Assignment 1 with Solutions for Linear Models | MATH 6010, Assignments of Mathematics

Material Type: Assignment; Class: Linear Models; Subject: Mathematics; University: University of Utah; Term: Fall 2004;

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Pre 2010

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Math 6010, Fall 2004: Homework
Homework 1 Consider the following hypothetical data; it is regarding
a particular bird species:1
Wing length (cm) Age (days)
1.5 4.0
2.2 5.0
3.1 8.0
3.2 9.0
3.2 10.0
3.9 11.0
4.1 12.0
4.7 14.0
5.2 16.0
Use simple linear regression to predict the age of a newly-caught bird
whose wing is 4.0 centimeter long. Assuming normal errors, test the
hypothesis (95%) that wing length and age are linearly related.
Solution. Let the ages be denoted by y1, . . . , y9, and the wing-lengths
by x1, . . . , x9. You should compute:
¯x3.46 sx1.17
¯y9.89 sy3.92 Corr(x, y)0.99.
Now recall that the regression line is described by
y= ¯y+b
β(x¯x),
where b
β= Corr(x, y)sy
sx
3.3.
So the regression line is (approximately) given by
y= 9.89 + 3.3(x3.46).
Plug in x= 4 to obtain the regression estimate:
y= 9.89 + 3.3(4 3.46) 11.21.
This answers the first question.
To answer the second question recall that
b
βNβ, σ2
ns2
X.
1Borrowed from http://math.hws.edu/javamath/ryan/Regression.html
1
pf2

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Math 6010, Fall 2004: Homework

Homework 1 Consider the following hypothetical data; it is regarding a particular bird species:^1

Wing length (cm) Age (days) 1.5 4. 2.2 5. 3.1 8. 3.2 9. 3.2 10. 3.9 11. 4.1 12. 4.7 14. 5.2 16.

Use simple linear regression to predict the age of a newly-caught bird whose wing is 4.0 centimeter long. Assuming normal errors, test the hypothesis (95%) that wing length and age are linearly related.

Solution. Let the ages be denoted by y 1 ,... , y 9 , and the wing-lengths by x 1 ,... , x 9. You should compute:

x¯ ≈ 3. 46 sx ≈ 1. 17 y¯ ≈ 9. 89 sy ≈ 3. 92 Corr(x, y) ≈ 0 .99.

Now recall that the regression line is described by

y = ¯y + β̂ (x − ¯x),

where β̂ = Corr(x, y) sy sx

So the regression line is (approximately) given by

y = 9.89 + 3.3(x − 3 .46).

Plug in x = 4 to obtain the regression estimate:

y = 9.89 + 3.3(4 − 3 .46) ≈ 11. 21.

This answers the first question. To answer the second question recall that

β̂ ∼ N

β,

σ^2 ns^2 X

(^1) Borrowed from http://math.hws.edu/javamath/ryan/Regression.html 1

2

Here, n = 9. Pretend that this is large enough. [This is not so good, and will be addressed later on.] Then, one would expect that σ^2 = Var(y) ≈ s^2 y ≈ 3. 922 ≈ 15. 37. Also, s^2 x ≈ 1. 172 ≈ 1 .37. So, we would

expect β̂ to have approximately a normal distribution with mean β and variance

  1. 37 9 × 1. 37

Under H 0 , β̂ ≈ N (0, 1 .25).

Therefore, the sample’s β̂ = 9.89 gives us a P-value of ≈ 0 .0. I.e., good reason to believe that H 0 is false; i.e., β 6 = 0; i.e., there is some linear relation between x and y.