text: # Shifting Data
  
  Shifting data is adding a constant k to each member of a data set, where k is a real number. In visual terms, it is lifting the entire distribution of data points and shifting en masse a distance of k.
  
  ## Shifting Data and the Mean & Median
  
  When data is shifted the mean(μ) and median are shifted by the constant k.
  
  That is to say, when you shift a data set by k, so that f(x)= x+k for every x in your data set,
  
  • f(μ)= μ + k
  • f(med)= med + k
  
  Note that k can be either positive or negative.
  
  ## Measures of Spread and Relative Standing
  
  Your standard deviation, variance, z scores and percentile values all remain unchanged when your data set is shifted. Since every point in your data set moves the exact same distance, there is no change in their relations to each other.
  
  ## An Example of Shifting Data
  
  Suppose that you were running a research project on the items people packed into USPS #4 Priority Mail Box. You collect your data and do your analysis, and then realize that although you’ve worked with the net weights only the contents of the boxes are significant to your research. Suppose your measurements are in ounces. Since each box weighs 5 ounces, k= -5.
  
  • If your mean was 41 before the shift, it is now 36.
  • If your median was 28, it is now 23.
  • If your standard deviation was 16, it is still 16.
  • Your variance will stay the same, as will your z score.
  
  ## Shifting Data and Rescaling
  
  There are some cases when you may have to do both a data shift and a rescale—for instance, when you need to change temperature readings from Fahrenheit to Celsius degrees, where the temperature in Fahrenheit, F = [(9/5)*C]+32.
  
  In this case, first shift your data by k= -32, and apply this additive constant to your mean and median. The standard deviation and variance will remain unchanged for this step. Then rescale, and multiply your mean and standard deviation by the rescaling constant 5/9 to find the mean and standard deviation for your data set in Celsius.
  
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  If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.
  
  Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!
  Shifting Data was last modified: March 17th, 2018 by
explanation: The document provides an explanation of the concept of shifting data, which involves adding a constant to each member of a data set. It covers how this shift affects the mean, median, and measures of spread such as standard deviation, variance, z scores, and percentile values. The document includes an example of shifting data in the context of measuring the net weights of items in a mail box, and it also discusses the process of shifting and rescaling data, such as converting temperatures from Fahrenheit to Celsius.
  
  The document does not contain a specific question or a question and answer.
distilabel_metadata.content: Evaluate the user given text extract and determine if it contains a math question and optionally and answer to this question.
  
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  1. Briefly describe the document and mention if it contains a question or a question and answer.
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distilabel_metadata.role: system
distilabel_metadata.content: # Shifting Data
  
  Shifting data is adding a constant k to each member of a data set, where k is a real number. In visual terms, it is lifting the entire distribution of data points and shifting en masse a distance of k.
  
  ## Shifting Data and the Mean & Median
  
  When data is shifted the mean(μ) and median are shifted by the constant k.
  
  That is to say, when you shift a data set by k, so that f(x)= x+k for every x in your data set,
  
  • f(μ)= μ + k
  • f(med)= med + k
  
  Note that k can be either positive or negative.
  
  ## Measures of Spread and Relative Standing
  
  Your standard deviation, variance, z scores and percentile values all remain unchanged when your data set is shifted. Since every point in your data set moves the exact same distance, there is no change in their relations to each other.
  
  ## An Example of Shifting Data
  
  Suppose that you were running a research project on the items people packed into USPS #4 Priority Mail Box. You collect your data and do your analysis, and then realize that although you’ve worked with the net weights only the contents of the boxes are significant to your research. Suppose your measurements are in ounces. Since each box weighs 5 ounces, k= -5.
  
  • If your mean was 41 before the shift, it is now 36.
  • If your median was 28, it is now 23.
  • If your standard deviation was 16, it is still 16.
  • Your variance will stay the same, as will your z score.
  
  ## Shifting Data and Rescaling
  
  There are some cases when you may have to do both a data shift and a rescale—for instance, when you need to change temperature readings from Fahrenheit to Celsius degrees, where the temperature in Fahrenheit, F = [(9/5)*C]+32.
  
  In this case, first shift your data by k= -32, and apply this additive constant to your mean and median. The standard deviation and variance will remain unchanged for this step. Then rescale, and multiply your mean and standard deviation by the rescaling constant 5/9 to find the mean and standard deviation for your data set in Celsius.
  
  ------------------------------------------------------------------------------
  
  If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.
  
  Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!
  Shifting Data was last modified: March 17th, 2018 by
distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The document provides an explanation of the concept of shifting data, which involves adding a constant to each member of a data set. It covers how this shift affects the mean, median, and measures of spread such as standard deviation, variance, z scores, and percentile values. The document includes an example of shifting data in the context of measuring the net weights of items in a mail box, and it also discusses the process of shifting and rescaling data, such as converting temperatures from Fahrenheit to Celsius.
  
  The document does not contain a specific question or a question and answer.
  
  Label: no-question