In: Statistics and Probability
a. Develop the estimated regression equation by using the
formula for computing the values for bo and b1. “ SHOW YOUR WORK”
means WRITE the formula and create the columns as needed below
follow the steps through. If you need more space below, create them
by pressing ENTER
b. Compute SSE, SST, and SSR using only Computing formulas.
c. Compute the coefficient of determination r (The same as R ).
Comment on the goodness of fit (That is, what does the value of r
tell you about the goodness of the fit). Explain why.
d. Compute the sample correlation coefficient (r). (Hint: You do
not need to use the formula for r !)
e. Determine if the slope is significant at Alpha = 0.05ant
y -3 -1 0 0
7 5 13
x 1 2 3
4 5 6 7
X | Y | (x-x̅)² | (y-ȳ)² | (x-x̅)(y-ȳ) |
1 | -3 | 9 | 36 | 18 |
2 | -1 | 4 | 16 | 8 |
3 | 0 | 1 | 9 | 3 |
4 | 0 | 0 | 9 | 0 |
5 | 7 | 1 | 16 | 4 |
6 | 5 | 4 | 4 | 4 |
7 | 13 | 9 | 100 | 30 |
ΣX | ΣY | Σ(x-x̅)² | Σ(y-ȳ)² | Σ(x-x̅)(y-ȳ) | |
total sum | 28 | 21 | 28 | 190 | 67 |
mean | 4 | 3 | SSxx | SSyy | SSxy |
sample size , n = 7
here, x̅ = 4 , ȳ =
3
SSxx = Σ(x-x̅)² = 28
SSxy= Σ(x-x̅)(y-ȳ) = 67
a)
slope , ß1 = SSxy/SSxx = 67/28 = 2.393
intercept, ß0 = y̅-ß1* x̄ = 3-2.393*4 =
-6.571
so, regression line is Ŷ =
-6.5714 + 2.3929 *x
b)
SSE= (Sx*Sy - S²xy)/Sx = (28*190-67²)/28 =
29.68
SST=SSyy = 190
SSR=SST-SSE=190-29.68=160.32
c)
coefficient of determination,R² = SSR/SST=0.8438
84.38% of obeservation of Y is explained by X, so, it is a good estimate.
d)
sample correlation coefficient (r). =√R² =√0.8438 =
0.9186
e)
std error ,Se = √(SSE/(n-2)) =
2.4363
slope hypothesis test
Ho: ß1= 0
H1: ß1╪ 0
n= 7
alpha= 0.05
estimated std error of slope =Se(ß1) =
s/√Sxx =
2.4363/√28 = 0.4604
t stat = ß1 /Se(ß1) = 2.3929/0.4604 = 5.197
df=n-2 = 5
p-value = 0.0035
decision : p-value<α , reject Ho
so, there is enough evidence that slope is significant at
α=0.05