Calculate the standard errors of ˆα and βˆ.

Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter2: Exponential, Logarithmic, And Trigonometric Functions
Section2.CR: Chapter 2 Review
Problem 111CR: Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data...
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A) Calculate the standard errors of ˆα and βˆ.

B) Use a t-test to test H0 : β = 0 versus H1 : β < 0 with significance level 5%. 

We are interested in using the pH of the lake water (which is easy to measure) to predict the
average mercury level in fish from the lake, which is hard to measure. Let x be the pH of the
lake water and Y be the average mercury level in fish from the lake. A sample of n = 10 lakes
yielded the following data:
Observation (i)
1
3
4
5
6
8
10
pH (x;)
Average mercury level (y;) 0.15
8.2
8.4
7.0
7.2
7.3
6.4
9.1
5.8
7.6
8.1
0.04
0.40
0.50
0.27
0.81
0.04
0.83
0.05
0.19
Suppose we fit the data with the following regression model:
Y; = a + Bx;+ Ei, i =
1,..., 10,
where ɛi ~
· N(0, o²)
are independent. We have the following quantities: ī = E–1 Xi = 7.51,
n Li=1
vi=1
i=1
i=1
Some R output that may help.
> p1 <- c(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99)
> qt (p1, 8)
[1] -2.896 -2.306 -1.860 -1.397
1.397
1.860
2.306
2.896
> qt (p1, 9)
[1] -2.821 -2.262 -1.833 -1.383
1.383
1.833
2.262
2.821
Transcribed Image Text:We are interested in using the pH of the lake water (which is easy to measure) to predict the average mercury level in fish from the lake, which is hard to measure. Let x be the pH of the lake water and Y be the average mercury level in fish from the lake. A sample of n = 10 lakes yielded the following data: Observation (i) 1 3 4 5 6 8 10 pH (x;) Average mercury level (y;) 0.15 8.2 8.4 7.0 7.2 7.3 6.4 9.1 5.8 7.6 8.1 0.04 0.40 0.50 0.27 0.81 0.04 0.83 0.05 0.19 Suppose we fit the data with the following regression model: Y; = a + Bx;+ Ei, i = 1,..., 10, where ɛi ~ · N(0, o²) are independent. We have the following quantities: ī = E–1 Xi = 7.51, n Li=1 vi=1 i=1 i=1 Some R output that may help. > p1 <- c(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99) > qt (p1, 8) [1] -2.896 -2.306 -1.860 -1.397 1.397 1.860 2.306 2.896 > qt (p1, 9) [1] -2.821 -2.262 -1.833 -1.383 1.383 1.833 2.262 2.821
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