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 |
| 0.381 | 0.544 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.601 |
| Method: | Least Squares | F-statistic: | 12.07 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000119 |
| Time: | 04:42:14 | Log-Likelihood: | -100.84 |
| No. Observations: | 23 | AIC: | 209.7 |
| Df Residuals: | 19 | BIC: | 214.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 93.6362 | 149.349 | 0.627 | 0.538 | -218.955 406.228 |
| C(dose)[T.1] | 63.6714 | 174.806 | 0.364 | 0.720 | -302.202 429.545 |
| expression | -5.6295 | 21.306 | -0.264 | 0.794 | -50.223 38.964 |
| expression:C(dose)[T.1] | -2.4495 | 25.891 | -0.095 | 0.926 | -56.640 51.741 |
| Omnibus: | 0.026 | Durbin-Watson: | 1.962 |
| Prob(Omnibus): | 0.987 | Jarque-Bera (JB): | 0.230 |
| Skew: | -0.033 | Prob(JB): | 0.891 |
| Kurtosis: | 2.514 | Cond. No. | 372. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.656 |
| Model: | OLS | Adj. R-squared: | 0.621 |
| Method: | Least Squares | F-statistic: | 19.04 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.35e-05 |
| Time: | 04:42:14 | Log-Likelihood: | -100.85 |
| 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 | 105.2533 | 82.877 | 1.270 | 0.219 | -67.625 278.131 |
| C(dose)[T.1] | 47.1835 | 13.220 | 3.569 | 0.002 | 19.607 74.760 |
| expression | -7.2881 | 11.802 | -0.618 | 0.544 | -31.906 17.330 |
| Omnibus: | 0.021 | Durbin-Watson: | 2.005 |
| Prob(Omnibus): | 0.990 | Jarque-Bera (JB): | 0.224 |
| Skew: | -0.021 | Prob(JB): | 0.894 |
| Kurtosis: | 2.518 | Cond. No. | 130. |
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: | 04:42:14 | 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: | 0.436 |
| Model: | OLS | Adj. R-squared: | 0.409 |
| Method: | Least Squares | F-statistic: | 16.25 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000603 |
| Time: | 04:42:14 | Log-Likelihood: | -106.51 |
| No. Observations: | 23 | AIC: | 217.0 |
| Df Residuals: | 21 | BIC: | 219.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 337.3726 | 64.141 | 5.260 | 0.000 | 203.985 470.761 |
| expression | -39.0385 | 9.684 | -4.031 | 0.001 | -59.176 -18.901 |
| Omnibus: | 0.603 | Durbin-Watson: | 2.252 |
| Prob(Omnibus): | 0.740 | Jarque-Bera (JB): | 0.594 |
| Skew: | 0.332 | Prob(JB): | 0.743 |
| Kurtosis: | 2.577 | Cond. No. | 80.2 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.322 | 0.093 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.569 |
| Model: | OLS | Adj. R-squared: | 0.451 |
| Method: | Least Squares | F-statistic: | 4.832 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0221 |
| Time: | 04:42:14 | Log-Likelihood: | -68.996 |
| No. Observations: | 15 | AIC: | 146.0 |
| Df Residuals: | 11 | BIC: | 148.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 227.8375 | 205.206 | 1.110 | 0.291 | -223.817 679.492 |
| C(dose)[T.1] | 71.0752 | 236.263 | 0.301 | 0.769 | -448.937 591.087 |
| expression | -26.1368 | 33.391 | -0.783 | 0.450 | -99.630 47.357 |
| expression:C(dose)[T.1] | -3.1902 | 38.305 | -0.083 | 0.935 | -87.500 81.119 |
| Omnibus: | 0.547 | Durbin-Watson: | 1.417 |
| Prob(Omnibus): | 0.761 | Jarque-Bera (JB): | 0.604 |
| Skew: | -0.258 | Prob(JB): | 0.739 |
| Kurtosis: | 2.163 | Cond. No. | 309. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.568 |
| Model: | OLS | Adj. R-squared: | 0.496 |
| Method: | Least Squares | F-statistic: | 7.898 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00647 |
| Time: | 04:42:14 | Log-Likelihood: | -69.000 |
| No. Observations: | 15 | AIC: | 144.0 |
| Df Residuals: | 12 | BIC: | 146.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 242.7152 | 96.712 | 2.510 | 0.027 | 31.997 453.433 |
| C(dose)[T.1] | 51.4359 | 13.983 | 3.678 | 0.003 | 20.969 81.903 |
| expression | -28.5610 | 15.671 | -1.823 | 0.093 | -62.705 5.583 |
| Omnibus: | 0.608 | Durbin-Watson: | 1.433 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.633 |
| Skew: | -0.255 | Prob(JB): | 0.729 |
| Kurtosis: | 2.132 | Cond. No. | 88.8 |
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: | 04:42:14 | 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: | 0.082 |
| Model: | OLS | Adj. R-squared: | 0.011 |
| Method: | Least Squares | F-statistic: | 1.154 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.302 |
| Time: | 04:42:14 | Log-Likelihood: | -74.662 |
| No. Observations: | 15 | AIC: | 153.3 |
| Df Residuals: | 13 | BIC: | 154.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 238.8492 | 135.523 | 1.762 | 0.101 | -53.930 531.628 |
| expression | -23.4957 | 21.876 | -1.074 | 0.302 | -70.755 23.764 |
| Omnibus: | 0.355 | Durbin-Watson: | 1.765 |
| Prob(Omnibus): | 0.837 | Jarque-Bera (JB): | 0.490 |
| Skew: | 0.195 | Prob(JB): | 0.783 |
| Kurtosis: | 2.204 | Cond. No. | 88.4 |