Pop-up coffee vendors have been popular in the city of Adelaide in 2013. A vendor is interested in knowing how temperature (in degrees Celsius) and number of different pastries and biscuits offered to customers impacts daily hot coffee sales revenue (in $00's).
A random sample of 6 days was taken, with the daily hot coffee sales revenue and the corresponding temperature and number of different pastries and biscuits offered on that day, noted.
Excel output for a multiple linear regression is given below: Coffee sales revenue 6.5105.54.53.528 Temperature 25173035409 Pastries/biscuits 71356315 SUMMARY OUTPUT Regression Statistios Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept Temperature Pastries/biscuits 0.870.750.595.956.00df2.003.005.00 Coeffients 18.68−0.500.49SS322.14106.20428.33 Standard Error 37.880.832.02MS161.0735.40 tStat 0.49−0.600.24F4.55 P-value 0.660.590.82 Significance F0.12 Lower 95%−101.88−3.15−5.94 Upper 95%139.242.156.92 Interpret the intercept. Does this make sense?
Average Collection Period
The average number of days it takes a company to receive payment after a sale has been made.
Operating Cycle
The period between the acquisition of inventory and the collection of receivable generated from sales, reflecting how long it takes for a business to convert its inventory into cash.
Cash Collections
The process of gathering and managing the cash received from customers or clients.
Collect
The act of receiving or gathering something, often referring to the collection of payments or receivables in a business context.