Proportional Error Definition

Toys Science & Nature Science Difference Between Constant & Proportional Error Difference Between Constant & Proportional Error By William Rockwell eHow Contributor William Rockwell Follow  Pin  Share  Tweet Share

Proportional Definition Physics

 Email Save Understanding the difference between constant and proportional error in statistical proportional definition deutsch analysis will allow a function to be properly graphed. Once a graph is completed any value on the y axis

Inversely Proportional Definition

can be found if the x value is known and vice versa. Constant Error A constant error is an average of the errors over the range of all data. The x value will inversely proportional definition chemistry be independent of the y value. For example, an affixed scale will always have deviation from the zero setting whether the item being weighed is 100 lbs., 600 lbs. or anywhere in between and this error has nothing to do with the actual weight of the object. The average deviation of a single instance will decrease as the number of instances increases. Proportional Error Proportional error is directly proportional definition an error that is dependent on the amount of change in a specific variable. So the change in x is directly related to the change in y. This change is always an equally measurable amount so that x divided by y always equals the same constant. The amount of error will always be a consistent percentage. Indeterminate Error An indeterminate error is one that is neither constant or proportional. These errors are often the result of observer bias or inconsistent methodology during an experiment. Indeterminate errors can also be a sign that there is absolutely no correlation between the two items being compared. In cases like this it is important to revisit all facets of data collection including experimental bias and inconsistent measurements. Graphing A constant error will be reflected in a change in the y intercept on the graph. A proportional error will change the slope of the line on the graph. Indeterminate errors will cause a scatter plot effect on the graph, making the determination of the line of best fit impossible. References Duke University Department of Chemistry: Systematic Errors Promoted By Zergnet Comments Please enable JavaScript to view the comments powered by Disqus. Related Searches R

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Proportional Representation

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Proportional Error Physics

of this question. Would you like to make it the primary and merge this question into it? MERGE CANCEL exists and is an alternate of . Merge this http://www.ehow.com/info_12185218_difference-between-constant-proportional-error.html question into Split and merge into it SAVE CANCEL Edit Answer by Meowijan Confidence votes 33 A constant error is something that does not change as the variable you are observing changes. For example, a set of scales that are always 0.3kg off. No matter who is standing on them, they will always get a reading that http://www.answers.com/Q/What_is_the_differences_between_constant_and_proportional_error is 0.3kg greater than their actual mass. A proportional error changes as the variable you are observing changes, but more importantly it changes in a way that can be predicted. A constant error is something that does not change as the variable you are observing changes. For example, a set of scales that are always 0.3kg off. No matter who is standing on them, they will always get a reading that is 0.3kg greater than their actual mass.

A proportional error changes as the variable you are observing changes, but more importantly it changes in a way that can be predicted. Minor edit? Save Cancel 15 people found this useful Was this answer useful? Yes Somewhat No Thanks for the feedback! Follow Malcolm Freberg Q&A TV personality: Contestant from TV's "Survivor", currently hosting "Wayfaring" What is one place you haven't visited that you want to check off your bucket list? View Full Interview Laurenblack 132 Contributions What is different between constant and integer? A constant is a v

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PRE measures. Proportional Reduction of Error (PRE) The concept that underlies the definition and interpretation of several measures of association, PRE measures are derived by comparing the errors made in predicting the dependent while ignoring the independent variable with errors made when making predictions that use information about the independent variable. E1 = errors of prediction made when the independent variable is ignored E2 = errors of prediction made when the prediction is based on the independent variable "All PRE measures are based on comparing predictive error levels that result from each of two methods of prediction" (Frankfort-Nachmias and Leon-Guerrero 2011:366). Table 12.1 on page 366 of the textbook helps us to understand this. The independent variable is number of children; the dependent variable is support for abortion. Content on this page requires a newer version of Adobe Flash Player. Two of the most commonly used PRE measures of association are lambda (λ) and gamma (γ). Two PRE Measures: Lambda and Gamma Lambda λ Appropriate for: Nominal Variables Gamma γ Appropriate for: Ordinal and Dichotomous Nominal Variables