# Polynominal Regression Fit

figure_1afigure_1bfigure_1c

The objective for the part of the assignment was to use already existing data and plot a fit to that data.
Initially, I began to use different values to find a particular fit that would ensure some sort of flexibility for the measurement which would be conducted later on. The final outcome of this section produce and a polynomial fit which calculated the average and maximum error values of each point on the dataset. A total of 50 dataset was produced. (Figure_1c represents the best fit)

#### Polynominal Regression Fit using, N= 3:

Error calculated was very high.

Sum of errors in the dataset: [ 2535033.60000039,]

Number of values in the dataset: 50

Average error in the dataset: [ 50700.67200001,]

Calcuation of maximum error for entire dataset [ 145286.39999986,]

#### Polynominal Regression Fit using, N=5:

Error calculated was lower than other N values, with room for flexiability.

Sum of errors in the dataset: [ 0.00050403,]

Number of values in dataset: 50

Average error in the dataset: [ 1.00805828e-005,]

Calcuation of maximum error for entire dataset [ 5.41539630e-005,]

#### Code Sample(Python Generated)

Download code here:

Polyfit_samplefile_1

Polyfit_samplefile_2

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