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 |
| 4.732 | 0.042 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.716 |
| Model: | OLS | Adj. R-squared: | 0.671 |
| Method: | Least Squares | F-statistic: | 15.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.98e-05 |
| Time: | 03:32:18 | Log-Likelihood: | -98.620 |
| No. Observations: | 23 | AIC: | 205.2 |
| Df Residuals: | 19 | BIC: | 209.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 156.8878 | 75.778 | 2.070 | 0.052 | -1.716 315.492 |
| C(dose)[T.1] | 51.0788 | 97.880 | 0.522 | 0.608 | -153.787 255.945 |
| expression | -16.2697 | 11.974 | -1.359 | 0.190 | -41.332 8.793 |
| expression:C(dose)[T.1] | -0.5208 | 15.803 | -0.033 | 0.974 | -33.597 32.555 |
| Omnibus: | 0.260 | Durbin-Watson: | 1.620 |
| Prob(Omnibus): | 0.878 | Jarque-Bera (JB): | 0.447 |
| Skew: | -0.061 | Prob(JB): | 0.800 |
| Kurtosis: | 2.328 | Cond. No. | 206. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.716 |
| Model: | OLS | Adj. R-squared: | 0.688 |
| Method: | Least Squares | F-statistic: | 25.24 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.39e-06 |
| Time: | 03:32:18 | Log-Likelihood: | -98.620 |
| No. Observations: | 23 | AIC: | 203.2 |
| Df Residuals: | 20 | BIC: | 206.6 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 158.7750 | 48.377 | 3.282 | 0.004 | 57.863 259.687 |
| C(dose)[T.1] | 47.8650 | 8.278 | 5.782 | 0.000 | 30.598 65.132 |
| expression | -16.5687 | 7.616 | -2.175 | 0.042 | -32.456 -0.681 |
| Omnibus: | 0.283 | Durbin-Watson: | 1.618 |
| Prob(Omnibus): | 0.868 | Jarque-Bera (JB): | 0.462 |
| Skew: | -0.068 | Prob(JB): | 0.794 |
| Kurtosis: | 2.320 | Cond. No. | 78.1 |
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: | 03:32:18 | 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: | 0.242 |
| Model: | OLS | Adj. R-squared: | 0.206 |
| Method: | Least Squares | F-statistic: | 6.696 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0172 |
| Time: | 03:32:18 | Log-Likelihood: | -109.92 |
| No. Observations: | 23 | AIC: | 223.8 |
| Df Residuals: | 21 | BIC: | 226.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 264.0161 | 71.499 | 3.693 | 0.001 | 115.326 412.707 |
| expression | -29.9521 | 11.575 | -2.588 | 0.017 | -54.024 -5.881 |
| Omnibus: | 0.608 | Durbin-Watson: | 2.492 |
| Prob(Omnibus): | 0.738 | Jarque-Bera (JB): | 0.601 |
| Skew: | 0.333 | Prob(JB): | 0.740 |
| Kurtosis: | 2.572 | Cond. No. | 72.1 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 3.384 | 0.091 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.591 |
| Model: | OLS | Adj. R-squared: | 0.480 |
| Method: | Least Squares | F-statistic: | 5.308 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0166 |
| Time: | 03:32:18 | Log-Likelihood: | -68.586 |
| No. Observations: | 15 | AIC: | 145.2 |
| Df Residuals: | 11 | BIC: | 148.0 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 142.5559 | 96.927 | 1.471 | 0.169 | -70.778 355.890 |
| C(dose)[T.1] | 157.2097 | 140.922 | 1.116 | 0.288 | -152.957 467.376 |
| expression | -13.4385 | 17.239 | -0.780 | 0.452 | -51.381 24.504 |
| expression:C(dose)[T.1] | -18.9261 | 24.921 | -0.759 | 0.464 | -73.777 35.925 |
| Omnibus: | 2.050 | Durbin-Watson: | 1.072 |
| Prob(Omnibus): | 0.359 | Jarque-Bera (JB): | 1.068 |
| Skew: | -0.262 | Prob(JB): | 0.586 |
| Kurtosis: | 1.802 | Cond. No. | 153. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.570 |
| Model: | OLS | Adj. R-squared: | 0.498 |
| Method: | Least Squares | F-statistic: | 7.955 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00632 |
| Time: | 03:32:19 | Log-Likelihood: | -68.970 |
| No. Observations: | 15 | AIC: | 143.9 |
| Df Residuals: | 12 | BIC: | 146.1 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 193.1851 | 69.107 | 2.795 | 0.016 | 42.614 343.756 |
| C(dose)[T.1] | 50.7311 | 13.926 | 3.643 | 0.003 | 20.389 81.073 |
| expression | -22.4949 | 12.228 | -1.840 | 0.091 | -49.136 4.147 |
| Omnibus: | 2.842 | Durbin-Watson: | 1.309 |
| Prob(Omnibus): | 0.241 | Jarque-Bera (JB): | 1.402 |
| Skew: | -0.414 | Prob(JB): | 0.496 |
| Kurtosis: | 1.752 | Cond. No. | 58.3 |
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: | 03:32:19 | 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: | 0.095 |
| Model: | OLS | Adj. R-squared: | 0.025 |
| Method: | Least Squares | F-statistic: | 1.357 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.265 |
| Time: | 03:32:19 | Log-Likelihood: | -74.555 |
| No. Observations: | 15 | AIC: | 153.1 |
| Df Residuals: | 13 | BIC: | 154.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 205.2281 | 96.242 | 2.132 | 0.053 | -2.689 413.145 |
| expression | -19.8267 | 17.018 | -1.165 | 0.265 | -56.591 16.937 |
| Omnibus: | 2.065 | Durbin-Watson: | 2.054 |
| Prob(Omnibus): | 0.356 | Jarque-Bera (JB): | 0.981 |
| Skew: | 0.118 | Prob(JB): | 0.612 |
| Kurtosis: | 1.770 | Cond. No. | 58.0 |