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 |
| 4.856 | 0.039 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.719 |
| Model: | OLS | Adj. R-squared: | 0.675 |
| Method: | Least Squares | F-statistic: | 16.21 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.80e-05 |
| Time: | 06:19:45 | Log-Likelihood: | -98.503 |
| No. Observations: | 23 | AIC: | 205.0 |
| Df Residuals: | 19 | BIC: | 209.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 187.7185 | 63.968 | 2.935 | 0.009 | 53.831 321.606 |
| C(dose)[T.1] | 1.7712 | 153.124 | 0.012 | 0.991 | -318.721 322.263 |
| expression | -20.3065 | 9.692 | -2.095 | 0.050 | -40.593 -0.020 |
| expression:C(dose)[T.1] | 7.5024 | 23.784 | 0.315 | 0.756 | -42.278 57.282 |
| Omnibus: | 0.088 | Durbin-Watson: | 1.122 |
| Prob(Omnibus): | 0.957 | Jarque-Bera (JB): | 0.201 |
| Skew: | -0.125 | Prob(JB): | 0.904 |
| Kurtosis: | 2.616 | Cond. No. | 291. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.718 |
| Model: | OLS | Adj. R-squared: | 0.689 |
| Method: | Least Squares | F-statistic: | 25.41 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.22e-06 |
| Time: | 06:19:45 | Log-Likelihood: | -98.563 |
| No. Observations: | 23 | AIC: | 203.1 |
| Df Residuals: | 20 | BIC: | 206.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 179.5265 | 57.128 | 3.143 | 0.005 | 60.359 298.694 |
| C(dose)[T.1] | 50.0038 | 8.011 | 6.242 | 0.000 | 33.294 66.714 |
| expression | -19.0605 | 8.650 | -2.204 | 0.039 | -37.103 -1.018 |
| Omnibus: | 0.068 | Durbin-Watson: | 1.166 |
| Prob(Omnibus): | 0.967 | Jarque-Bera (JB): | 0.117 |
| Skew: | -0.088 | Prob(JB): | 0.943 |
| Kurtosis: | 2.698 | Cond. No. | 97.1 |
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: | 06:19:45 | 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: | 0.167 |
| Model: | OLS | Adj. R-squared: | 0.128 |
| Method: | Least Squares | F-statistic: | 4.225 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0525 |
| Time: | 06:19:45 | Log-Likelihood: | -111.00 |
| No. Observations: | 23 | AIC: | 226.0 |
| Df Residuals: | 21 | BIC: | 228.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 269.6172 | 92.621 | 2.911 | 0.008 | 77.000 462.234 |
| expression | -29.2553 | 14.233 | -2.055 | 0.052 | -58.854 0.344 |
| Omnibus: | 1.903 | Durbin-Watson: | 2.242 |
| Prob(Omnibus): | 0.386 | Jarque-Bera (JB): | 1.485 |
| Skew: | 0.449 | Prob(JB): | 0.476 |
| Kurtosis: | 2.139 | Cond. No. | 93.7 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.330 | 0.576 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.655 |
| Model: | OLS | Adj. R-squared: | 0.561 |
| Method: | Least Squares | F-statistic: | 6.972 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00678 |
| Time: | 06:19:45 | Log-Likelihood: | -67.311 |
| No. Observations: | 15 | AIC: | 142.6 |
| Df Residuals: | 11 | BIC: | 145.5 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 597.3584 | 366.951 | 1.628 | 0.132 | -210.294 1405.011 |
| C(dose)[T.1] | -1135.1999 | 479.364 | -2.368 | 0.037 | -2190.273 -80.127 |
| expression | -64.6021 | 44.719 | -1.445 | 0.176 | -163.028 33.823 |
| expression:C(dose)[T.1] | 145.0004 | 58.603 | 2.474 | 0.031 | 16.016 273.985 |
| Omnibus: | 0.278 | Durbin-Watson: | 1.206 |
| Prob(Omnibus): | 0.870 | Jarque-Bera (JB): | 0.373 |
| Skew: | -0.258 | Prob(JB): | 0.830 |
| Kurtosis: | 2.425 | Cond. No. | 845. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.464 |
| Model: | OLS | Adj. R-squared: | 0.374 |
| Method: | Least Squares | F-statistic: | 5.184 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0238 |
| Time: | 06:19:45 | Log-Likelihood: | -70.630 |
| No. Observations: | 15 | AIC: | 147.3 |
| Df Residuals: | 12 | BIC: | 149.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -95.2431 | 283.422 | -0.336 | 0.743 | -712.766 522.280 |
| C(dose)[T.1] | 50.4393 | 15.678 | 3.217 | 0.007 | 16.281 84.598 |
| expression | 19.8308 | 34.523 | 0.574 | 0.576 | -55.389 95.051 |
| Omnibus: | 2.755 | Durbin-Watson: | 0.653 |
| Prob(Omnibus): | 0.252 | Jarque-Bera (JB): | 2.010 |
| Skew: | -0.859 | Prob(JB): | 0.366 |
| Kurtosis: | 2.482 | Cond. No. | 304. |
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: | 06:19:45 | 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: | 0.001 |
| Model: | OLS | Adj. R-squared: | -0.076 |
| Method: | Least Squares | F-statistic: | 0.01008 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.922 |
| Time: | 06:19:45 | Log-Likelihood: | -75.294 |
| No. Observations: | 15 | AIC: | 154.6 |
| Df Residuals: | 13 | BIC: | 156.0 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 56.8890 | 366.419 | 0.155 | 0.879 | -734.712 848.490 |
| expression | 4.5018 | 44.835 | 0.100 | 0.922 | -92.357 101.361 |
| Omnibus: | 0.483 | Durbin-Watson: | 1.616 |
| Prob(Omnibus): | 0.785 | Jarque-Bera (JB): | 0.533 |
| Skew: | -0.001 | Prob(JB): | 0.766 |
| Kurtosis: | 2.077 | Cond. No. | 299. |