Use Linear Approximation to Find Margin of Error

In summary: Where do you get 2 margins of error? I just see one, and that is the one that guarantees |exp(x) - 1 - x| ≤ margin for all x in (-1,1).A more serious consideration is that *maybe* you are not supposed to be able to compute exp(x) at all yet (so cannot do the computations above), but are somehow expected to be able to place an upper bound... somewhere? on the magnitude of the error?
  • #1
Painguy
120
0

Homework Statement


For x near 0, local linearization gives the following equation.
e^x ≈ 1 + x

Estimate to one decimal place the magnitude of the error for −1 ≤ x ≤ 1.

Homework Equations



The Attempt at a Solution


I'm no exactly sure what to do here to be honest, but what I thought I'd do is try to work backwards. Generally I'd be given a margin of error to be accurate within, but instead I am given the values that are accurate within the margin of error(i assume). So what I did was take -1 & 1 and stick it in |e^x - 1+x|. I got 1/e and e-2. I took the difference of the two and got .3504. I'm pretty sure this is wrong I think I messed up when I took 1 & 1 for |e^x - 1+x|.
 
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  • #2
Painguy said:

Homework Statement


For x near 0, local linearization gives the following equation.
e^x ≈ 1 + x

Estimate to one decimal place the magnitude of the error for −1 ≤ x ≤ 1.

Homework Equations



The Attempt at a Solution


I'm no exactly sure what to do here to be honest, but what I thought I'd do is try to work backwards. Generally I'd be given a margin of error to be accurate within, but instead I am given the values that are accurate within the margin of error(i assume). So what I did was take -1 & 1 and stick it in |e^x - 1+x|. I got 1/e and e-2. I took the difference of the two and got .3504. I'm pretty sure this is wrong I think I messed up when I took 1 & 1 for |e^x - 1+x|.

Have you studied power series, and particularly Maclaurin and Taylor series? If so, look up the Taylor Remainder Theorem.
 
  • #3
Mark44 said:
Have you studied power series, and particularly Maclaurin and Taylor series? If so, look up the Taylor Remainder Theorem.

Unfortunately I haven't covered teh topic of series yet. I've heard of them though, but I'm sure that's not good enough :P

----Edit-----
I took a look at the Taylor Remainder Theorem, and I understand the concept well enough to give a simple explanation, but I still don't feel comfortable using it. Just to make sure that I do understand your suggestion correctly, if I tried to estimate the Remainder using the form q<=f^(k+1)(x)<=Q (k being an arbitrary value I assign for accuracy) that should give me the correct solution right?
 
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  • #4
Painguy said:

Homework Statement


For x near 0, local linearization gives the following equation.
e^x ≈ 1 + x

Estimate to one decimal place the magnitude of the error for −1 ≤ x ≤ 1.

Homework Equations



The Attempt at a Solution


I'm no exactly sure what to do here to be honest, but what I thought I'd do is try to work backwards. Generally I'd be given a margin of error to be accurate within, but instead I am given the values that are accurate within the margin of error(i assume). So what I did was take -1 & 1 and stick it in |e^x - 1+x|. I got 1/e and e-2. I took the difference of the two and got .3504. I'm pretty sure this is wrong I think I messed up when I took 1 & 1 for |e^x - 1+x|.

Why would you take the difference? When x = +1 the error is e -2 ≈ 0.718, and when x = -1 the error is exp(-1)-1+1 = exp(-1) ≈ 0.368. The difference of these two numbers has no meaning at all in this question.

RGV
 
  • #5
Ray Vickson said:
Why would you take the difference? When x = +1 the error is e -2 ≈ 0.718, and when x = -1 the error is exp(-1)-1+1 = exp(-1) ≈ 0.368. The difference of these two numbers has no meaning at all in this question.

RGV

Well I was never sure what to do in the first place, but since the site requires me to attempt something that's what I did. Can't really explain it. :tongue2: I'm still a bit confused on what to do with 2 margin of errors. I feel like .7 is the margin of error, but I'm not sure why.
 
  • #6
Painguy said:
Well I was never sure what to do in the first place, but since the site requires me to attempt something that's what I did. Can't really explain it. :tongue2: I'm still a bit confused on what to do with 2 margin of errors. I feel like .7 is the margin of error, but I'm not sure why.

Where do you get 2 margins of error? I just see one, and that is the one that guarantees |exp(x) - 1 - x| ≤ margin for all x in (-1,1).

A more serious consideration is that *maybe* you are not supposed to be able to compute exp(x) at all yet (so cannot do the computations above), but are somehow expected to be able to place an upper bound on the error anyway. I don't know if that is what the questioner wants.

RGV
 
  • #7
Ahhhh i see what I was doing wrong. I went ahead a graphed it and got a better visualization of what you are saying. Out of the two results you got (.718, and .368) .718 fits the inequality |exp(x) - 1 - x| ≤ margin. So .368≤ margin (margin=.718) works wit that. Is that right?
 
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1. What is linear approximation in statistics?

Linear approximation is a method used in statistics to estimate an unknown value by using a known value that is close to it. This is done by assuming that the relationship between the two values is linear and using that line to make the estimation.

2. How is linear approximation used to find margin of error?

Linear approximation can be used to find the margin of error by approximating the standard error of a sample mean using a linear approximation. This is done by using the formula: margin of error = (z-score)(standard deviation)/sqrt(n), where z-score is the critical value, standard deviation is the measure of variability in the population, and n is the sample size.

3. What are the assumptions made when using linear approximation?

The assumptions made when using linear approximation are that the relationship between the known and unknown values is linear, and that the values being estimated are close enough for the approximation to be accurate. Additionally, the known value should be a good representation of the unknown value.

4. Can linear approximation be used with any type of data?

No, linear approximation is most commonly used with numerical data that follows a linear pattern. It may not be appropriate for non-linear data or data that has a large amount of variability.

5. How accurate is linear approximation in finding margin of error?

The accuracy of linear approximation in finding margin of error depends on the accuracy of the assumptions made. If the assumptions are reasonable and the data follows a linear pattern, then linear approximation can provide a relatively accurate estimate of the margin of error. However, if the assumptions are not met, the accuracy of the approximation may be compromised.

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