Sample standard deviation proof

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SUMMARY

The discussion focuses on proving that if \( y_{i} = ax_{i} + b \), then the standard deviation \( s_{y} \) is equal to \( |a|s_{x} \). Participants emphasize the importance of starting the proof with variance rather than standard deviation, as variance is less affected by translation and more by scaling. The proof involves manipulating the means and variances of the two sets of observations to establish the relationship definitively.

PREREQUISITES
  • Understanding of standard deviation and variance concepts
  • Familiarity with linear transformations in statistics
  • Knowledge of basic algebra and manipulation of equations
  • Experience with statistical notation and terminology
NEXT STEPS
  • Study the properties of variance and standard deviation in detail
  • Learn about linear transformations and their effects on statistical measures
  • Explore proofs related to the independence of standard deviation from translation
  • Practice deriving standard deviation from variance in various contexts
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Students studying statistics, educators teaching statistical concepts, and anyone interested in understanding the mathematical foundations of standard deviation and variance.

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Homework Statement



Let x_{1},...,x_{n} be n observations. If y_{1},...,y_{n} is another set of observations s.t. y_{i}=ax_{i}+b , prove that s_{y}=|a|s_{x} .

The Attempt at a Solution




Attempt at a proof: Since \bar{y}=a\bar{x} +b then \bar{x}=(\bar{y}-b)/a and s_{x}=\sqrt{\frac{1}{n-1}\sum(x_{i}-\frac{\bar{y}-b}{a})}. This is where I get stuck. Any ideas?
 
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First of all, you should know that standard deviation is independent of translation (change of origin), but is affected by scale. See if you can knock out the proofs of each separately, and then you should be able to put them together. Also, begin your proofs by using the variance, not the standard deviation.
 
and it will probably be easier to calculate s_y directly and compare with the form of s_x
 

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