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
0.080 0.780 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.675
Model: OLS Adj. R-squared: 0.624
Method: Least Squares F-statistic: 13.17
Date: Thu, 21 Nov 2024 Prob (F-statistic): 6.95e-05
Time: 05:02:59 Log-Likelihood: -100.17
No. Observations: 23 AIC: 208.3
Df Residuals: 19 BIC: 212.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 182.8264 167.804 1.090 0.290 -168.392 534.045
C(dose)[T.1] -195.0758 208.285 -0.937 0.361 -631.022 240.871
expression -11.5374 15.043 -0.767 0.453 -43.023 19.948
expression:C(dose)[T.1] 23.1243 19.193 1.205 0.243 -17.046 63.295
Omnibus: 0.321 Durbin-Watson: 1.919
Prob(Omnibus): 0.852 Jarque-Bera (JB): 0.484
Skew: -0.185 Prob(JB): 0.785
Kurtosis: 2.393 Cond. No. 714.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.650
Model: OLS Adj. R-squared: 0.615
Method: Least Squares F-statistic: 18.61
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.72e-05
Time: 05:02:59 Log-Likelihood: -101.02
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 24.4604 105.487 0.232 0.819 -195.583 244.503
C(dose)[T.1] 55.4961 11.620 4.776 0.000 31.257 79.735
expression 2.6685 9.447 0.282 0.780 -17.038 22.375
Omnibus: 0.238 Durbin-Watson: 1.885
Prob(Omnibus): 0.888 Jarque-Bera (JB): 0.432
Skew: 0.091 Prob(JB): 0.806
Kurtosis: 2.354 Cond. No. 263.

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: 05:02:59 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: 0.252
Model: OLS Adj. R-squared: 0.216
Method: Least Squares F-statistic: 7.067
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0147
Time: 05:02:59 Log-Likelihood: -109.77
No. Observations: 23 AIC: 223.5
Df Residuals: 21 BIC: 225.8
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 370.3547 109.506 3.382 0.003 142.624 598.086
expression -27.0085 10.160 -2.658 0.015 -48.137 -5.880
Omnibus: 1.516 Durbin-Watson: 2.050
Prob(Omnibus): 0.469 Jarque-Bera (JB): 1.345
Skew: 0.522 Prob(JB): 0.510
Kurtosis: 2.439 Cond. No. 191.

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
3.501 0.086 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.600
Model: OLS Adj. R-squared: 0.491
Method: Least Squares F-statistic: 5.509
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0148
Time: 05:02:59 Log-Likelihood: -68.421
No. Observations: 15 AIC: 144.8
Df Residuals: 11 BIC: 147.7
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 123.2128 112.225 1.098 0.296 -123.793 370.219
C(dose)[T.1] 167.1006 142.736 1.171 0.266 -147.058 481.260
expression -6.0747 12.170 -0.499 0.628 -32.861 20.711
expression:C(dose)[T.1] -13.5580 15.695 -0.864 0.406 -48.102 20.986
Omnibus: 0.445 Durbin-Watson: 1.368
Prob(Omnibus): 0.801 Jarque-Bera (JB): 0.526
Skew: -0.112 Prob(JB): 0.769
Kurtosis: 2.110 Cond. No. 260.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.573
Model: OLS Adj. R-squared: 0.502
Method: Least Squares F-statistic: 8.061
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00604
Time: 05:02:59 Log-Likelihood: -68.913
No. Observations: 15 AIC: 143.8
Df Residuals: 12 BIC: 145.9
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 198.0731 70.549 2.808 0.016 44.360 351.786
C(dose)[T.1] 44.4134 14.082 3.154 0.008 13.730 75.096
expression -14.2266 7.603 -1.871 0.086 -30.792 2.339
Omnibus: 1.753 Durbin-Watson: 0.955
Prob(Omnibus): 0.416 Jarque-Bera (JB): 1.025
Skew: -0.290 Prob(JB): 0.599
Kurtosis: 1.858 Cond. No. 93.8

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: 05:02:59 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: 0.220
Model: OLS Adj. R-squared: 0.160
Method: Least Squares F-statistic: 3.658
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0781
Time: 05:02:59 Log-Likelihood: -73.441
No. Observations: 15 AIC: 150.9
Df Residuals: 13 BIC: 152.3
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 260.9496 87.929 2.968 0.011 70.991 450.908
expression -18.5792 9.715 -1.912 0.078 -39.567 2.408
Omnibus: 0.204 Durbin-Watson: 2.100
Prob(Omnibus): 0.903 Jarque-Bera (JB): 0.026
Skew: 0.006 Prob(JB): 0.987
Kurtosis: 2.796 Cond. No. 89.7