Approximating and regression method

In summary, the speaker conducted tests to determine the natural frequency of a crane lifting loads and tried to find two constants, a and b, from its initial function. Using three different loads on three different positions, they plotted a graph and attempted to calculate the constants manually and through nonlinear regression using MATHCAD. However, they are unsure if this is the correct approach and are asking for suggestions on mathematical approximation methods. Suggestions include using Excel for linear regression or calculating s1, s2, t1, t2, and v1 and using them to find a and b for the regression line y=ax+b.
  • #1
Iqbal94
4
0
Hi guys,

I did a few sets of test in determining the natural frequency of a crane lifting loads. From that, I tried to find two constant from its initial function.

upload_2016-4-5_20-30-10.png


a is the stiffness of the pole that was holding the crane
b is the weight of the crane
x is the weight of the load

The tests were completed by using three different loads on three different position. I plotted the graph as below.

upload_2016-4-5_20-36-7.png


From the obtained graph and the initial function, I tried to determine the value of constant A and B. By calculating manually, the value of A and B are not constant but it supposed to be constant. I tried using MATHCAD to find the value of the constants by using nonlinear regression method but I am not convince that is a right solution. Any of you know any mathematical approximation method that I can use?
 
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  • #2
Iqbal94 said:
Any of you know any mathematical approximation method that I can use?
Excel does linear regression directly. If you want it mathematically, you must calculate:
  1. [itex]s_{1}=\sum x_{j} [/itex]
  2. [itex]s_{2}=\sum x_{j}^{2} [/itex]
  3. [itex]t_{1}=\sum y_{j} [/itex]
  4. [itex]t_{2}=\sum y_{j}^{2}[/itex]
  5. [itex]v_{1}=\sum x_{j}\cdot y_{j} [/itex]
Assuming that you have n points, you then calculate [itex]a=\frac{t_{1}\cdot s_{2}-s_{1}\cdot v_{1}}{n\cdot s_{2}-s_{1}^{2}} [/itex] and [itex] b=\frac{n\cdot v_{1}-s_{1}\cdot t_{1}}{n\cdot s_{2}-s_{1}^{2}}[/itex]. The regression line is then given by [itex] y=a\cdot x + b[/itex].
 

1. What is the difference between approximating and regression method?

The main difference between approximating and regression method is their purpose. Approximating method is used for finding an approximate solution to a problem, while regression method is used for predicting the relationship between variables.

2. How is approximating and regression method used in scientific research?

Approximating and regression method are commonly used in scientific research to analyze and predict complex relationships between variables. They are particularly useful in fields such as statistics, economics, and engineering.

3. What is the process of approximating and regression method?

The process of approximating and regression method involves collecting data, creating a mathematical model, and using statistical techniques to estimate the relationship between variables. This includes determining the best fit line or curve and evaluating the accuracy of the model.

4. What are the advantages of using approximating and regression method?

The advantages of using approximating and regression method include its ability to handle large datasets, its flexibility in analyzing various types of relationships, and its ability to provide quantitative predictions. It also allows for the identification of outliers and the evaluation of the accuracy of the model.

5. What are the limitations of approximating and regression method?

Some limitations of approximating and regression method include its sensitivity to outliers and the assumption of a linear relationship between variables. It also requires a large amount of data to accurately estimate the relationship between variables. Additionally, the results may not always be generalizable to other populations.

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