Dummy Variables in Taylor Series

In summary, the conversation discusses the use of dummy variables in Taylor Series and specifically applies it to the function g(x) = xlnx in terms of (x-2). The speaker's original method involves using a dummy variable, but they get stuck in their calculations. The book's answer, which uses the method of taking successive derivatives, is also provided. The suggestion is made to combine the two sums to see if they match.
  • #1
AngelofMusic
58
0
Taylor Series in x-a

Hi,

I've got a question about the use of dummy variables in Taylor Series.

We are asked to expand:

g(x) = xlnx

In terms of (x-2). So originally, I used a dummy variable approach to try and find an answer.

Let t = x-2, so x = t+2.

g(x) = (t+2)ln(2+t)

We're given:

[tex]ln(1+x) = \sum_{k=1}^{\infty}\frac{(-1)^k}{k}x^k[/tex]

So, I re-arrange g(x) = (t+2)ln[2(1+t/2)] = (t+2)(ln2 + ln(1+t/2)).

Expanding, I get: g(x) = tln2 + tln(1+t/2) + 2ln2 + 2ln(1+t/2).

So [tex]g(x) = 2ln2 + (x-2)ln2 + (x-2)\sum_{k=1}^{\infty}\frac{(-1)^k}{k}(x/2-1)^k + [/tex][tex]2\sum_{k=1}^{\infty}\frac{(-1)^k}{k}(x/2-1)^k.[/tex]

This is where I get stuck because the book's answer says it should be:

[tex]g(x) = 2ln2 + (1+ln2)(x-2) + \sum_{k=2}^{\infty}\frac{(-1)^k}{k(k-1)2^{k-1}}(x-2)^k[/tex]

I'm sort of close to the answer, but can't quite make the final manipulations to make it work. The book used the method of taking successive derivatives to arrive at that formula, so I know how to do it that way - but is my original method correct as well? If not, why not?
 
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  • #2
Your method should work out.

Before we work too much on it, why don't you try putting the two sums you have into one and see if it doesn't match up then?

cookiemonster
 
  • #3




Hi there,

Thank you for sharing your approach to expanding g(x) = xlnx in terms of (x-2) using dummy variables. Your approach is definitely correct and it is a valid way of solving this problem. However, the book's answer uses a different approach which involves taking successive derivatives of g(x) and using the general formula for the Taylor Series expansion of a function.

The reason why your answer might not match exactly with the book's answer is because of the use of dummy variables. When we use dummy variables, we are essentially substituting x with another variable (in this case, t). This can sometimes lead to slight differences in the final answer compared to the traditional approach of taking derivatives and using the general formula.

But don't worry, both approaches are correct and it's always good to have multiple ways of solving a problem. Keep up the good work!
 

1. What are dummy variables in Taylor series?

Dummy variables in Taylor series are additional variables that are introduced to represent different categories or levels of a categorical variable in a regression model. They are used to handle non-numerical data in a regression analysis.

2. How are dummy variables created?

Dummy variables are created by assigning a numerical value of 0 or 1 to each category or level of a categorical variable. For example, if a variable has three categories A, B, and C, then three dummy variables will be created: DA, DB, and DC. If an observation belongs to category A, then DA=1 and DB=DC=0.

3. What is the purpose of using dummy variables in Taylor series?

The purpose of using dummy variables in Taylor series is to include categorical variables in a regression model and account for their effect on the outcome variable. They allow for the comparison of the different categories or levels of a categorical variable and help to avoid the assumption of linearity in the relationship between the categorical variable and the outcome variable.

4. How do dummy variables affect the interpretation of regression coefficients?

When dummy variables are included in a regression model, the regression coefficients for the dummy variables represent the difference in the outcome variable between the reference category (assigned a value of 0) and the category represented by the dummy variable. This allows for the comparison of the effect of each category on the outcome variable.

5. Can dummy variables be used for more than one categorical variable in a regression model?

Yes, dummy variables can be used for multiple categorical variables in a regression model. However, it is important to avoid multicollinearity, which occurs when two or more dummy variables are highly correlated. This can be avoided by using a reference category for each categorical variable and dropping one of the dummy variables for that category.

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