Determine g from least squares fit line?

In summary, the conversation discussed how to determine the value of G in a least squares fit project for physics. The variables used were acceleration (m/s²) and a hard to explain factor, and the relationship between them was derived from a previous worksheet. The gradient of the graph was found to represent the value of g, and the only remaining task was to determine the value of the gradient from the graph.
  • #1
noname1
134
0
I did a least squares fit project for physics and now i have to say the value of G and the slope.

I know that slope is m from the equation

y = mx+b

but how do i determine G?
 
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  • #2
Perhaps it would help to provide the variables that you are plotting in your graphical analysis?
 
  • #3
Sorry...

The x values i am using are:
0.03139
0.05198
0.09315
0.1755

The y values i am using are:
0.00306
0.00514
0.00929
0.01729
 
  • #4
Err...no not the values of the variables...I need to know what the variables in question are. ie what x and y represent.
 
  • #5
Y is acceleration (m/s²) and x is hard to explain.

We measured these values using an air track where we had a mass hanging which is m1 and than we had a cart m2

x values is (m1/m1+m2)
 
  • #6
noname1 said:
Y is acceleration (m/s²) and x is hard to explain.

We measured these values using an air track where we had a mass hanging which is m1 and than we had a cart m2

x values is (m1/m1+m2)
Air track? Hm...I guess I have to trouble you to explain the set-up a bit.
 
  • #7
maybe this can help better
 

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  • #8
From the worksheet, they already derived the relation:
[tex]a = \frac{m_{1}}{m_{1} + m_{2}}g[/tex]​
which is in the form y = mx + c.

Hence, from the plot, we expect the gradient m to be g, and c to be zero. If c is not zero, then it probably indicates the existence of some experimental error. So, in fact, your gradient m is the value of g measured by the experiment.
 
  • #9
yes c is really close to 0, its 1.848x10^-4

y = 0.09651x + 1.848x10^-4

and sorry i didnt understand really well about the g part, could you explain in other words...

I was thinking of this but don't know if i am right or not, but i was going to do

g = (m1/(m1+m2))*a

thanks for trying to help me
 
  • #10
I'll try. By comparing y = mx + c and a = g (m1/(m1+m2)) + 0, we can see that m in fact represents g - basically the gradient of the graph that you obtain when you plot acceleration against (m1/(m1+m2)) in this case is the value of the gravitational acceleration g.

So, g would be 0.09651 when measured in the units that you are using.
 
  • #11
so there is no work i can show to demonstrate this correct?
 
  • #12
Well, the work of linearising the equation and choosing the variables to plot in fact already fixes the gradient m as g, so no, there is no further need to substantiate why m = g; it is clear from the variables plotted and the given equation from the beginning.

The only task left for you was to determine the value of the gradient and hence g from your graphical analysis. So, essentially, you are done :)
 

Related to Determine g from least squares fit line?

1. What is the least squares method?

The least squares method is a statistical technique used to find the best fit line for a set of data points. It minimizes the sum of the squared differences between the actual data points and the predicted values from the line of best fit.

2. How do you determine g from a least squares fit line?

To determine g from a least squares fit line, you need to have a set of data points that represent the relationship between two variables, one of which is the acceleration due to gravity (g). You can then use a graphing calculator or statistical software to perform a least squares regression analysis on the data, which will give you the slope of the line of best fit. This slope represents the value of g.

3. What is the significance of determining g from a least squares fit line?

Determining g from a least squares fit line allows us to accurately calculate the acceleration due to gravity, which is a fundamental constant in physics. It also helps us understand the relationship between two variables and make predictions about future data points.

4. Can the least squares method be used for any type of data?

The least squares method can be used for linear data, meaning a data set that can be represented by a straight line. It is not suitable for non-linear data as it assumes a linear relationship between the variables being studied.

5. How is the accuracy of the least squares fit line determined?

The accuracy of the least squares fit line is determined by the coefficient of determination (R2). This value ranges from 0 to 1, with 1 indicating a perfect fit and 0 indicating no relationship between the variables. The closer the R2 value is to 1, the more accurate the fit line is.

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