How can I use calculus to find the equation for a curve from given data points?

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    Calculus
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Discussion Overview

The discussion revolves around using calculus and statistical concepts to derive equations for curves based on given data points, particularly in the context of probability distributions and demand forecasting. Participants explore how to create equations from data, integrate functions, and the applicability of normal distribution in real-world scenarios.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant questions the relationship between z-tables and integrals, suggesting that the values in z-tables represent areas under the normal distribution curve.
  • Another participant mentions that there is no closed formula for the indefinite integral related to the normal distribution.
  • Some participants propose using quadrature formulas like Simpson's rule for numerical integration, noting that the context of the data needs clarification.
  • A participant expresses skepticism about the normal distribution's applicability to their t-shirt sales data, which they describe as having multiple peaks and not following a neat distribution.
  • There is a request for assistance in creating a probability density function from sales data, with a focus on finding a breakeven point between sales and spoilage.
  • One participant asserts that the normal distribution is not a polynomial and provides the formula for the normal distribution function, while another participant suggests that the normal distribution curve could be approximated by a polynomial.
  • Some participants share their experiences using Excel to visualize data, noting discrepancies between expected normal curves and the shapes they observed.

Areas of Agreement / Disagreement

Participants express differing views on the applicability of the normal distribution to their data, with some advocating for its use while others argue against it. There is no consensus on how to derive the necessary equations or the best methods for integration and analysis.

Contextual Notes

Participants highlight limitations in their understanding of the z-table, probability density functions, and the integration process. There are unresolved questions about the definitions and assumptions underlying their data and models.

GiTS
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Maybe this should be in calculus instead...

I'm getting reaquainted with stats and I ran across the z-table again. I always wondered how the value in the table are populated.

As I understand it, the values in the table are just the value along the X axis and the corresponding area for a normal distribution. Wouldn't this be the same as find the area under the curve using an integral in calculus?

I don't know the equation for the curve used in creating the z-table.

Anyway, if what I said is true, then I should be able to make an equation for whatever curve is generated by the data. I made up some data below with a strong left curve.

X value Y value
1 1
2 2
3 3.3
4 5
5 7
6 9
7 11
8 11
9 9
10 2

1. How do I make an equation using these data points?
2. Once I have that equation, how do I integrate?

Note: I will answer Qs 1 and 2 soon. I am wondering if my original thoughts on how a z-table are created are true. When I asked in class some 2 years ago I remember my prof saying "you don't want to go into that, it's complicated"! :P

Thanks PF crew!
 
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We generally use suitable quadrature formula for the integral values. Simpson's 2/3 or Weddle formula are very good with short intervals like 0.1 to 0.5 etc. We can take smaller intervals for higher desired degree of accuracy.
PS. What is Y in your data and how the question of z score arises is not clear.
 
Last edited:
Thanks for sending me in the right direction.
The Y values are the # of observations.

What's the point of using the Z table at all? I'm trying to predict the likelihood of demand going above the average demand (and by how much). Why can't I just look at the past 10 orders and use a more precise function than a Z table?
I sell t-shirts. Here’s the data for t-shirts sold in the past 10 weeks. (Fictional product and units)
Code:
10724
13119
13576
13711
13849
19285
19734
19978
20760
21077
What I want to know is how much inventory(X) of t-shirts I need to not run out 95% of the time. Basically, what is the x value when the probability (shirts sold < X) = .95.

T-shirt demand doesn’t follow a nice neat normal distribution. There are booms and busts but seldom is the average number of shirts sold. Therefore, I would not want to use a normal distribution.
Some t-shirts have multiple, but predictable, humps. That is, if you plotted the distribution it would have 3 or 4 crests and valleys.
Can I just use the data to make the “z-table”.
I have actually forgotten how to turn those values into a distribution graph. Help on that would be appreciated.

Thank you,
-GITS
 
Does anyone know how to find the f(x) function for the probability density function? I did not understand the wikipedia article.

The problem is to find the breakeven point between sales and spoilage. I have the mean sales and the standard of deviation for the sales. I know that if the product is sold, I will get $100. If the product spoils I will lose $50. I sell 1000 units on average with a standard deviation of 250 units. How many units should I order above the mean? Basically I am looking for X. Problem is I can't use a z table in a breakeven calculation. I need to have the actual function.

So if the function was x^2 *250x - .5x^2 *250x I could solve for X. As it stands I'm not sure what to do to find said function. It's got to be a simple one given that I only need half the function.

Thanks!

Ideas? The normal distribution curve has to be a polynomial
 
GiTS said:
Does anyone know how to find the f(x) function for the probability density function? I did not understand the wikipedia article.

The problem is to find the breakeven point between sales and spoilage. I have the mean sales and the standard of deviation for the sales. I know that if the product is sold, I will get $100. If the product spoils I will lose $50. I sell 1000 units on average with a standard deviation of 250 units. How many units should I order above the mean? Basically I am looking for X. Problem is I can't use a z table in a breakeven calculation. I need to have the actual function.

So if the function was x^2 *250x - .5x^2 *250x I could solve for X. As it stands I'm not sure what to do to find said function. It's got to be a simple one given that I only need half the function.

Thanks!

Ideas? The normal distribution curve has to be a polynomial
No. The normal distribution curve is not a polynomial. If N is the "normal function" with parameters μ and σ, then ##\displaystyle N(x;\mu,\sigma)=\frac{e^{-\frac{1}{2}(\frac{x-\mu}{\sigma})^2}}{\sigma\sqrt{2\pi}}##. This is not a polynomial.

If you want a polynomial, consider ##\displaystyle \sum_{n=0}^{\infty}\frac{d^n}{dx^n}\left.\left[\frac{e^{-\frac{1}{2}(\frac{x-\mu}{\sigma})^2}}{\sigma\sqrt{2\pi}}\right]\right|_a\frac{(x-a)^n}{n!}##. :smile:
 
I put the equation into excel and got a line that looks more like an exponential than a normal curve. Mean= 0, Stdev = 2, X=1-99.
 
GiTS said:
I put the equation into excel and got a line that looks more like an exponential than a normal curve. Mean= 0, Stdev = 2, X=1-99.
Out of curiosity, how on Earth does a line look like an exponential? :-p

You should be careful to put in the equation correctly. The definition of the normal curve is the formula given above.
 

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