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EastWind [94]
2 years ago
6

For a set of data: x = (0,1,2,3,4,5,6) and y=(36, 28, 25, 24, 23, 21, 19), is it wise to use a linear regression to extrapolate

data for x = 50? Solution: Since the coefficient of determination is 0.8582, the linear model is a reasonably good fit for the data, so extrapolation for any x-value is acceptable. What is wrong with this solution?
Mathematics
1 answer:
melisa1 [442]2 years ago
4 0

Answer:

The problem with this solution is that a regression model is not recommended to extrapolate because we do not know if the linear relation that we calculated for a specific range of x values still holds outside this range.

Step-by-step explanation:

We have a linear regression model, with a range of the independent variable "x" that goes from 0 to 6.

The regression model finds a good fit (r=0.8582).

As it has a good fit, it is proposed to use this model to extrapolate and calculate the value of y for x=50.

It is not recommended to extrapolate a regression model unless we are really sure that the model is still valid within the range within we are extrapolating.

This means that if we have no proof that y has a linear relation in a range of x that includes x, the extrapolation has no validity and can lead to serious errors.

A linear regression model is only suitable for interpolation or extrapolating within the range we are sure that the relation between y and x is linear within a certain acceptable error.

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Step-by-step explanation:

We have been given that an amount of $53,000 is placed in an investment account that grows at a fixed rate of 2% (compound growth) per year. We are asked to find the amount in the account after 4 years.

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Upon substituting our given values in compound interest formula we will get,

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