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 |
| 1.045 | 0.319 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.682 |
| Model: | OLS | Adj. R-squared: | 0.631 |
| Method: | Least Squares | F-statistic: | 13.56 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 5.78e-05 |
| Time: | 05:15:39 | Log-Likelihood: | -99.942 |
| No. Observations: | 23 | AIC: | 207.9 |
| Df Residuals: | 19 | BIC: | 212.4 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 216.8318 | 116.787 | 1.857 | 0.079 | -27.606 461.270 |
| C(dose)[T.1] | -111.9866 | 175.612 | -0.638 | 0.531 | -479.546 255.572 |
| expression | -20.5543 | 14.742 | -1.394 | 0.179 | -51.409 10.301 |
| expression:C(dose)[T.1] | 20.8899 | 21.965 | 0.951 | 0.354 | -25.084 66.864 |
| Omnibus: | 0.180 | Durbin-Watson: | 1.590 |
| Prob(Omnibus): | 0.914 | Jarque-Bera (JB): | 0.390 |
| Skew: | -0.061 | Prob(JB): | 0.823 |
| Kurtosis: | 2.374 | Cond. No. | 422. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.666 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 19.98 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 1.70e-05 |
| Time: | 05:15:39 | Log-Likelihood: | -100.48 |
| No. Observations: | 23 | AIC: | 207.0 |
| Df Residuals: | 20 | BIC: | 210.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 142.3839 | 86.461 | 1.647 | 0.115 | -37.971 322.739 |
| C(dose)[T.1] | 54.8227 | 8.672 | 6.322 | 0.000 | 36.733 72.912 |
| expression | -11.1447 | 10.902 | -1.022 | 0.319 | -33.887 11.597 |
| Omnibus: | 1.170 | Durbin-Watson: | 1.838 |
| Prob(Omnibus): | 0.557 | Jarque-Bera (JB): | 0.859 |
| Skew: | 0.114 | Prob(JB): | 0.651 |
| Kurtosis: | 2.081 | Cond. No. | 165. |
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: | 05:15:39 | 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: | 0.000 |
| Model: | OLS | Adj. R-squared: | -0.048 |
| Method: | Least Squares | F-statistic: | 0.0004984 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.982 |
| Time: | 05:15:39 | Log-Likelihood: | -113.10 |
| No. Observations: | 23 | AIC: | 230.2 |
| Df Residuals: | 21 | BIC: | 232.5 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 76.4834 | 145.038 | 0.527 | 0.603 | -225.140 378.107 |
| expression | 0.4055 | 18.163 | 0.022 | 0.982 | -37.366 38.177 |
| Omnibus: | 3.321 | Durbin-Watson: | 2.486 |
| Prob(Omnibus): | 0.190 | Jarque-Bera (JB): | 1.567 |
| Skew: | 0.285 | Prob(JB): | 0.457 |
| Kurtosis: | 1.856 | Cond. No. | 163. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.043 | 0.840 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.482 |
| Model: | OLS | Adj. R-squared: | 0.341 |
| Method: | Least Squares | F-statistic: | 3.410 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0567 |
| Time: | 05:15:39 | Log-Likelihood: | -70.368 |
| No. Observations: | 15 | AIC: | 148.7 |
| Df Residuals: | 11 | BIC: | 151.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 106.9829 | 77.190 | 1.386 | 0.193 | -62.912 276.878 |
| C(dose)[T.1] | -98.1270 | 181.096 | -0.542 | 0.599 | -496.718 300.464 |
| expression | -5.7593 | 11.111 | -0.518 | 0.614 | -30.214 18.695 |
| expression:C(dose)[T.1] | 22.0964 | 27.156 | 0.814 | 0.433 | -37.674 81.867 |
| Omnibus: | 6.123 | Durbin-Watson: | 0.672 |
| Prob(Omnibus): | 0.047 | Jarque-Bera (JB): | 3.483 |
| Skew: | -1.148 | Prob(JB): | 0.175 |
| Kurtosis: | 3.546 | Cond. No. | 184. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.451 |
| Model: | OLS | Adj. R-squared: | 0.359 |
| Method: | Least Squares | F-statistic: | 4.923 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0275 |
| Time: | 05:15:39 | Log-Likelihood: | -70.806 |
| No. Observations: | 15 | AIC: | 147.6 |
| Df Residuals: | 12 | BIC: | 149.7 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 81.5796 | 69.594 | 1.172 | 0.264 | -70.052 233.211 |
| C(dose)[T.1] | 48.6374 | 15.944 | 3.050 | 0.010 | 13.898 83.377 |
| expression | -2.0605 | 9.995 | -0.206 | 0.840 | -23.837 19.716 |
| Omnibus: | 2.500 | Durbin-Watson: | 0.784 |
| Prob(Omnibus): | 0.287 | Jarque-Bera (JB): | 1.779 |
| Skew: | -0.812 | Prob(JB): | 0.411 |
| Kurtosis: | 2.543 | Cond. No. | 61.7 |
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: | 05:15:39 | 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: | 0.025 |
| Model: | OLS | Adj. R-squared: | -0.050 |
| Method: | Least Squares | F-statistic: | 0.3303 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.575 |
| Time: | 05:15:39 | Log-Likelihood: | -75.112 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 142.3826 | 85.361 | 1.668 | 0.119 | -42.028 326.793 |
| expression | -7.2460 | 12.608 | -0.575 | 0.575 | -34.485 19.993 |
| Omnibus: | 0.838 | Durbin-Watson: | 1.581 |
| Prob(Omnibus): | 0.658 | Jarque-Bera (JB): | 0.686 |
| Skew: | 0.155 | Prob(JB): | 0.710 |
| Kurtosis: | 1.999 | Cond. No. | 58.8 |