Pina Colada Corp. received a check for $19800 on July 1, which represents a 6-month advance payment of rent on a building it rents to a client. Unearned Rent Revenue was credited for the full $19800. Financial statements will be prepared on July 31. Pina Colada's should make the following adjusting entry on July 31: 1. debit Rent Revenue, $3300; credit Unearned Rent Revenue, $3300. 2. debit Unearned Rent Revenue, $19800; credit Rent Revenue, $19800. 3. debit Unearned Rent Revenue, $3300; credit Rent Revenue, $3300.4. debit Cash, $19800; credit Rent Revenue, $19800.

If you want to compare two different investments, what should you calculate

During january 2018, the following transactions occur: january 1 purchase equipment for $20,600. the company estimates a residual value of $2,600 and a five-year service life. january 4 pay cash on accounts payable, $10,600. january 8 purchase additional inventory on account, $93,900. january 15 receive cash on accounts receivable, $23,100 january 19 pay cash for salaries, $30,900. january 28 pay cash for january utilities, $17,600. january 30 firework sales for january total $231,000. all of these sales are on account. the cost of the units sold is $120,500. the following information is available on january 31, 2018. depreciation on the equipment for the month of january is calculated using the straight-line method. the company estimates future uncollectible accounts. at the end of january, considering the total ending balance of the accounts receivable account as shown on the general ledger tab, $4,100 is now past due (older than 90 days), while the remainder of the balance is current (less than 90 days old). the company estimates that 50% of the past due balance will be uncollectible and only 3% of the current balance will become uncollectible. record the estimated bad debt expense. accrued interest revenue on notes receivable for january. unpaid salaries at the end of january are $33,700. accrued income taxes at the end of january are $10,100

True or false: a fitted model with more predictors will necessarily have a lower training set error than a model with fewer predictors.