Fitting a model to a data

In summary, fitting a model to data is a statistical technique used to find the best possible relationship between a set of data points. The choice of model depends on the type of data and the research question being addressed, as different models have different assumptions and are suitable for different types of data. Common techniques for fitting a model include linear regression, logistic regression, and neural networks. The accuracy of a fitted model can be evaluated using metrics such as R-squared, mean squared error, and root mean squared error. Overfitting, where a model may perform well on training data but not on new data, can be avoided by carefully choosing the model and using techniques like cross-validation.
  • #1
HalcyonStorm
7
0

Homework Statement


I need to fit a model to some data, where y = systolic blood pressure and x = time in weeks. The problem is, all of the 'usua' trendline options on Excel produce awful R squared values. Is there some other method I can do to fit a different sort of model that would be accurate?


Homework Equations


None that I know.


The Attempt at a Solution


Only thing I can put here are the graphs, but that seems a little pointless.

Weeks (x)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Systolic BP (mmHg) (y)
135
115
130
110
120
125
130
130
115
125
120
130
140
115
125
120

Thanks heaps!
 
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  • #2
I'm not sure anything other than a linear fit will be good. That data, from what I saw when I plotted it, is pretty scattered. That's why your R squared value is so low. You will either need more data or more precise data in order to get a better trendline.
 
  • #3
HalcyonStorm said:

Homework Statement


I need to fit a model to some data, where y = systolic blood pressure and x = time in weeks. The problem is, all of the 'usua' trendline options on Excel produce awful R squared values. Is there some other method I can do to fit a different sort of model that would be accurate?


Homework Equations


None that I know.


The Attempt at a Solution


Only thing I can put here are the graphs, but that seems a little pointless.

Weeks (x)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

Systolic BP (mmHg) (y)
135
115
130
110
120
125
130
130
115
125
120
130
140
115
125
120

Thanks heaps!

When plotted, your data looks almost random, with considerable "noise" masking the signal. That is why your R is so large---as it should be! It would be a great mistake to try to fit an accurate formula to random data.

RGV
 

1. What is the purpose of fitting a model to data?

Fitting a model to data is a statistical technique used to find the best possible relationship between a set of data points. This relationship can then be used to make predictions or gain insights about the data.

2. How do you choose which model to fit to your data?

Choosing the appropriate model depends on the type of data and the research question being addressed. Different models have different assumptions and are suitable for different types of data. It is important to carefully consider the data and the research question before selecting a model.

3. What are some common techniques for fitting a model to data?

Some common techniques for fitting a model to data include linear regression, logistic regression, and neural networks. These techniques involve finding the best parameters to represent the relationship between the independent and dependent variables in the data.

4. How do you evaluate the accuracy of a fitted model?

The accuracy of a fitted model can be evaluated using various metrics such as R-squared, mean squared error, and root mean squared error. These metrics measure how well the model fits the data and can help determine if the model is a good fit for the data.

5. Can a model be overfitted to the data?

Yes, a model can be overfitted to the data. This means that the model may fit the training data very well, but it may not perform well on new data. To avoid overfitting, it is important to carefully choose the model and to use techniques like cross-validation to evaluate the model's performance on unseen data.

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