Concept explainers
Cognitive Bias. A team of accounting students is working on a case where they are required to assess a set of information to determine a company’s allowance for
The company’s allowance for bad debts has been 5% of its receivables for the last several years
This year, the company has strengthened its credit extension policy.
The average time that an
The economy has weakened over the year, with a pending recession.
Following is part of the discussion at their first team meeting. Analyze the discussion and determine the type of cognitive bias most consistent with the statements made by each student, providing an explanation for your answer.
Discussion
Tom initiated the discussion saying, “I have seen this kind of situation before when a company has to report a higher allowance than last year. Allowances are always increasing.”
Jennifer offered. “The first piece of information in the case is always the most important.
The bad debts have historically been 5%. Therefore, the allowance has to be 5%.”
Jake added, “As I look at the case, I keep coming back to the fact that the average time that an account receivable has been outstanding has increased by 10 days. In my view, this is the most important piece of information—the other facts don’t matter.”
Manna’s view was, “Even though the economy has deteriorated, the historical data is always more important. The general trends in the economy are not relevant.”
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Intermediate Accounting
- Consider again the example introduced in Section 7.5 of a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3). Figure 7.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? d. Recall the multiple regression in Figure 7.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts (a), (b), and (c)? What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? e. Add the coefficients of determination for the simple linear regression in parts (a), (b), and (c), and compare the result to the coefficient of determination for the multiple regression model in Figure 7.23. What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? f. Add age, a dummy variable for sex, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 7.23. Code the dummy variable for sex as 1 if the customer is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?arrow_forwardHistorically, your company has calculated bad debts using an aging of accounts receivable. Near the end of the fiscal year, the company is in a cash crunch and needs to borrow money from the bank, using accounts receivable as collateral. The owner of the company knows that many of the accounts receivable are more than 90 days past due, resulting in net receivables equal to only 80% of total receivables. You are asked by the owner asks you to change the method of estimating bad debts to a flat 3% of receivables. What should you do?arrow_forwardWhat's the answer?arrow_forward
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