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 |
| 0.072 | 0.791 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.651 |
| Model: | OLS | Adj. R-squared: | 0.596 |
| Method: | Least Squares | F-statistic: | 11.82 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000135 |
| Time: | 05:01:06 | Log-Likelihood: | -101.00 |
| No. Observations: | 23 | AIC: | 210.0 |
| Df Residuals: | 19 | BIC: | 214.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -3.3897 | 172.649 | -0.020 | 0.985 | -364.748 357.969 |
| C(dose)[T.1] | 107.8403 | 259.524 | 0.416 | 0.682 | -435.350 651.031 |
| expression | 6.2785 | 18.808 | 0.334 | 0.742 | -33.086 45.643 |
| expression:C(dose)[T.1] | -5.9329 | 28.661 | -0.207 | 0.838 | -65.921 54.055 |
| Omnibus: | 0.347 | Durbin-Watson: | 1.975 |
| Prob(Omnibus): | 0.841 | Jarque-Bera (JB): | 0.497 |
| Skew: | -0.006 | Prob(JB): | 0.780 |
| Kurtosis: | 2.280 | Cond. No. | 669. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.650 |
| Model: | OLS | Adj. R-squared: | 0.615 |
| Method: | Least Squares | F-statistic: | 18.60 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.73e-05 |
| Time: | 05:01:06 | Log-Likelihood: | -101.02 |
| 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 | 20.0472 | 127.184 | 0.158 | 0.876 | -245.254 285.348 |
| C(dose)[T.1] | 54.1541 | 9.266 | 5.844 | 0.000 | 34.825 73.483 |
| expression | 3.7238 | 13.848 | 0.269 | 0.791 | -25.163 32.610 |
| Omnibus: | 0.145 | Durbin-Watson: | 1.931 |
| Prob(Omnibus): | 0.930 | Jarque-Bera (JB): | 0.365 |
| Skew: | 0.005 | Prob(JB): | 0.833 |
| Kurtosis: | 2.383 | Cond. No. | 268. |
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: | 05:01:06 | 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: | 0.053 |
| Model: | OLS | Adj. R-squared: | 0.008 |
| Method: | Least Squares | F-statistic: | 1.179 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.290 |
| Time: | 05:01:06 | Log-Likelihood: | -112.48 |
| No. Observations: | 23 | AIC: | 229.0 |
| Df Residuals: | 21 | BIC: | 231.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 286.6031 | 190.654 | 1.503 | 0.148 | -109.885 683.091 |
| expression | -22.8128 | 21.009 | -1.086 | 0.290 | -66.503 20.877 |
| Omnibus: | 3.784 | Durbin-Watson: | 2.403 |
| Prob(Omnibus): | 0.151 | Jarque-Bera (JB): | 1.667 |
| Skew: | 0.293 | Prob(JB): | 0.435 |
| Kurtosis: | 1.818 | Cond. No. | 249. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.812 | 0.385 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.485 |
| Model: | OLS | Adj. R-squared: | 0.345 |
| Method: | Least Squares | F-statistic: | 3.453 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0550 |
| Time: | 05:01:06 | Log-Likelihood: | -70.323 |
| No. Observations: | 15 | AIC: | 148.6 |
| Df Residuals: | 11 | BIC: | 151.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 272.5040 | 237.510 | 1.147 | 0.276 | -250.253 795.261 |
| C(dose)[T.1] | -60.1977 | 639.020 | -0.094 | 0.927 | -1466.671 1346.276 |
| expression | -19.8712 | 22.987 | -0.864 | 0.406 | -70.464 30.722 |
| expression:C(dose)[T.1] | 10.4286 | 62.887 | 0.166 | 0.871 | -127.985 148.842 |
| Omnibus: | 1.864 | Durbin-Watson: | 0.872 |
| Prob(Omnibus): | 0.394 | Jarque-Bera (JB): | 1.465 |
| Skew: | -0.675 | Prob(JB): | 0.481 |
| Kurtosis: | 2.279 | Cond. No. | 973. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.484 |
| Model: | OLS | Adj. R-squared: | 0.398 |
| Method: | Least Squares | F-statistic: | 5.621 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0189 |
| Time: | 05:01:06 | Log-Likelihood: | -70.342 |
| No. Observations: | 15 | AIC: | 146.7 |
| Df Residuals: | 12 | BIC: | 148.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 258.1247 | 211.967 | 1.218 | 0.247 | -203.712 719.961 |
| C(dose)[T.1] | 45.7368 | 15.710 | 2.911 | 0.013 | 11.509 79.965 |
| expression | -18.4778 | 20.511 | -0.901 | 0.385 | -63.167 26.211 |
| Omnibus: | 1.935 | Durbin-Watson: | 0.866 |
| Prob(Omnibus): | 0.380 | Jarque-Bera (JB): | 1.475 |
| Skew: | -0.622 | Prob(JB): | 0.478 |
| Kurtosis: | 2.099 | Cond. No. | 288. |
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: | 05:01:06 | 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: | 0.119 |
| Model: | OLS | Adj. R-squared: | 0.051 |
| Method: | Least Squares | F-statistic: | 1.756 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.208 |
| Time: | 05:01:06 | Log-Likelihood: | -74.350 |
| No. Observations: | 15 | AIC: | 152.7 |
| Df Residuals: | 13 | BIC: | 154.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 431.7083 | 255.284 | 1.691 | 0.115 | -119.800 983.216 |
| expression | -33.0752 | 24.960 | -1.325 | 0.208 | -86.999 20.849 |
| Omnibus: | 2.599 | Durbin-Watson: | 1.566 |
| Prob(Omnibus): | 0.273 | Jarque-Bera (JB): | 1.159 |
| Skew: | 0.242 | Prob(JB): | 0.560 |
| Kurtosis: | 1.727 | Cond. No. | 276. |