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 |
| 0.122 | 0.730 | 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: | 0.600 |
| Method: | Least Squares | F-statistic: | 12.00 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000123 |
| Time: | 03:39:37 | Log-Likelihood: | -100.88 |
| No. Observations: | 23 | AIC: | 209.8 |
| Df Residuals: | 19 | BIC: | 214.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -12.1316 | 123.501 | -0.098 | 0.923 | -270.622 246.359 |
| C(dose)[T.1] | 137.0890 | 205.676 | 0.667 | 0.513 | -293.396 567.574 |
| expression | 7.7275 | 14.368 | 0.538 | 0.597 | -22.345 37.800 |
| expression:C(dose)[T.1] | -9.5271 | 22.248 | -0.428 | 0.673 | -56.092 37.038 |
| Omnibus: | 0.097 | Durbin-Watson: | 1.963 |
| Prob(Omnibus): | 0.953 | Jarque-Bera (JB): | 0.316 |
| Skew: | 0.059 | Prob(JB): | 0.854 |
| Kurtosis: | 2.438 | Cond. No. | 535. |
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: | 18.67 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.67e-05 |
| Time: | 03:39:37 | Log-Likelihood: | -100.99 |
| 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 | 21.9809 | 92.430 | 0.238 | 0.814 | -170.824 214.786 |
| C(dose)[T.1] | 49.2441 | 14.617 | 3.369 | 0.003 | 18.754 79.735 |
| expression | 3.7540 | 10.743 | 0.349 | 0.730 | -18.657 26.164 |
| Omnibus: | 0.083 | Durbin-Watson: | 1.979 |
| Prob(Omnibus): | 0.959 | Jarque-Bera (JB): | 0.309 |
| Skew: | 0.006 | Prob(JB): | 0.857 |
| Kurtosis: | 2.432 | Cond. No. | 197. |
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: | 03:39:37 | 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: | 0.453 |
| Model: | OLS | Adj. R-squared: | 0.427 |
| Method: | Least Squares | F-statistic: | 17.41 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000430 |
| Time: | 03:39:37 | Log-Likelihood: | -106.16 |
| No. Observations: | 23 | AIC: | 216.3 |
| Df Residuals: | 21 | BIC: | 218.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -218.6021 | 71.699 | -3.049 | 0.006 | -367.709 -69.495 |
| expression | 32.7594 | 7.852 | 4.172 | 0.000 | 16.431 49.088 |
| Omnibus: | 1.739 | Durbin-Watson: | 2.377 |
| Prob(Omnibus): | 0.419 | Jarque-Bera (JB): | 1.011 |
| Skew: | 0.077 | Prob(JB): | 0.603 |
| Kurtosis: | 1.984 | Cond. No. | 124. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.159 | 0.303 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.505 |
| Model: | OLS | Adj. R-squared: | 0.371 |
| Method: | Least Squares | F-statistic: | 3.748 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0447 |
| Time: | 03:39:37 | Log-Likelihood: | -70.019 |
| No. Observations: | 15 | AIC: | 148.0 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 13.3175 | 110.140 | 0.121 | 0.906 | -229.100 255.735 |
| C(dose)[T.1] | -30.2087 | 172.504 | -0.175 | 0.864 | -409.887 349.470 |
| expression | 8.2258 | 16.654 | 0.494 | 0.631 | -28.429 44.880 |
| expression:C(dose)[T.1] | 10.6911 | 25.078 | 0.426 | 0.678 | -44.505 65.888 |
| Omnibus: | 2.766 | Durbin-Watson: | 0.599 |
| Prob(Omnibus): | 0.251 | Jarque-Bera (JB): | 1.616 |
| Skew: | -0.554 | Prob(JB): | 0.446 |
| Kurtosis: | 1.835 | Cond. No. | 202. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.497 |
| Model: | OLS | Adj. R-squared: | 0.414 |
| Method: | Least Squares | F-statistic: | 5.936 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0161 |
| Time: | 03:39:37 | Log-Likelihood: | -70.141 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 12 | BIC: | 148.4 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -17.6969 | 79.825 | -0.222 | 0.828 | -191.620 156.226 |
| C(dose)[T.1] | 42.9874 | 16.099 | 2.670 | 0.020 | 7.911 78.064 |
| expression | 12.9405 | 12.019 | 1.077 | 0.303 | -13.248 39.129 |
| Omnibus: | 2.898 | Durbin-Watson: | 0.601 |
| Prob(Omnibus): | 0.235 | Jarque-Bera (JB): | 1.671 |
| Skew: | -0.568 | Prob(JB): | 0.434 |
| Kurtosis: | 1.824 | Cond. No. | 75.0 |
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: | 03:39:37 | 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: | 0.199 |
| Model: | OLS | Adj. R-squared: | 0.137 |
| Method: | Least Squares | F-statistic: | 3.223 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0959 |
| Time: | 03:39:37 | Log-Likelihood: | -73.639 |
| No. Observations: | 15 | AIC: | 151.3 |
| Df Residuals: | 13 | BIC: | 152.7 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -73.3432 | 93.475 | -0.785 | 0.447 | -275.283 128.597 |
| expression | 24.4376 | 13.613 | 1.795 | 0.096 | -4.971 53.846 |
| Omnibus: | 1.981 | Durbin-Watson: | 1.251 |
| Prob(Omnibus): | 0.371 | Jarque-Bera (JB): | 0.957 |
| Skew: | 0.101 | Prob(JB): | 0.620 |
| Kurtosis: | 1.779 | Cond. No. | 72.0 |