Question
Do the data suggest a linear . quadratic or an exponential function ? Use regression to find a model for each data set.
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)
Hint:
1. When the difference between 2 consecutive output values (y values) for a given constant change in the input values (x values) is constant. i.e. y(n)- y(n-1) is constant for any value of n, the function is known as a linear function.
2. Regression is a statistical tool used to find a model that can represent the relation between a given change in dependent variable (output values/ y values) for a given change in independent variable (input values/ x values).
Linear Equation using regression can be represented as-
Y = a + bX, where-
a = ![fraction numerator left square bracket left parenthesis capital sigma y right parenthesis left parenthesis capital sigma x squared right parenthesis space minus space left parenthesis capital sigma x right parenthesis space left parenthesis capital sigma x y right parenthesis right square bracket space over denominator left square bracket n left parenthesis capital sigma x squared right parenthesis space minus space left parenthesis capital sigma x right parenthesis squared right square bracket end fraction](data:image/png;base64,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)
b = ![fraction numerator left square bracket n left parenthesis capital sigma x y right parenthesis space minus space left parenthesis capital sigma x right parenthesis space left parenthesis capital sigma y right parenthesis right square bracket over denominator space left square bracket n left parenthesis capital sigma x squared right parenthesis space minus space left parenthesis capital sigma x right parenthesis squared right square bracket end fraction](data:image/png;base64,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)
The correct answer is: The given function is a quadratic function and using Regression, the given function can be modelled into the equation- Y = - X2 + 20 X - 103.
Step-by-step solution:-
From the given table, we observe the following readings-
x1 = 6, y1 = -19;
x2 = 7, y2 = -12;
x3 = 8, y3 = -7;
x4 = 9, y4 = -4;
x5 = 10, y5 = -3
a). Difference between 2 consecutive x values-
dx1 = x2 - x1 = 7 - 6 = 1
dx2 = x3 - x2 = 8 - 7 = 1
dx3 = x4 - x3 = 9 - 8 = 1
dx4 = x5 - x4 = 10 - 9 = 1
Difference between 2 consecutive y values-
dy1 = y2 - y1 = -12 - (-19) = -12 + 19 = 7
dy2 = y3 - y2 = -7 - (-12) = -7 + 12 = 5
dy3 = y4 - y3 = -4 - (-7) = -4 + 7 = 3
dy4 = y5 - y4 = -3 - (-4) = -3 + 4 = 1
We observe that the difference for every consecutive x values is constant i.e. 1 but for y values the difference is not constant.
Hence, the given function is not a linear function.
b). Now, difference between 2 consecutive differences for y values-
dy2 - dy1 = 7 - 5 = 2
dy3 - dy2 = 5 - 3 = 2
dy4 - dy3 = 3 - 1 = 2
We observe that the difference of differences of 2 consecutive y values are constant i.e. 2.
Hence, the given function is a quadratic function.
Using Quadratic Regression formula and values from the adjacent table-
Y = aX2 + bX + c, where-
Σy = nc + b(Σx) + a(Σx2)
∴ -45 = 5c + b(40) + a(330)
∴ -45 = 5c + 40b + 330a .................................................. (Equation i)
Σxy = c(Σx) + b(Σx2) + a(Σx3)
∴ -320 = c(40) + b(330) + a(2,800)
∴ -320 = 40c + 330b + 2,800a ....................................... (Equation ii)
Σx2y = c(Σx2) + b(Σx3) + a(Σx4)
∴ -2,344 = c(330) + b(2,800) + a(24,354)
∴ -2,344 = 330c + 2,800b + 24,354a ....................... (Equation iii)
Dividing Equation 2 by 8, we get-
350a + 41.25b + 5c = -40 …............................................... (Equation iv)
Subtracting Equation I from Equation iv, we get-
350a + 41.25b + 5c = -40 …............................................... (Equation iv)
- 330a + 40b + 5c = -45 …............................................... (Equation i)
20a + 1.25b = 5 .................................................. (Equation v)
Multiplying Equation ii with 8.25, we get-
23,100a + 2,722.5b + 330c = -2,640 ......................... (Equation vi)
Subtracting Equation vi from Equation iii, we get-
24,354a + 2,800b + 330c = -2,344 ......................... (Equation iii)
- 23,100a + 2,722.5b + 330c = -2,640 ......................... (Equation vi)
1,254a + 77.5b = 296 ......................... (Equation vii)
Multiplying Equation v with 62, we get-
1,240a + 77.5b = 310 ............................................... (Equation viii)
Subtracting Equation vii from Equation viii, we get-
1,240a + 77.5b = 310 ............................................... (Equation viii)
- 1,254a + 77.5b = 296 ............................................... (Equation vii)
-14a = 14
i.e. -14a = 14
∴ a = 14/ -14 ................................... (Dividing both sides by -14)
∴ a = -1
Substituting a = -1 in Equation v, we get-
20a + 1.25b = 5 .................................................. (Equation v)
∴ 20(-1) + 1.25b = 5
∴ -20 + 1.25b = 5
∴ 1.25b = 5 + 20 ........................................ (Taking all constants together)
∴ 1.25b = 25
∴ b = 25/1.25 ............................................ (Dividing both sides by 1.25)
∴ b = 20
Substituting a = -1 and b = 20 in Equation i, we get-
330a + 40b + 5c = -45 .............................. (Equation i)
∴ 330 (-1) + 40 (20) + 5c = -45
∴ -330 + 800 + 5c = -45
∴ 470 + 5c = -45
∴ 5c = -45 - 470 ..................... (Taking all constants together)
∴ 5c = -515
∴ c = -515/5 ........................... (Dividing both sides by 5)
∴ c = -103
∴ The Quadratic Equation is-
Y = aX2 + bX + c
∴ Y = -1 X2 + 20 X + (-103)
∴ Y = - X2 + 20 X - 103
Final Answer:-
∴ The given function is a quadratic function and using Regression, the given function can be modelled into the equation- Y = - X2 + 20 X - 103.
x2 = 7, y2 = -12;
x3 = 8, y3 = -7;
x4 = 9, y4 = -4;
x5 = 10, y5 = -3
a). Difference between 2 consecutive x values-
dx1 = x2 - x1 = 7 - 6 = 1
dx2 = x3 - x2 = 8 - 7 = 1
dx3 = x4 - x3 = 9 - 8 = 1
dx4 = x5 - x4 = 10 - 9 = 1
Difference between 2 consecutive y values-
dy1 = y2 - y1 = -12 - (-19) = -12 + 19 = 7
dy2 = y3 - y2 = -7 - (-12) = -7 + 12 = 5
dy3 = y4 - y3 = -4 - (-7) = -4 + 7 = 3
dy4 = y5 - y4 = -3 - (-4) = -3 + 4 = 1
We observe that the difference for every consecutive x values is constant i.e. 1 but for y values the difference is not constant.
Hence, the given function is not a linear function.
b). Now, difference between 2 consecutive differences for y values-
dy2 - dy1 = 7 - 5 = 2
dy3 - dy2 = 5 - 3 = 2
dy4 - dy3 = 3 - 1 = 2
We observe that the difference of differences of 2 consecutive y values are constant i.e. 2.
Hence, the given function is a quadratic function.
Using Quadratic Regression formula and values from the adjacent table-
Y = aX2 + bX + c, where-
Σy = nc + b(Σx) + a(Σx2)
∴ -45 = 5c + b(40) + a(330)
∴ -45 = 5c + 40b + 330a .................................................. (Equation i)
Σxy = c(Σx) + b(Σx2) + a(Σx3)
∴ -320 = c(40) + b(330) + a(2,800)
∴ -320 = 40c + 330b + 2,800a ....................................... (Equation ii)
Σx2y = c(Σx2) + b(Σx3) + a(Σx4)
∴ -2,344 = c(330) + b(2,800) + a(24,354)
∴ -2,344 = 330c + 2,800b + 24,354a ....................... (Equation iii)
Dividing Equation 2 by 8, we get-
350a + 41.25b + 5c = -40 …............................................... (Equation iv)
Subtracting Equation I from Equation iv, we get-
350a + 41.25b + 5c = -40 …............................................... (Equation iv)
- 330a + 40b + 5c = -45 …............................................... (Equation i)
20a + 1.25b = 5 .................................................. (Equation v)
Multiplying Equation ii with 8.25, we get-
23,100a + 2,722.5b + 330c = -2,640 ......................... (Equation vi)
Subtracting Equation vi from Equation iii, we get-
24,354a + 2,800b + 330c = -2,344 ......................... (Equation iii)
- 23,100a + 2,722.5b + 330c = -2,640 ......................... (Equation vi)
1,254a + 77.5b = 296 ......................... (Equation vii)
Multiplying Equation v with 62, we get-
1,240a + 77.5b = 310 ............................................... (Equation viii)
Subtracting Equation vii from Equation viii, we get-
1,240a + 77.5b = 310 ............................................... (Equation viii)
- 1,254a + 77.5b = 296 ............................................... (Equation vii)
-14a = 14
i.e. -14a = 14
∴ a = 14/ -14 ................................... (Dividing both sides by -14)
∴ a = -1
Substituting a = -1 in Equation v, we get-
20a + 1.25b = 5 .................................................. (Equation v)
∴ 20(-1) + 1.25b = 5
∴ -20 + 1.25b = 5
∴ 1.25b = 5 + 20 ........................................ (Taking all constants together)
∴ 1.25b = 25
∴ b = 25/1.25 ............................................ (Dividing both sides by 1.25)
∴ b = 20
Substituting a = -1 and b = 20 in Equation i, we get-
330a + 40b + 5c = -45 .............................. (Equation i)
∴ 330 (-1) + 40 (20) + 5c = -45
∴ -330 + 800 + 5c = -45
∴ 470 + 5c = -45
∴ 5c = -45 - 470 ..................... (Taking all constants together)
∴ 5c = -515
∴ c = -515/5 ........................... (Dividing both sides by 5)
∴ c = -103
∴ The Quadratic Equation is-
Y = aX2 + bX + c
∴ Y = -1 X2 + 20 X + (-103)
∴ Y = - X2 + 20 X - 103
Final Answer:-
∴ The given function is a quadratic function and using Regression, the given function can be modelled into the equation- Y = - X2 + 20 X - 103.