Questions regarding dummy variables

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In summary: It is not a dummy variable, it is just a name for the variable of integration. The upper limit of t is just some variable that is used with the lower limit of t0 to indicate the interval of integration. In summary, the fundamental theorem of calculus justifies the use of dummy variables when dealing with initial value problems in differential equations.
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Math451
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I am sorry guys I don't know how to edit latex stuff.



====


Is there anyone who can tell me why we are justified in using dummy variables when we are dealing with some inital problems?

suppose, some differential equation has a solution and has the intial condition.


[tex]y(t) = \int (p(t)) dt[/tex]

[tex]y(t_{0}) = y_{0}








now suppose we cannot intergrate p(t), then my professor mentioned that

we can introduce a dummy variable so that

the variable becomes somelike s and the

the upperbound becomes t and the lowerbound becomes [tex]t_{0}[/tex].





My question is,

what theorem says that the initial condition value of [tex]t_{0}[/tex]must be the lowerbound, once we introduce a dummy variable?

Can we have the upperbound as [tex]t_{0}[/tex] and the lowerbound as t?

if not, why can [tex]t_{0}[/tex] be only the lowerbound?



The pace in my differential equation course is really fast, so we are far passed the point of thinking about the legitimacy of dummy variables in class but I am so frustrated that I don't quite have a firm understanding on this.

I don't want to use dummy variables just because my professor tells me it is okay to do so.


Please help me.

Thank you.
 
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Your post is basically unreadable and I can only guess at what is really bothering you. Since I think I know, I will take a shot at it. Apparently you have a differential equation with a solution written in the form:

[tex]y(t) = \int\ p(t)\, dt[/tex]

and which has initial condition:

[tex]y(t_0) = y_0[/tex]

and you are wondering why the solution can be written as:

[tex]y(t) = \int_{t_0}^t p(u)\, du + y_0[/tex]

Remember the fundamental theorem of calculus that says if F is any antiderivative of f, then:

[tex]F(b) - F(a) = \int_a^b\ f(u)\, du[/tex]

In the first equation above, y(t) is an antiderivative of p(t), so by the fundamental theorem of calculus:

[tex] y(t) - y(t_0) = \int_{t_0}^t\ p(u)\, du[/tex]

Since y(t0) = y0, put that in and solve for y(t) to get your equation.
 

1. What are dummy variables?

Dummy variables are variables that take on binary values (usually 0 and 1) to represent different categories or groups in a dataset. They are often used in regression analysis to represent categorical data and allow for the inclusion of categorical variables in statistical models.

2. When should I use dummy variables?

Dummy variables should be used when you have categorical data that cannot be represented numerically, or when you want to compare the effects of different categories on a dependent variable in a regression model. They are also useful for avoiding multicollinearity in regression analysis.

3. How do I create dummy variables?

Dummy variables can be created by assigning a numerical value (usually 1 or 0) to each category in a categorical variable. For example, if a variable has three categories (A, B, C), you would create three dummy variables (D1, D2, D3) and assign a value of 1 to the category and 0 to the others. These dummy variables can then be used in a regression model.

4. Can I use dummy variables with more than two categories?

Yes, dummy variables can be used for categorical variables with any number of categories. However, it is important to remember to only include one less dummy variable than the total number of categories in a regression model to avoid multicollinearity.

5. What is the interpretation of dummy variables in a regression model?

The interpretation of dummy variables in a regression model depends on the specific model and the different categories being compared. In general, the coefficient for a dummy variable represents the difference in the dependent variable between the category represented by the dummy variable and the reference category (usually the category with a value of 0). For example, if a dummy variable for gender has a coefficient of 0.5, it would indicate that the dependent variable is 0.5 units higher for the group represented by that dummy variable compared to the reference group.

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