Solution
"\\sum x = 716 \\space \\sum y=141 \\space n=10"
1. Regression Model
"\\hat\\beta = {n \\sum xy - \\sum x \\sum y \\over n \\sum x^2 - {( \\sum x)} ^2}"
"={10(14953)-(716*141) \\over 10(80654)-{(716)}^2}""=0.1653"
"\\alpha = {\\sum y - \\beta \\sum x \\over n}"
"={141 - 0.1653(716) \\over 10} =2.2645"
2. When expenditure is p40k
y=40
"x= {40-2.2645 \\over 0.1653} = 228.28"
"\\approx 228.28k \\space copies"
3. If there are 220k copies.
x=220
=p38.63k
4. Test regression coefficient
"H_0 : \\beta =0 \\space vs \\space H_1: \\beta \\not = 0"
"S= \\sqrt {SS_y - \\beta SS_x \\over n-2}"
"SS_y = {n \\sum y^2 - {( \\sum y)} ^2 \\over n}=1220.9"
"SS_x = {n \\sum x^2 - {( \\sum x)} ^2 \\over n} = 29388.4"
"S_{xy} = {n \\sum xy - \\sum x \\sum y \\over n} =4857.4"
"S= \\sqrt {1220.9 - 0.1653 (4857.4) \\over 8} = 7.228"
"t= {0.1653 - 0 \\over {7.228 \\over 29388.4}} = 3.921"
T-critical
"t_{0.025,8}=2.306"
Since t-stat > t-critical, we reject H0
Therefore the regression coefficient is significant
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