Math/Statistics

Least Squares Regression for No-Show Projections
The method of least squares is a standard approach in regression analysis to approximate the solution of overdetermined systems (sets of equations in which there are more equations than unknowns) by minimizing the sum of the squares of the residuals made in the results of every single equation.
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The Problem

No-show Appointments are costly 

According to this survey, the healthcare industry loses more than $150 billion a year to no-shows alone. On average, clinics, systems, and practices are experiencing a no-show rate around 18.8 percent. That’s one out of every five of your appointment slots not reimbursable. 

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Step 1 - Import Google Sheet Data using gspread

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Step 2 - For each (x,y) point calculate x2 and xy. Add Totals Row

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Step 3 - calculate slope (m) and Intercept (b)

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Step 4 - Assemble the equation for predicted value

Display on a scatter plot using Plotly

Same as above, but with scipy