(a) In a regression analysis, the sum of squares for the predicted sco

osi4a2nxk

osi4a2nxk

Answered question

2021-11-20

(a) In a regression analysis, the sum of squares for the predicted scores is 100 and the sum of squares error is 200, what is R2?
(b) In a different regression analysis, 40% of the variance was explained. The sum of squares total is 1000. What is the sum of squares of the predicted values?

Answer & Explanation

Elizabeth Witte

Elizabeth Witte

Beginner2021-11-21Added 24 answers

Step 1.
Introduction:
In a regression analysis, denote SST as the total sum of squares, SSR as the regression sum of squares or the sum of squares for the predicted values, and SSE as the error sum of squares.
Then, the coefficient of determination is given as:
R2=SSRSST...... (1)
Now, it is known that for a regression analysis, SST=SSR+SSE.
Thus, SSR=SST-SSE.
Using this relation, R2 can be obtained as:
R2=(SSTSSE)SST
R2=1(SSESST)......(2)
Any of equation (1) or (2) may be used to find R2.
Step 2
a.
Here, SSR=100, SSE=200.
Thus,
SST=SSR+SSE
=100+200
=300.
Substitute SSE=200, SST=300 in equation (2):
R2=1(SSESST)
=1(200300)
=13
0.3333.
Thus, the value of R2 is 0.3333.
Sometimes, R2 is expressed as a percentage, instead of a proportion. The proportion 0.3333 can be expressed as a percentage, by multiplying it with 100.
Hence, in percentage form, the value of R2 is 33.33%.
It must be understood that both give the same value, just expressed in a different manner. You must provide the answer as instructed in your class.
Howell

Howell

Beginner2021-11-22Added 11 answers

Step 1
b.
The R2 value can be interpreted as the proportion or percentage of variation in the response variable that is explained by the variation in the predictor variables in the model.
In the second regression model, since 40% of the variance in the response variable was explained by the variation in the model, the value of R2 is 40% or 0.40 (obtained by dividing the percentage by 100).
Further, it is also given that, SST=1,000.
In order to find the value of the sum of squares of the predicted values, substitute SST=1,000, R2= 0.40 in equation (1)
R2=SSRSST
0.40=SSR1,000
SSR=1,000×(0.4)
SSR=400.
Hence, the sum of squares of the predicted values is 400.

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