$\backslash$table$[[$Year$,\backslash$table$[[$Total Outstanding$],[$Receivables$]],\backslash$table$[[$Percent of sales on$],[$account written off$]],$Percent of outstanding receivables written off by receivale age group$],[,,,0-30,31-60,61-90,$over $90],[2013,\backslash$table$[[$$$],[33,584,855]],1\mathrm{.}65\%,0\mathrm{.}00\%,1\mathrm{.}92\%,3\mathrm{.}26\%,6\mathrm{.}73\%],[2014,\backslash$table$[[$$$],[37,040,513]],3\mathrm{.}37\%,0\mathrm{.}02\%,0\mathrm{.}00\%,5\mathrm{.}31\%,14\mathrm{.}86\%],[2015,\backslash$table$[[$$$],[34,048,875]],3\mathrm{.}11\%,0\mathrm{.}02\%,3\mathrm{.}17\%,4\mathrm{.}67\%,13\mathrm{.}15\%],[2016,\backslash$table$[[$$$],[40,884,402]],1\mathrm{.}57\%,0\mathrm{.}00\%,0\mathrm{.}00\%,4\mathrm{.}38\%,5\mathrm{.}95\%],[2017,\backslash$table$[[$$$],[40,902,344]],2\mathrm{.}03\%,0\mathrm{.}01\%,2\mathrm{.}58\%,4\mathrm{.}30\%,6\mathrm{.}11\%],[2018($predicted$),\backslash$table$[[$$$],[40,857,493]],2\mathrm{.}34\%,0\mathrm{.}01\%,1\mathrm{.}53\%,4\mathrm{.}39\%,9\mathrm{.}36\%]]$

Based on your recommendation, calculate the amount of bad debt expense you should report for $2018.$ Assume beginning balance of Allowance for Doubtful Accounts is zero in $2018.$

Your recommendations and the reasoning behind them $-$ should you use percent of credit sales or aging of receivables? $($Some questions you may want to consider are: is the aging method the right choice for the company given the historical data? Should all the years be included in the analysis? Should you use mean or median to calculate the percentages? Are there any extreme observations that should not be included in the analysis? Do the number of customers matter in a given year? Does the balance of total receivables vary significantly and what you conclude given these differences?$)$

Based on your recommendation, calculate the amount of bad debt expense you should report for $2018.$ Assume beginning balance of Allowance for Uncollectible Accounts is zero in $2018($Provide the analysis of the credit sales or outstanding receivables in each aging category for $2018$ that led you to this number in the table above$).$

Anything else you would like to share with your CFO $($can you think of different data you might be able to use to get a better sense of the write$-$offs$)?$