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multiple_regression [2019/05/21 22:33] – [무엇부터? 라는 문제] hkimscilmultiple_regression [2019/05/23 10:20] – [Why overall model is significant while IVs are not?] hkimscil
Line 331: Line 331:
 see https://www.researchgate.net/post/Why_is_the_Multiple_regression_model_not_significant_while_simple_regression_for_the_same_variables_is_significant see https://www.researchgate.net/post/Why_is_the_Multiple_regression_model_not_significant_while_simple_regression_for_the_same_variables_is_significant
  
-This problem +<code> 
 +RSS = 3:10 #Right shoe size 
 +LSS = rnorm(RSS, RSS, 0.1) #Left shoe size - similar to RSS 
 +cor(LSS, RSS) #correlation ~ 0.99 
 +  
 +weights = 120 + rnorm(RSS, 10*RSS, 10) 
 +  
 +##Fit a joint model 
 +m = lm(weights ~ LSS + RSS) 
 + 
 +##F-value is very small, but neither LSS or RSS are significant 
 +summary(m) 
 +</code> 
 + 
 + 
 +<code>> RSS = 3:10 #Right shoe size 
 +> LSS = rnorm(RSS, RSS, 0.1) #Left shoe size - similar to RSS 
 +> cor(LSS, RSS) #correlation ~ 0.99 
 +[1] 0.9994836 
 +>  
 +> weights = 120 + rnorm(RSS, 10*RSS, 10) 
 +>  
 +> ##Fit a joint model 
 +> m = lm(weights ~ LSS + RSS) 
 +>  
 +> ##F-value is very small, but neither LSS or RSS are significant 
 +> summary(m) 
 + 
 +Call: 
 +lm(formula = weights ~ LSS + RSS) 
 + 
 +Residuals: 
 +      1                                           8  
 + 4.8544  4.5254 -3.6333 -7.6402 -0.2467 -3.1997 -5.2665 10.6066  
 + 
 +Coefficients: 
 +            Estimate Std. Error t value Pr(>|t|)     
 +(Intercept)  104.842      8.169  12.834 5.11e-05 *** 
 +LSS          -14.162     35.447  -0.400    0.706     
 +RSS           26.305     35.034   0.751    0.487     
 +--- 
 +Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
 + 
 +Residual standard error: 7.296 on 5 degrees of freedom 
 +Multiple R-squared:  0.9599, Adjusted R-squared:  0.9439  
 +F-statistic: 59.92 on 2 and 5 DF,  p-value: 0.000321 
 + 
 +>  
 +> ##Fitting RSS or LSS separately gives a significant result.  
 +> summary(lm(weights ~ LSS)) 
 + 
 +Call: 
 +lm(formula = weights ~ LSS) 
 + 
 +Residuals: 
 +   Min     1Q Median     3Q    Max  
 +-6.055 -4.930 -2.925  4.886 11.854  
 + 
 +Coefficients: 
 +            Estimate Std. Error t value Pr(>|t|)     
 +(Intercept)  103.099      7.543   13.67 9.53e-06 *** 
 +LSS           12.440      1.097   11.34 2.81e-05 *** 
 +--- 
 +Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
 + 
 +Residual standard error: 7.026 on 6 degrees of freedom 
 +Multiple R-squared:  0.9554, Adjusted R-squared:  0.948  
 +F-statistic: 128.6 on 1 and 6 DF,  p-value: 2.814e-05 
 + 
 +>  
 +</code>
  
  
multiple_regression.txt · Last modified: 2023/10/19 08:39 by hkimscil

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