We can also change the color by passing the color name as a string in the scatter() function. In the above scatter plot, the color of the circles is black, but we can give it any color by entering that color’s RGB value as a fourth argument in the scatter() function. For example, let’s change the color of the circles in the above scatter plot to black. We can also alter the color of the circles in the scatter plot by passing it as a fourth argument in the scatter() function. In the above output, each circle has a different size. For example, let’s change the size of each circle in the above scatter plot. We can also give each circle a different size value using a vector of the same length as the input vectors x and y. If the size is a single positive numeric value, just like in the above code, it will apply to all the circles present in the scatter plot. The size should be a positive numeric value or a vector of the same size as the input vectors x and y. The size of the circles in the above output is different as compared with the size of circles in the previous scatter plot. For example, let’s change the size of the circles in the above plot. For example, to change the size of the circles, we have to define the size of the circles as a third argument inside the scatter() function. By default, the scatter() function uses the default value for the size and color of the circles, but we can change the default properties of the function. Make sure the length of the variable x and y should be the same. The data stored in the variables x and y is used to create a scatter plot in the output. For example, let’s use the scatter() function to create a scatter plot of given data. By default, the scatter() function uses circular markers to plot the given data. The scatter(x,y) function creates a scatter plot on the location specified by the input vectors x and y. Create a Scatter Plot Using the scatter() Function in MATLAB This tutorial will discuss creating a scatter plot using the scatter() function in MATLAB.

Suppose we call this dimension d, you'll receive back d+1 coefficients in p, which represent a polynomial conforming to an estimate of f(x): f(x) = p(1) * x^d + p(2) * x^(d-1) +. Note that if you want to fit an arbitrary polynomial to your data you can do so by changing the last parameter of polyfit to be the dimensionality of the curvefit. % now plot both the points in y and the curve fit in r * x + p(2) % compute a new vector r that has matching datapoints in x Suppose you have some data in y and you have corresponding domain values in x, (ie you have data approximating y = f(x) for arbitrary f) then you can fit a linear curve as follows: p = polyfit(x,y,1) % p returns 2 coefficients fitting r = a_1 * x + a_2 You need to use polyfit to fit a line to your data. Lsline is only available in the Statistics Toolbox, do you have the statistics toolbox? A more general solution might be to use polyfit.

Matlab scatter