob(Omnibus): | 0.501 | Jarque-Bera (JB): | 0.963 |
| Skew: | 0.175 | Prob(JB): | 0.618 |
| Kurtosis: | 2.060 | Cond. No. | 47.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.822 | 0.074 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.583 |
| Model: | OLS | Adj. R-squared: | 0.469 |
| Method: | Least Squares | F-statistic: | 5.125 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0185 |
| Time: | 05:15:23 | Log-Likelihood: | -68.741 |
| No. Observations: | 15 | AIC: | 145.5 |
| Df Residuals: | 11 | BIC: | 148.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 172.3100 | 62.322 | 2.765 | 0.018 | 35.140 309.480 |
| C(dose)[T.1] | 25.7205 | 120.785 | 0.213 | 0.835 | -240.126 291.567 |
| expression | -19.0666 | 11.169 | -1.707 | 0.116 | -43.650 5.517 |
| expression:C(dose)[T.1] | 3.6533 | 22.475 | 0.163 | 0.874 | -45.813 53.119 |
| Omnibus: | 2.003 | Durbin-Watson: | 1.010 |
| Prob(Omnibus): | 0.367 | Jarque-Bera (JB): | 1.111 |
| Skew: | -0.664 | Prob(JB): | 0.574 |
| Kurtosis: | 2.870 | Cond. No. | 114. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.582 |
| Model: | OLS | Adj. R-squared: | 0.512 |
| Method: | Least Squares | F-statistic: | 8.352 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00534 |
| Time: | 05:15:23 | Log-Likelihood: | -68.759 |
| No. Observations: | 15 | AIC: | 143.5 |
| Df Residuals: | 12 | BIC: | 145.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 167.3465 | 52.079 | 3.213 | 0.007 | 53.877 280.816 |
| C(dose)[T.1] | 45.2134 | 13.858 | 3.263 | 0.007 | 15.020 75.407 |
| expression | -18.1643 | 9.291 | -1.955 | 0.074 | -38.407 2.079 |
| Omnibus: | 1.643 | Durbin-Watson: | 0.950 |
| Prob(Omnibus): | 0.440 | Jarque-Bera (JB): | 0.929 |
| Skew: | -0.602 | Prob(JB): | 0.628 |
| Kurtosis: | 2.810 | Cond. No. | 43.0 |
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: | 05:15:23 | 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: | 0.211 |
| Model: | OLS | Adj. R-squared: | 0.150 |
| Method: | Least Squares | F-statistic: | 3.478 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0849 |
| Time: | 05:15:23 | Log-Likelihood: | -73.522 |
| No. Observations: | 15 | AIC: | 151.0 |
| Df Residuals: | 13 | BIC: | 152.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 215.4532 | 65.922 | 3.268 | 0.006 | 73.038 357.869 |
| expression | -22.6207 | 12.129 | -1.865 | 0.085 | -48.824 3.583 |
| Omnibus: | 2.611 | Durbin-Watson: | 1.694 |
| Prob(Omnibus): | 0.271 | Jarque-Bera (JB): | 1.590 |
| Skew: | 0.560 | Prob(JB): | 0.452 |
| Kurtosis: | 1.865 | Cond. No. | 41.0 |