An important application of regression analysis is in the estimation of cost$.$ By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year $2018.$

$\backslash$table$[[$Month$,$Production Volume $($units$),$Total Costs $($$$)],[$January $2018,500,6,000],[$February $2018,350,4,000],[$March $2018,450,5,000],[$April $2018,550,5,400],[$May $2018,600,5,900],[$June $2018,400,4,000],[$July $2018,400,4,200],[$August $2018,350,3,900],[$September $2018,400,4,300],[$October $2018,600,6,000],[$November $2018,700,6,400],[$December $2018,750,7,000]]1.$ calculqtw the pearson correlation coeffieint between production volume