Regression Analysis for a Gamma function

In summary, the conversation discusses the use of a Gamma function in a regression analysis program and the possibility of modifying the program to include this function. The speaker also suggests using matrices to solve for the constants in the regression equation. The conversation is not related to homework and is based on a real situation involving flooding from a tropical storm.
  • #1
chicopee
12
0
[SOLVED] Regression Analysis for a Gamma function

My regression analysis program that I developed in BASICS back in the 1980's applies for half a dozen linear equations some of which are transormed into log forms. I would like to modify my program to include this Gamma function: I(t)=P*(t^s)*(e^(-ft)) which I can transform into this equivalent non- linear log form: Ln I(t)=Ln P + s*Ln t + (-ft); P,s and f are constants; t if for time; I(t) has for units cu.ft/sec or cu.m./sec. Is there any way to take care of the term (-ft). I got 18 data points available for this regression .

I am not looking forward to trial and error to determine the constants P,s and f.

Here is another thought. Since I have 18 data points (flow vs time), can I solve theses constants with matrices using this transformation: Ln I(t)=Ln P + s*Ln t + (-ft) eventho I would have 18 rows and and only 4 columns?
 
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  • #2
Note, my request is not homework. This is a real situation involving a flooding situation arising from tropical storm Tammy back in 2005.
 
  • #3
Why not run the regression y(t) = a + b1 x1(t) + b2 x2(t) + error, where y = log I, x1 = log t, x2 = t, and the parameters are a = Log P, b1 = s, b2 = -f?
 
  • #4
Yeah, I see what you mean. Instead of one independent variable, I'll two in that transformed equation. Thx.
 

1. What is regression analysis for a Gamma function?

Regression analysis for a Gamma function is a statistical technique used to analyze the relationship between a dependent variable and one or more independent variables. It is specifically used when the dependent variable follows a gamma distribution, which is commonly used to model skewed data with positive values.

2. What are the assumptions of regression analysis for a Gamma function?

The assumptions for regression analysis for a Gamma function include: the dependent variable follows a gamma distribution, the relationship between the dependent and independent variables is linear, the errors are normally distributed, and the variance of the errors is constant.

3. How is regression analysis for a Gamma function different from linear regression?

Regression analysis for a Gamma function is different from linear regression in that it is specifically designed for data that follows a gamma distribution, whereas linear regression assumes a normal distribution. Additionally, the error term in regression analysis for a Gamma function follows a gamma distribution, whereas in linear regression it follows a normal distribution.

4. What is the purpose of using regression analysis for a Gamma function?

The purpose of using regression analysis for a Gamma function is to understand and quantify the relationship between a dependent variable and one or more independent variables, when the dependent variable follows a gamma distribution. It can also be used for prediction and forecasting of future values of the dependent variable.

5. What are some common applications of regression analysis for a Gamma function?

Regression analysis for a Gamma function is commonly used in fields such as economics, finance, and medical research to analyze data that is skewed and has positive values. It is also used in environmental studies to model variables such as pollution levels and species diversity. Additionally, it is used in actuarial science to analyze insurance claims data.

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