From the given data of x & y
CASE 1 :
Let us consider that y is the dependent variable and x is an independent variable then the formula for the regression line is given by,
y = a + bx
where,
a = y intercept
b = slope of the line
The formula for a and b is given by,
"b = \\frac{n\\sum(xy)- \\sum x\\sum y}{n\\sum(x)^2-(\\sum x)^2}\n\n\\ \\ \\ \\ \\ \\ \\ and\\ \\ \\ \\ \\ \n\na = \\frac{\\sum y}{n} - b\\frac{\\sum x}{n}"
where n = sample size = 6
from the above table we get the values of a and b as under
"b = \\frac{n\\sum(xy)- \\sum x\\sum y}{n\\sum(x^2)-(\\sum x)^2} = \\frac{6*174 - 21*42}{6*91 - 21^2} = 1.5428"
"a = \\frac{\\sum y}{n} - b\\frac{\\sum x}{n} = \\frac{42}{6} - 1.542857*\\frac{21}{6} = 1.6"
Hence the equation of the regression line becomes y = 1.6 + 1.54x
So the value of y, when x = 2.5 can be found using above equation
y = 1.6 + 1.54*2.5 = 5.457
(X1,Y1) = (2.5 , 5.457)
CASE 2 :
Let us consider that x is the dependent variable and y is an independent variable then the formula for the regression line is given by,
x = a + by
where,
a = x intercept
b = slope of the line
The formula for a and b is given by,
"b = \\frac{n\\sum(xy)- \\sum x\\sum y}{n\\sum(y)^2-(\\sum y)^2}\n\n\\ \\ \\ \\ \\ \\ \\ and\\ \\ \\ \\ \\ \n\na = \\frac{\\sum x}{n} - b\\frac{\\sum y}{n}"
where n = sample size = 6
from the above table we get the values of a and b as under
"b = \\frac{n\\sum(xy)- \\sum x\\sum y}{n\\sum(y)^2-(\\sum y)^2} = \\frac{6*174 - 21*42}{6*336-42^2} = \\frac{9}{14} = 0.64285"
"a = \\frac{\\sum x}{n} - b\\frac{\\sum y}{n} = \\frac{21}{6} - 0.64285*\\frac{42}{6} = -1"
Hence the equation of the regression line becomes x = -1 + 0.64y
So the value of x, when y = 7 can be found using above equation
x = -1 + 0.64*7 = 3.48
(X2 , Y2) = (3.48 , 7)
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