Yes. The exception arises when you have outliers.
The straight line that best fits the data on a coordinate plane is the Line Of Best Fit.
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Because it fits the data. That's an extremely vague answer, but it was an extremely vague question.
You have to choose one that fits the available data. Check the relationship between the data you know, for example an angle between two sides, etc.
Curvie fitting is used in mathematics to find a mathematicalmodel that fits your data. The curve fit fins the specific parameters which make that function match your data as closely as possible.
A straight line which best describes the data on a scatter plot is called a "line of best fit". The line could pass through some of the points, all of them, or none of them.
The straight line that best fits the data on a coordinate plane is the Line Of Best Fit.
False. When solving for the slope of the best fit line, you should consider all data points in your dataset to find the line that best fits the overall trend. Choosing points closest to the line or on the line may bias your results and not accurately represent the relationship between the variables.
Oh, honey, let me break it down for you. A trend line is a general direction showing the overall trend of data points, while a line of best fit is a specific line that minimizes the distance between the line and the data points. So basically, a trend line is like a rough sketch, and a line of best fit is like the tailor-made suit that hugs those data points just right.
A best fit graph to some data is exactly that: it is a line which fits the data best according to some optimality criterion. There is a always a trade off in fitting a line to data: one can change the number of degrees of freedom of the underlying equation, which affects how close the line can get to the data points. With more degrees of freedom, the line can more closely approximate the data. This is not to say that more degrees of freedom are better: with too many degrees of freedom, one is merely fitting to the noise in the measurement of the data, and the line will predict subsequent data poorly, when both interpolating and extrapolating the existing data. This is an example of Occam's Razor: one must pick the simplest model which adequately fits the data.
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The line of best fit is also known as the least square line. It uses a statistical technique to determine the line that fits best through a series of scattered data (plots). Using regression analysis, it finds the line that minimizes the amount of errors (deviations - the sum of vertical distance of data points from the line. The result is a unique line that minimizes the total squared deviations, statistically termed the sum of squared errors.
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It fits the known data.
IT fits Utwonk
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The objectives of selecting a data type for a field include ensuring data integrity by restricting the type of values that can be stored, optimizing storage space by choosing the most appropriate data type for the data being stored, and facilitating efficient data retrieval and manipulation by selecting a data type that best fits the operations that will be performed on the data.