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 |
| 0.091 | 0.766 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.653 |
| Model: | OLS | Adj. R-squared: | 0.599 |
| Method: | Least Squares | F-statistic: | 11.94 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000127 |
| Time: | 04:14:13 | Log-Likelihood: | -100.92 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 28.8561 | 54.847 | 0.526 | 0.605 | -85.941 143.653 |
| C(dose)[T.1] | 88.6620 | 88.658 | 1.000 | 0.330 | -96.901 274.225 |
| expression | 4.3209 | 9.288 | 0.465 | 0.647 | -15.120 23.762 |
| expression:C(dose)[T.1] | -6.1951 | 16.005 | -0.387 | 0.703 | -39.695 27.304 |
| Omnibus: | 0.202 | Durbin-Watson: | 1.971 |
| Prob(Omnibus): | 0.904 | Jarque-Bera (JB): | 0.407 |
| Skew: | 0.009 | Prob(JB): | 0.816 |
| Kurtosis: | 2.348 | Cond. No. | 139. |
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: | 18.62 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.71e-05 |
| Time: | 04:14:13 | Log-Likelihood: | -101.01 |
| 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 | 41.0976 | 43.848 | 0.937 | 0.360 | -50.368 132.563 |
| C(dose)[T.1] | 54.5579 | 9.639 | 5.660 | 0.000 | 34.451 74.665 |
| expression | 2.2345 | 7.402 | 0.302 | 0.766 | -13.205 17.674 |
| Omnibus: | 0.486 | Durbin-Watson: | 1.985 |
| Prob(Omnibus): | 0.784 | Jarque-Bera (JB): | 0.574 |
| Skew: | 0.063 | Prob(JB): | 0.751 |
| Kurtosis: | 2.237 | Cond. No. | 58.8 |
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: | 04:14:13 | 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: | 0.091 |
| Model: | OLS | Adj. R-squared: | 0.048 |
| Method: | Least Squares | F-statistic: | 2.104 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.162 |
| Time: | 04:14:13 | Log-Likelihood: | -112.01 |
| No. Observations: | 23 | AIC: | 228.0 |
| Df Residuals: | 21 | BIC: | 230.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 165.7196 | 59.689 | 2.776 | 0.011 | 41.589 289.851 |
| expression | -15.3410 | 10.576 | -1.450 | 0.162 | -37.336 6.654 |
| Omnibus: | 2.110 | Durbin-Watson: | 1.960 |
| Prob(Omnibus): | 0.348 | Jarque-Bera (JB): | 1.110 |
| Skew: | 0.089 | Prob(JB): | 0.574 |
| Kurtosis: | 1.939 | Cond. No. | 50.5 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.221 | 0.647 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.502 |
| Model: | OLS | Adj. R-squared: | 0.366 |
| Method: | Least Squares | F-statistic: | 3.695 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0464 |
| Time: | 04:14:13 | Log-Likelihood: | -70.072 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 151.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -128.3083 | 181.374 | -0.707 | 0.494 | -527.511 270.894 |
| C(dose)[T.1] | 250.8321 | 200.804 | 1.249 | 0.238 | -191.135 692.799 |
| expression | 29.3439 | 27.137 | 1.081 | 0.303 | -30.384 89.072 |
| expression:C(dose)[T.1] | -30.3955 | 31.125 | -0.977 | 0.350 | -98.901 38.110 |
| Omnibus: | 2.720 | Durbin-Watson: | 1.154 |
| Prob(Omnibus): | 0.257 | Jarque-Bera (JB): | 1.704 |
| Skew: | -0.819 | Prob(JB): | 0.426 |
| Kurtosis: | 2.787 | Cond. No. | 239. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 5.085 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0251 |
| Time: | 04:14:13 | Log-Likelihood: | -70.696 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 25.8132 | 89.210 | 0.289 | 0.777 | -168.558 220.185 |
| C(dose)[T.1] | 55.8148 | 21.006 | 2.657 | 0.021 | 10.046 101.583 |
| expression | 6.2388 | 13.264 | 0.470 | 0.647 | -22.662 35.139 |
| Omnibus: | 3.030 | Durbin-Watson: | 0.778 |
| Prob(Omnibus): | 0.220 | Jarque-Bera (JB): | 2.135 |
| Skew: | -0.900 | Prob(JB): | 0.344 |
| Kurtosis: | 2.583 | Cond. No. | 73.6 |
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: | 04:14:13 | 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: | 0.140 |
| Model: | OLS | Adj. R-squared: | 0.074 |
| Method: | Least Squares | F-statistic: | 2.122 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.169 |
| Time: | 04:14:13 | Log-Likelihood: | -74.166 |
| No. Observations: | 15 | AIC: | 152.3 |
| Df Residuals: | 13 | BIC: | 153.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 199.7085 | 73.405 | 2.721 | 0.017 | 41.126 358.291 |
| expression | -17.3707 | 11.925 | -1.457 | 0.169 | -43.133 8.392 |
| Omnibus: | 0.377 | Durbin-Watson: | 1.358 |
| Prob(Omnibus): | 0.828 | Jarque-Bera (JB): | 0.018 |
| Skew: | 0.074 | Prob(JB): | 0.991 |
| Kurtosis: | 2.915 | Cond. No. | 49.2 |