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 |
| 0.088 | 0.770 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.603 |
| Method: | Least Squares | F-statistic: | 12.13 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.000116 |
| Time: | 23:03:53 | Log-Likelihood: | -100.80 |
| No. Observations: | 23 | AIC: | 209.6 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 12.5613 | 155.897 | 0.081 | 0.937 | -313.735 338.857 |
| C(dose)[T.1] | 173.1284 | 202.603 | 0.855 | 0.403 | -250.924 597.181 |
| expression | 7.9029 | 29.560 | 0.267 | 0.792 | -53.967 69.772 |
| expression:C(dose)[T.1] | -22.8974 | 38.584 | -0.593 | 0.560 | -103.656 57.861 |
| Omnibus: | 1.242 | Durbin-Watson: | 1.931 |
| Prob(Omnibus): | 0.537 | Jarque-Bera (JB): | 0.941 |
| Skew: | 0.205 | Prob(JB): | 0.625 |
| Kurtosis: | 2.098 | Cond. No. | 334. |
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, 03 Apr 2025 | Prob (F-statistic): | 2.71e-05 |
| Time: | 23:03:53 | 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 | 83.3826 | 98.670 | 0.845 | 0.408 | -122.439 289.204 |
| C(dose)[T.1] | 53.0143 | 8.818 | 6.012 | 0.000 | 34.620 71.409 |
| expression | -5.5361 | 18.688 | -0.296 | 0.770 | -44.519 33.447 |
| Omnibus: | 0.414 | Durbin-Watson: | 1.871 |
| Prob(Omnibus): | 0.813 | Jarque-Bera (JB): | 0.548 |
| Skew: | 0.141 | Prob(JB): | 0.760 |
| Kurtosis: | 2.298 | Cond. No. | 123. |
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, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:03:53 | 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: | 0.019 |
| Model: | OLS | Adj. R-squared: | -0.028 |
| Method: | Least Squares | F-statistic: | 0.4102 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.529 |
| Time: | 23:03:53 | Log-Likelihood: | -112.88 |
| No. Observations: | 23 | AIC: | 229.8 |
| Df Residuals: | 21 | BIC: | 232.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 181.5172 | 159.109 | 1.141 | 0.267 | -149.368 512.402 |
| expression | -19.4202 | 30.322 | -0.640 | 0.529 | -82.479 43.639 |
| Omnibus: | 2.272 | Durbin-Watson: | 2.512 |
| Prob(Omnibus): | 0.321 | Jarque-Bera (JB): | 1.349 |
| Skew: | 0.297 | Prob(JB): | 0.510 |
| Kurtosis: | 1.973 | Cond. No. | 121. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 2.644 | 0.130 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.558 |
| Model: | OLS | Adj. R-squared: | 0.438 |
| Method: | Least Squares | F-statistic: | 4.630 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0250 |
| Time: | 23:03:53 | Log-Likelihood: | -69.176 |
| No. Observations: | 15 | AIC: | 146.4 |
| Df Residuals: | 11 | BIC: | 149.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -249.2709 | 234.664 | -1.062 | 0.311 | -765.762 267.220 |
| C(dose)[T.1] | 181.7307 | 305.106 | 0.596 | 0.563 | -489.803 853.265 |
| expression | 55.7108 | 41.236 | 1.351 | 0.204 | -35.050 146.472 |
| expression:C(dose)[T.1] | -25.6382 | 52.073 | -0.492 | 0.632 | -140.250 88.973 |
| Omnibus: | 0.128 | Durbin-Watson: | 0.958 |
| Prob(Omnibus): | 0.938 | Jarque-Bera (JB): | 0.297 |
| Skew: | -0.168 | Prob(JB): | 0.862 |
| Kurtosis: | 2.398 | Cond. No. | 357. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.548 |
| Model: | OLS | Adj. R-squared: | 0.473 |
| Method: | Least Squares | F-statistic: | 7.283 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00849 |
| Time: | 23:03:53 | Log-Likelihood: | -69.340 |
| No. Observations: | 15 | AIC: | 144.7 |
| Df Residuals: | 12 | BIC: | 146.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -157.8731 | 138.945 | -1.136 | 0.278 | -460.609 144.863 |
| C(dose)[T.1] | 31.7847 | 17.823 | 1.783 | 0.100 | -7.049 70.618 |
| expression | 39.6329 | 24.373 | 1.626 | 0.130 | -13.472 92.738 |
| Omnibus: | 0.034 | Durbin-Watson: | 0.931 |
| Prob(Omnibus): | 0.983 | Jarque-Bera (JB): | 0.259 |
| Skew: | 0.022 | Prob(JB): | 0.879 |
| Kurtosis: | 2.358 | Cond. No. | 120. |
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, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:03:53 | 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: | 0.429 |
| Model: | OLS | Adj. R-squared: | 0.385 |
| Method: | Least Squares | F-statistic: | 9.751 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00809 |
| Time: | 23:03:54 | Log-Likelihood: | -71.103 |
| No. Observations: | 15 | AIC: | 146.2 |
| Df Residuals: | 13 | BIC: | 147.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -295.4882 | 124.861 | -2.367 | 0.034 | -565.234 -25.743 |
| expression | 65.7466 | 21.055 | 3.123 | 0.008 | 20.260 111.233 |
| Omnibus: | 5.394 | Durbin-Watson: | 1.221 |
| Prob(Omnibus): | 0.067 | Jarque-Bera (JB): | 3.127 |
| Skew: | 1.106 | Prob(JB): | 0.209 |
| Kurtosis: | 3.332 | Cond. No. | 99.1 |