Observation (i) pH (x;) Average mercury level (y;) 0.15 1 3 4 5 6. 7 8 9 10 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-α+ βα + εί, i= 1, . , 10, N(0, o?) are independent. We have the following quantities: = E1 xi = 7.51, 0.328, Σ 572.71, ΣΗ = 1.8922, ΣΗ πυ-22.218. where ɛ; ~ n Li=1 y = ÷Li=1Yi =

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.1: Measures Of Center
Problem 9PPS
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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)
pH (x;)
Average mercury level (y;)
1
3
6
7
9
10
8.2
8.4
7.0
7.2
7.3
6.4
9.1
5.8
7.6
8.1
0.15
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 ɛ; ~ N(0, o²) are independent. We have the following quantities: a = E1 ; = 7.51,
j = £i=1 Yi =
0.328, E1 x? = 572.71, 1 y? = 1.8922, -1 Tiyi = 22.218.
n
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
> qt (p1, 9)
[1] -2.821 -2.262 -1.833 -1.383
1.397
1.860
2.306 2.896
1.383
1.833
2.262
2.821
(a) Find the ordinary least squares (OLS) estimates (denoted as â and B) of the regression
coefficients (a and ß).
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) pH (x;) Average mercury level (y;) 1 3 6 7 9 10 8.2 8.4 7.0 7.2 7.3 6.4 9.1 5.8 7.6 8.1 0.15 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 ɛ; ~ N(0, o²) are independent. We have the following quantities: a = E1 ; = 7.51, j = £i=1 Yi = 0.328, E1 x? = 572.71, 1 y? = 1.8922, -1 Tiyi = 22.218. n 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 > qt (p1, 9) [1] -2.821 -2.262 -1.833 -1.383 1.397 1.860 2.306 2.896 1.383 1.833 2.262 2.821 (a) Find the ordinary least squares (OLS) estimates (denoted as â and B) of the regression coefficients (a and ß).
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