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multiple_regression [2019/05/21 22:40] – [Why overall model is significant while IVs are not?] hkimscilmultiple_regression [2019/05/21 22:41] – [Why overall model is significant while IVs are not?] hkimscil
Line 334: Line 334:
 > LSS = rnorm(RSS, RSS, 0.1) #Left shoe size - similar to RSS > LSS = rnorm(RSS, RSS, 0.1) #Left shoe size - similar to RSS
 > cor(LSS, RSS) #correlation ~ 0.99 > cor(LSS, RSS) #correlation ~ 0.99
-[1] 0.9983294+[1] 0.9994836
 >  > 
 > weights = 120 + rnorm(RSS, 10*RSS, 10) > weights = 120 + rnorm(RSS, 10*RSS, 10)
Line 348: Line 348:
  
 Residuals: Residuals:
-      1       2       3       4       5       6       7  +      1       2       3       4       5       6       7       8  
- 4.6231 -4.8706  1.3063  0.9639 -1.3120 -6.1247  2.6604  + 4.8544  4.5254 -3.6333 -7.6402 -0.2467 -3.1997 -5.2665 10.6066 
-      8  +
- 2.7536 +
  
 Coefficients: Coefficients:
             Estimate Std. Error t value Pr(>|t|)                 Estimate Std. Error t value Pr(>|t|)    
-(Intercept)  103.116      4.832  21.339 4.19e-06 *** +(Intercept)  104.842      8.169  12.834 5.11e-05 *** 
-LSS          -27.546     11.952  -2.305   0.0694 .   +LSS          -14.162     35.447  -0.400    0.706     
-RSS           39.299     12.040   3.264   0.0223 *  +RSS           26.305     35.034   0.751    0.487    
 --- ---
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
-Residual standard error: 4.508 on 5 degrees of freedom +Residual standard error: 7.296 on 5 degrees of freedom 
-Multiple R-squared:  0.9827, Adjusted R-squared:  0.9757  +Multiple R-squared:  0.9599, Adjusted R-squared:  0.9439  
-F-statistic: 141.6 on 2 and 5 DF,  p-value: 3.964e-05+F-statistic: 59.92 on 2 and 5 DF,  p-value: 0.000321
  
 >  > 
Line 373: Line 371:
  
 Residuals: Residuals:
-    Min      1Q  Median      3Q     Max  +   Min     1Q Median     3Q    Max  
--11.044  -2.203  -0.422   2.774  12.369 +-6.055 -4.930 -2.925  4.886 11.854 
  
 Coefficients: Coefficients:
             Estimate Std. Error t value Pr(>|t|)                 Estimate Std. Error t value Pr(>|t|)    
-(Intercept)  105.939      7.679   13.79 9.03e-06 *** +(Intercept)  103.099      7.543   13.67 9.53e-06 *** 
-LSS           11.401      1.115   10.22 5.11e-05 ***+LSS           12.440      1.097   11.34 2.81e-05 ***
 --- ---
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
-Residual standard error: 7.282 on 6 degrees of freedom +Residual standard error: 7.026 on 6 degrees of freedom 
-Multiple R-squared:  0.9457, Adjusted R-squared:  0.9366  +Multiple R-squared:  0.9554, Adjusted R-squared:  0.948  
-F-statistic: 104.5 on 1 and 6 DF,  p-value: 5.113e-05+F-statistic: 128.6 on 1 and 6 DF,  p-value: 2.814e-05
  
 >  > 
multiple_regression.txt · Last modified: 2023/10/19 08:39 by hkimscil
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