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 |
| 0.464 | 0.504 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.662 |
| Model: | OLS | Adj. R-squared: | 0.609 |
| Method: | Least Squares | F-statistic: | 12.43 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 9.95e-05 |
| Time: | 04:31:04 | Log-Likelihood: | -100.62 |
| No. Observations: | 23 | AIC: | 209.2 |
| Df Residuals: | 19 | BIC: | 213.8 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -51.6497 | 126.898 | -0.407 | 0.689 | -317.251 213.951 |
| C(dose)[T.1] | 225.5898 | 308.892 | 0.730 | 0.474 | -420.929 872.109 |
| expression | 11.8099 | 14.141 | 0.835 | 0.414 | -17.787 41.407 |
| expression:C(dose)[T.1] | -19.3709 | 35.043 | -0.553 | 0.587 | -92.716 53.975 |
| Omnibus: | 0.320 | Durbin-Watson: | 1.992 |
| Prob(Omnibus): | 0.852 | Jarque-Bera (JB): | 0.482 |
| Skew: | 0.030 | Prob(JB): | 0.786 |
| Kurtosis: | 2.293 | Cond. No. | 723. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.657 |
| Model: | OLS | Adj. R-squared: | 0.623 |
| Method: | Least Squares | F-statistic: | 19.16 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.25e-05 |
| Time: | 04:31:04 | Log-Likelihood: | -100.80 |
| No. Observations: | 23 | AIC: | 207.6 |
| Df Residuals: | 20 | BIC: | 211.0 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -23.3761 | 114.100 | -0.205 | 0.840 | -261.384 214.632 |
| C(dose)[T.1] | 54.9153 | 8.974 | 6.119 | 0.000 | 36.195 73.635 |
| expression | 8.6556 | 12.712 | 0.681 | 0.504 | -17.861 35.172 |
| Omnibus: | 0.248 | Durbin-Watson: | 1.900 |
| Prob(Omnibus): | 0.884 | Jarque-Bera (JB): | 0.438 |
| Skew: | 0.051 | Prob(JB): | 0.803 |
| Kurtosis: | 2.331 | Cond. No. | 237. |
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: | 04:31:04 | 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: | 0.015 |
| Model: | OLS | Adj. R-squared: | -0.032 |
| Method: | Least Squares | F-statistic: | 0.3169 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.579 |
| Time: | 04:31:05 | Log-Likelihood: | -112.93 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 181.1996 | 180.428 | 1.004 | 0.327 | -194.020 556.420 |
| expression | -11.4330 | 20.311 | -0.563 | 0.579 | -53.672 30.806 |
| Omnibus: | 2.634 | Durbin-Watson: | 2.466 |
| Prob(Omnibus): | 0.268 | Jarque-Bera (JB): | 1.315 |
| Skew: | 0.203 | Prob(JB): | 0.518 |
| Kurtosis: | 1.901 | Cond. No. | 227. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.233 | 0.638 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.559 |
| Model: | OLS | Adj. R-squared: | 0.439 |
| Method: | Least Squares | F-statistic: | 4.644 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0248 |
| Time: | 04:31:05 | Log-Likelihood: | -69.163 |
| No. Observations: | 15 | AIC: | 146.3 |
| Df Residuals: | 11 | BIC: | 149.2 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -288.4060 | 231.423 | -1.246 | 0.239 | -797.764 220.952 |
| C(dose)[T.1] | 516.4310 | 294.593 | 1.753 | 0.107 | -131.965 1164.827 |
| expression | 40.5956 | 26.373 | 1.539 | 0.152 | -17.452 98.643 |
| expression:C(dose)[T.1] | -53.9878 | 34.269 | -1.575 | 0.143 | -129.413 21.437 |
| Omnibus: | 0.697 | Durbin-Watson: | 1.425 |
| Prob(Omnibus): | 0.706 | Jarque-Bera (JB): | 0.635 |
| Skew: | -0.415 | Prob(JB): | 0.728 |
| Kurtosis: | 2.427 | Cond. No. | 481. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.459 |
| Model: | OLS | Adj. R-squared: | 0.369 |
| Method: | Least Squares | F-statistic: | 5.096 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0250 |
| Time: | 04:31:05 | Log-Likelihood: | -70.689 |
| No. Observations: | 15 | AIC: | 147.4 |
| Df Residuals: | 12 | BIC: | 149.5 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -8.1157 | 156.870 | -0.052 | 0.960 | -349.906 333.675 |
| C(dose)[T.1] | 53.0500 | 17.513 | 3.029 | 0.010 | 14.892 91.208 |
| expression | 8.6185 | 17.849 | 0.483 | 0.638 | -30.272 47.509 |
| Omnibus: | 4.429 | Durbin-Watson: | 0.842 |
| Prob(Omnibus): | 0.109 | Jarque-Bera (JB): | 2.528 |
| Skew: | -1.001 | Prob(JB): | 0.283 |
| Kurtosis: | 3.189 | Cond. No. | 175. |
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: | 04:31:05 | 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: | 0.046 |
| Model: | OLS | Adj. R-squared: | -0.028 |
| Method: | Least Squares | F-statistic: | 0.6242 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.444 |
| Time: | 04:31:05 | Log-Likelihood: | -74.948 |
| No. Observations: | 15 | AIC: | 153.9 |
| Df Residuals: | 13 | BIC: | 155.3 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 230.2784 | 173.192 | 1.330 | 0.207 | -143.880 604.437 |
| expression | -16.0213 | 20.278 | -0.790 | 0.444 | -59.829 27.786 |
| Omnibus: | 0.112 | Durbin-Watson: | 1.435 |
| Prob(Omnibus): | 0.946 | Jarque-Bera (JB): | 0.147 |
| Skew: | -0.136 | Prob(JB): | 0.929 |
| Kurtosis: | 2.598 | Cond. No. | 151. |