or 20Y8, Raphael Frame Company prepared the sales budget that follows.

At the end of December 20Y8, the following unit sales data were reported for the year:

For the year ending December 31, 20Y9, unit sales are expected to follow the patterns established during the year ending December 31, 20Y8. The unit selling price for the 8" × 10" frame is expected to increase to $27 and the unit selling price for the 12" × 16" frame is expected to increase to $29, effective January 1, 20Y9.

Required:

1. Compute the increase or decrease of actual unit sales for the year ended December 31, 20Y8, over budget. Use the minus sign to indicate a decrease in amount and percent. Round percents to the nearest whole percent.

2. Assuming that the increase or decrease in actual sales to budget indicated in part (1) is to continue in 20Y9, compute the unit sales volume to be used for preparing the sales budget for the year ending December 31, 20Y9. Use the minus sign to indicate a decrease in percent. Round budgeted units to the nearest whole unit.

3.  Prepare a sales budget for the year ending December 31, 20Y9.

The company has the following account balances on June 1, 2020. (all accounts have their ‘normal’ balances)

Drawings: 1000

Cash: 20000

Service revenue: 50000

Capital: 15000

Depreciation Expense: 700

Equipment: 30000

Accounts Payable: 5000

Insurance Expense: 500

Unearned Service Revenue: 4000

Prepaid Service Revenue: 500

Accounts Receivable: 4000

Rent Expense: 5000

Salaries Expense: 16000

Accumulated Depreciation - Equipment: 3000

During June 2018, the following events took place. Where appropriate, record a journal entry for each transaction. If no journal entry is required, write ‘no entry’.

1. On June 2, the company prepaid rent for July to September for $6,000.

2. On June 8, someone invested $3,000 cash and a computer system valued at $2,000 into the company.

3. On June 10, the company collected $4,000 it was owed on account.

4. On June 15, The company provided a quotation for membership fees to a corporation looking to provide fitness benefits to its employees. The quotation was for $10,000. The corporation will decide next month if it is a good fit.

5. On June 22 the company provided product and collected $5,000.

6. On June 24 the company received a $1,000 bill for advertising expense that it will pay in the near future.

7. On June 27 the company paid $2,000 cash on account.

8. On June 29, the owner withdrew $600 for personal use.

1. On June 30, the company purchased $1,000 of supplies on account.

10. On June 30, the company paid employee salaries of $3,000.

Explanation is needed if the item needs to to be calculated.

The company has the following account balances on June 1, 2020. (all accounts have their ‘normal’ balances)

Drawings: 1000

Cash: 20000

Service revenue: 50000

Capital: 15000

Depreciation Expense: 700

Equipment: 30000

Accounts Payable: 5000

Insurance Expense: 500

Unearned Service Revenue: 4000

Prepaid Service Revenue: 500

Accounts Receivable: 4000

Rent Expense: 5000

Salaries Expense: 16000

Accumulated Depreciation - Equipment: 3000

During June 2018, the following events took place. Where appropriate, record a journal entry for each transaction. If no journal entry is required, write ‘no entry’.

1. On June 2, the company prepaid rent for July to September for $6,000.
2. On June 8, someone invested $3,000 cash and a computer system valued at $2,000 into the company.
3. On June 10, the company collected $4,000 it was owed on account.
4. On June 15, The company provided a quotation for membership fees to a corporation looking to provide fitness benefits to its employees. The quotation was for $10,000. The corporation will decide next month if it is a good fit.
5. On June 22 the company provided product and collected $5,000.
6. On June 24 the company received a $1,000 bill for advertising expense that it will pay in the near future.
7. On June 27 the company paid $2,000 cash on account.
8. On June 29, the owner withdrew $600 for personal use.
9. On June 30, the company purchased $1,000 of supplies on account.
10. On June 30, the company paid employee salaries of $3,000.

Question: Open T-accounts using the beginning balances provided and post entries into T-accounts. Calculate the balance of each one.

This is the trial balance of Cullumber Company on September 30.

The October transactions were as follows.

Post to the ledger accounts. (Post entries in the order of information presented in the question.)

Part1. Calculate the nuclear binding energy (in J) and the nuclear binding energy per nucleon of

(241.0568453 amu).

Part 2.

A freshly isolated sample of ⁹⁰Y was found to have an activity of 8.2 × 10⁵ disintegrations per minute at 1:00 p.m. on December 3, 2006. At 2:15 p.m. on December 17, 2006, its activity was measured again and found to be 2.2 × 10⁴ disintegrations per minute. Calculate the half-life of ⁹⁰Y.

An economist with a major bank wants to learn, quantitatively, how much spending on luxury goods and services can be explained based on consumers’ perception about the current state of the economy and what do they expect in the near future (6 months ahead).  Consumers, of all income and wealth classes, were surveyed.  Every year, 1500 consumers were interviewed.  The bank having all of the data from the 1500 consumers interviewed every year, computed the average level of consumer confidence (an index ranging from 0 to 100, 100 being absolutely optimistic) and computed the average dollar amount spent on luxuries annually.  Below is the data shown for the last 24 years.

Date                 X                     Y (in thousands of dollars)

1994                79.1                 55.6

1995                79                    54.8

1996                80.2                 55.4

1997                80.5                 55.9

1998                81.2                 56.4

1999                80.8                 57.3

2000                81.2                 57

2001                80.7                 57.5

2002                80.3                 56.9

2003                79.4                 55.8

2004                78.6                 56.1

2005                78.3                 55.7

2006                78.3                 55.7

2007                77.8                 55

2008                77.7                 54.4

2009                77.6                 54

2010                77.6                 56

2011                78.5                 56.7

2012                78.3                 56.3

2013                78.5                 57.2

2014                78.9                 57.8

2015                79.8                 58.7

2016                80.4                 59.3

2017                80.7                 59.9

Questions:

1. Measure the strength of the linear association between consumers’ moods and the dollar amounts spent on luxury items.
2. Construct the linear regression model for the dollar amount spent on luxury goods and services.
3. Explain how you would interpret the slope and the intercept of the regression model.
4. How well does our model fit the data? Explain what it means.
5. Do you think that measuring the level of optimism is a good predictor for trying to forecast future spending on luxury items?  Explain why or why not.
6. How would you be able to improve on the model?  You must provide a minimum of two specific ways to go about improving the model.
7. If the economist expects that, by year’s end, the average level of consumer confidence will hit 81.5 points, how much will be expected by consumers to spend on luxury items?

Is the number of tornadoes increasing? In the last homework, data on the number of tornadoes in the United States between 1953 and 2014 were analyzed to see if there was a linear trend over time. Some argue that it’s not the number of tornadoes increasing over time, but rather the probability of sighting them because there are more people living in the United States. Let’s investigate this by including the U.S. census count (in thousands) as an additional explanatory variable (data in EX11-24TWISTER.csv).

Perform a multiple regression using both year and census count as explanatory variables. Write down the fitted model. Are year and census count respectively significant in the MLR model?

Is the number of tornadoes increasing? In the last homework, data on the number of tornadoes in the United States between 1953 and 2014 were analyzed to see if there was a linear trend over time. Some argue that it’s not the number of tornadoes increasing over time, but rather the probability of sighting them because there are more people living in the United States. Let’s investigate this by including the U.S. census count (in thousands) as an additional explanatory variable (data in EX11-24TWISTER.csv).

Fit one SLR model with year as the predictor, another SLR model with census count as the predictor. Write down the two models. Are year and census count significant, respectively?