DATA 1
ID | X1 | Y |
A | 0 | 9 |
B | 1 | 10 |
C | 0 | 5 |
D | 1 | 1 |
E | 0 | 0 |
The zero order model is Ŷ = 8 − 2.5X1
1. What is the residual error in using the zero order model to predict Y for ID C? (Correct answer is -3, please show me how to find it)
2. What is the error in using the mean of Y to predict Y for ID C? (correct answer is -2, please show me how to find it)
1).when x=0,
8-(2.5 * 0)= 8
y=5
residual error for ID C = (observed value - predicted value) = (5-8)= -3
2).mean of y=(9+10+5+1+0)/5 =25/5 =5
when x1=mean of y =5,
8 - (2.5*5) = 8-12.5 =4.5
error for ID C = (observed value - predicted value) = (5 - 4.5) = 0.5
**********BUT I THINK IN ORDER TO GET THE ANSWER -2,YOUR QUESTION SHOULD BE:
What is the error in using the mean of X to predict Y for ID C?
now ,i am solving this:
mean of x = (0 + 1+ 0+ 1 +0)/5 = 2/5 =0.4
when x1=mean of x = 0.4,
8 - (2.5* 0.4) = 8-1 = 7
error for ID C = (observed value - predicted value) = (5-7) = -2
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DATA 1 ID X1 Y A 0 9 B 1 10 C 0 5 D 1 1 E 0 0 The zero order model is Ŷ = 8 − 2.5X1 1. What is the residual error in using the zero order model to predict Y for ID C? (Correct answer...
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