Sample mean and linear relation?

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SUMMARY

The discussion centers on the computation of the sample mean and its relationship to linear transformations. Specifically, the sample mean is calculated using the formula \(\bar{x}=\sum_{i=1}^{n}x_{i}/n\). The example provided involves winning scores from the U.S. Masters golf tournament, where transforming the scores by subtracting 280 simplifies the calculation of the mean. The key takeaway is that by selecting appropriate constants 'a' and 'b' in the linear equation \(y_{i}=ax_{i}+b\), the process of finding the mean can be streamlined, resulting in the same sample mean regardless of the constant chosen.

PREREQUISITES
  • Understanding of sample mean calculation
  • Familiarity with linear transformations in statistics
  • Basic knowledge of arithmetic operations
  • Concept of constants in linear equations
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  • Study the properties of linear transformations in statistics
  • Learn about the implications of choosing different constants in mean calculations
  • Explore the concept of intercepts in linear equations
  • Investigate the relationship between sample means and linear regression
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Students studying statistics, data analysts, and anyone interested in understanding the relationship between linear transformations and sample mean calculations.

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Homework Statement



The book states the following:

The sample mean is defined by: [tex]\bar{x}=\sum_{i=1}^{n}x_{i}/n[/tex]

The computation of the sample mean can often be simplified by noting that if for constants
a and b, [tex]y_{i}=ax_{i}+b[/tex], then the sample mean of the data set y1 , . . . , yn is: [tex]\bar{y}=\sum_{i=1}^{n}(ax_{i}+b)/n=\sum_{i=1}^{n}ax_{i}/n+\sum_{i=1}^{n}b/n=a\bar{x}+b[/tex]


Given the question:


The winning scores in the U.S. Masters golf tournament in the years from
1982 to 1991 were as follows: 284, 280, 277, 282, 279, 285, 281, 283, 278, 277

The book computes as follows:

Rather than directly adding these values, it is easier to first subtract 280 from
each one to obtain the new values yi = xi − 280, we obtain:

4, 0, −3, 2, −1, 5, 1, 3, −2, −3

ecause the arithmetic average of the transformed data set is
y_bar = 6/10
̄
it follows that
x_bar = y_bar + 280 = 280.6
(1) Why are we choosing 280?? What is the reason for that? I tried other numbers don't they don't give the same sample mean.

(2) I also want to confirm my understanding of the linear relation given the summation. I know the constant a should be the 1/n, as x1 / n + x2 / n + xn / n is the same as (x1+x2+xn)/n. Why do we need the b, the intercept? Is it just stating the obvious ,a general form? Or is it possible that we will encounter a statistical sample mean that cross the y?

But what I just said don't make sense to "yi = xi − 280". the constant a is 1...Can someone correct me? Thanks!
 
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Let's choose 20 instead of 280. Then, new data set becomes

264 260 257 262 259 265 261 263 258 257

hence, y_bar = 260.6. It follows that x_bar = 20 + 260.6 = 280.6

y = ax + b is the general form of the linear relation between x and y.
 
The book states that if the mean [tex]\bar{x}=\sum_{i=1}^{n}x_{i}/n[/tex]

Then for another distribution which is related as [tex]y_{i}=ax_{i}+b[/tex]

the mean is [tex]\bar{y}=a\bar{x}+b[/tex]

what they are trying to imply here is that by properly choosing 'a' and 'b' the process of finding mean can be simplified.

in the example solution you mentioned

a = 1, b = -280 (the values can be any real number, but it is chosen in such a way to make the problem simpler)

so the equation becomes y[itex]_{i}[/itex] = x[itex]_{i}[/itex] - 280

this effectively converts the problem into a much simpler problem involving small numbers. Once we compute [tex]\bar{y}[/tex]
we have to get back
[tex]\bar{x}[/tex]
This is done by solving [tex]\bar{y}=a\bar{x}+b[/tex]
( [tex]\bar{y}[/tex] , a ,b are known)

This gives the answer.
 

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