Solution :
Given:
(a):
X V a l u e s : ∑ = 28 M e a n = 5.6 ∑ ( X − M x ) 2 = S S x = 29.2 Y V a l u e s : ∑ = 29 M e a n = 5.8 ∑ ( Y − M y ) 2 = S S y = 6.8 X a n d Y C o m b i n e d : N = 5 ∑ ( X − M x ) ( Y − M y ) = 10.6 X\ Values:
\\∑ = 28
\\Mean = 5.6
\\∑(X - M_x)^2 = SS_x = 29.2
\\Y\ Values:
\\∑ = 29
\\Mean = 5.8
\\∑(Y - M_y)^2 = SS_y = 6.8
\\X\ and\ Y\ Combined:
\\N = 5
\\∑(X - M_x)(Y - M_y) = 10.6 X Va l u es : ∑ = 28 M e an = 5.6 ∑ ( X − M x ) 2 = S S x = 29.2 Y Va l u es : ∑ = 29 M e an = 5.8 ∑ ( Y − M y ) 2 = S S y = 6.8 X an d Y C o mbin e d : N = 5 ∑ ( X − M x ) ( Y − M y ) = 10.6
r C a l c u l a t i o n : r = ∑ ( ( X − M y ) ( Y − M x ) ) / ( S S x ) ( S S y ) r = 10.6 / ( 29.2 ) ( 6.8 ) = 0.7522 r\ Calculation:
\\r = ∑((X - M_y)(Y - M_x)) / \sqrt{(SS_x)(SS_y)}
\\r = 10.6 / \sqrt{(29.2)(6.8)} = 0.7522 r C a l c u l a t i o n : r = ∑ (( X − M y ) ( Y − M x )) / ( S S x ) ( S S y ) r = 10.6/ ( 29.2 ) ( 6.8 ) = 0.7522
So, the correlation coefficient=0.7522
Interpretation: This is a strong positive correlation, which means that high X variable scores go with high Y variable scores (and vice versa).
(b):
S u m o f p r o d u c t s ( S P ) = 10.6 R e g r e s s i o n E q u a t i o n = y ^ = b X + a b = S P / S S X = 10.6 / 29.2 = 0.36301 a = M Y − b M X = 5.8 − ( 0.36 × 5.6 ) = 3.76712 y ^ = 0.36301 X + 3.76712 \\Sum\ of\ products (SP) = 10.6
\\Regression\ Equation = ŷ = bX + a
\\b = SP/SS_X = 10.6/29.2 = 0.36301
\\a = M_Y - bM_X = 5.8 - (0.36\times5.6) = 3.76712
\\ŷ = 0.36301X + 3.76712 S u m o f p ro d u c t s ( SP ) = 10.6 R e g ress i o n Eq u a t i o n = y ^ = b X + a b = SP / S S X = 10.6/29.2 = 0.36301 a = M Y − b M X = 5.8 − ( 0.36 × 5.6 ) = 3.76712 y ^ = 0.36301 X + 3.76712
(c):
Put x=7
y ^ = 0.36301 ( 7 ) + 3.76712 y ^ = 6.30819 ŷ = 0.36301(7) + 3.76712
\\ŷ =6.30819 y ^ = 0.36301 ( 7 ) + 3.76712 y ^ = 6.30819
(d):
Uses:
The Chi-square goodness of fit test is a statistical hypothesis test used to determine whether a variable is likely to come from a specified distribution or not. It is often used to evaluate whether sample data is representative of the full population.
Comments