LID SPN TRAP

1451642_at

Kif1b kinesin family member 1B

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
2.011 0.172 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.694
Model: OLS Adj. R-squared: 0.646
Method: Least Squares F-statistic: 14.37
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.98e-05
Time: 03:42:18 Log-Likelihood: -99.482
No. Observations: 23 AIC: 207.0
Df Residuals: 19 BIC: 211.5
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 136.7933 127.039 1.077 0.295 -129.102 402.689
C(dose)[T.1] 274.1828 234.366 1.170 0.257 -216.350 764.716
expression -10.2545 15.758 -0.651 0.523 -43.236 22.727
expression:C(dose)[T.1] -24.9354 27.740 -0.899 0.380 -82.996 33.125
Omnibus: 0.458 Durbin-Watson: 2.097
Prob(Omnibus): 0.795 Jarque-Bera (JB): 0.580
Skew: 0.176 Prob(JB): 0.748
Kurtosis: 2.306 Cond. No. 571.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.681
Model: OLS Adj. R-squared: 0.649
Method: Least Squares F-statistic: 21.36
Date: Thu, 21 Nov 2024 Prob (F-statistic): 1.09e-05
Time: 03:42:19 Log-Likelihood: -99.961
No. Observations: 23 AIC: 205.9
Df Residuals: 20 BIC: 209.3
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 201.5939 104.101 1.937 0.067 -15.557 418.744
C(dose)[T.1] 63.7527 11.128 5.729 0.000 40.540 86.966
expression -18.3007 12.906 -1.418 0.172 -45.222 8.621
Omnibus: 1.096 Durbin-Watson: 2.220
Prob(Omnibus): 0.578 Jarque-Bera (JB): 0.819
Skew: 0.070 Prob(JB): 0.664
Kurtosis: 2.087 Cond. No. 212.

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: 03:42:19 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: 0.158
Model: OLS Adj. R-squared: 0.118
Method: Least Squares F-statistic: 3.935
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0605
Time: 03:42:19 Log-Likelihood: -111.13
No. Observations: 23 AIC: 226.3
Df Residuals: 21 BIC: 228.5
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -174.2415 128.189 -1.359 0.188 -440.826 92.343
expression 30.5029 15.376 1.984 0.061 -1.474 62.479
Omnibus: 2.653 Durbin-Watson: 1.991
Prob(Omnibus): 0.265 Jarque-Bera (JB): 1.302
Skew: 0.186 Prob(JB): 0.522
Kurtosis: 1.895 Cond. No. 164.

CP101

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

F-statistic p-value df difference
0.002 0.968 1.0

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

OLS Regression Results
Dep. Variable: AIM R-squared: 0.494
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 3.586
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0501
Time: 03:42:19 Log-Likelihood: -70.185
No. Observations: 15 AIC: 148.4
Df Residuals: 11 BIC: 151.2
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 269.3836 259.732 1.037 0.322 -302.283 841.051
C(dose)[T.1] -267.8668 319.062 -0.840 0.419 -970.116 434.383
expression -25.4127 32.651 -0.778 0.453 -97.277 46.452
expression:C(dose)[T.1] 40.3101 40.489 0.996 0.341 -48.805 129.425
Omnibus: 2.362 Durbin-Watson: 0.957
Prob(Omnibus): 0.307 Jarque-Bera (JB): 1.429
Skew: -0.750 Prob(JB): 0.489
Kurtosis: 2.805 Cond. No. 460.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.357
Method: Least Squares F-statistic: 4.886
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0280
Time: 03:42:19 Log-Likelihood: -70.832
No. Observations: 15 AIC: 147.7
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 61.0573 153.813 0.397 0.698 -274.073 396.188
C(dose)[T.1] 49.3731 16.303 3.028 0.010 13.852 84.894
expression 0.8017 19.301 0.042 0.968 -41.251 42.854
Omnibus: 2.712 Durbin-Watson: 0.819
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.856
Skew: -0.842 Prob(JB): 0.395
Kurtosis: 2.631 Cond. No. 156.

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: 03:42:19 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: 0.028
Model: OLS Adj. R-squared: -0.047
Method: Least Squares F-statistic: 0.3691
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.554
Time: 03:42:19 Log-Likelihood: -75.090
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 206.7697 186.440 1.109 0.288 -196.010 609.549
expression -14.4457 23.778 -0.608 0.554 -65.815 36.924
Omnibus: 1.334 Durbin-Watson: 1.530
Prob(Omnibus): 0.513 Jarque-Bera (JB): 0.834
Skew: 0.162 Prob(JB): 0.659
Kurtosis: 1.891 Cond. No. 148.