When Patey Pontoons issued 4% bonds on January 1, 2018, with a face amount of $500,000, the market yield for bonds of similar risk and maturity was 5%. The bonds mature December 31, 2021 (4 years). Interest is paid semiannually on June 30 and December 31. (FV of $1, PV of $1, FVA of $1, PVA of $1, FVAD of $1 and PVAD of $1) (Use appropriate factor(s) from the tables provided.) Required: 1. Determine the price of the bonds at January 1, 2018. 2. Prepare the journal entry to record their issuance by Patey on January 1, 2018. 3. Prepare an amortization schedule that determines interest at the effective rate each period. 4. Prepare the journal entry to record interest on June 30, 2018. 5. What is the amount related to the bonds that Patey will report in its balance sheet at December 31, 2018? 6. What is the amount related to the bonds that Patey will report in its income statement for the year ended December 31, 2018? (Ignore income taxes.) 7. Prepare the appropriate journal entries at maturity on December 31, 2021.

When Patey Pontoons issued 10% bonds on January 1, 2018, with a face amount of $560,000, the market yield for bonds of similar risk and maturity was 11%. The bonds mature December 31, 2021 (4 years). Interest is paid semiannually on June 30 and December 31. (FV of $1, PV of $1, FVA of $1, PVA of $1, FVAD of $1 and PVAD of $1) (Use appropriate factor(s) from the tables provided.) Required: 1. Determine the price of the bonds at January 1, 2018. 2. Prepare the journal entry to record their issuance by Patey on January 1, 2018. 3. Prepare an amortization schedule that determines interest at the effective rate each period. 4. Prepare the journal entry to record interest on June 30, 2018. 5. What is the amount related to the bonds that Patey will report in its balance sheet at December 31, 2018? 6. What is the amount related to the bonds that Patey will report in its income statement for the year ended December 31, 2018? (Ignore income taxes.) 7. Prepare the appropriate journal entries at maturity on December 31, 2021.

The December 31, 2018, inventory of Tog Company, based on a physical count, was determined to be $461,000. Included in that count was a shipment of goods received from a supplier at the end of the month that cost $61,000. The purchase was recorded and paid for in 2019. Another supplier shipment costing $25,500 was correctly recorded as a purchase in 2018. However, the merchandise, shipped FOB shipping point, was not received until 2019 and was incorrectly omitted from the physical count. A third purchase, shipped from a supplier FOB shipping point on December 28, 2018, did not arrive until January 3, 2019. The merchandise, which cost $91,000, was not included in the physical count and the purchase has not yet been recorded.

The company uses a periodic inventory system.

Required:
1. Determine the correct December 31, 2018, inventory balance and, assuming that the errors were discovered after the 2018 financial statements were issued, analyze the effect of the errors on 2018 cost of goods sold, net income, and retained earnings. (Ignore income taxes.)
2. Prepare a journal entry to correct the errors.

Required 1: Effect Amount

Correct End Inv

COGS

Net Income

Retained Earnings

NutraLabs, Inc., leased a protein analyzer to Werner Chemical, Inc., on September 30, 2018. NutraLabs manufactured the machine at a cost of $5 million. The five-year lease agreement calls for Werner to make quarterly lease payments of $391,548, payable each September 30, December 31, March 31, June 30, with the first payment at September 30, 2018. NutraLabs’ implicit interest rate is 12%. (FV of $1, PV of $1, FVA of $1, PVA of $1, FVAD of $1 and PVAD of $1) (Use appropriate factor(s) from the tables provided.) Required: 1. Determine the price at which NutraLabs is “selling” the equipment (present value of the lease payments) at September 30, 2018 2. What pretax amounts related to the lease would NutraLabs report in its balance sheet at December 31, 2018? 3. What pretax amounts related to the lease would NutraLabs report in its income statement for the year ended December 31, 2018? 4. What pretax amounts related to the lease would NutraLabs report in its statement of cash flows for the year ended December 31, 2018?

On December 31, 2017, Berclair Inc. had 400 million shares of common stock and 14 million shares of 9%, $100 par value cumulative preferred stock issued and outstanding. On March 1, 2018, Berclair purchased 120 million shares of its common stock as treasury stock. Berclair issued a 6% common stock dividend on July 1, 2018. Four million treasury shares were sold on October 1. Net income for the year ended December 31, 2018, was $700 million. Also outstanding at December 31 were 63 million incentive stock options granted to key executives on September 13, 2013. The options were exercisable as of September 13, 2017, for 63 million common shares at an exercise price of $60 per share. During 2018, the market price of the common shares averaged $70 per share. The options were exercised on September 1, 2018. Required: Compute Berclair’s basic and diluted earnings per share for the year ended December 31, 2018. (Enter your answers in millions (i.e., 10,000,000 should be entered as 10).)

Baci is a well-known lollipops maker in Western Australia and produces lollipops in two size, i.e., regular and large. The company sells their products to convenience stores, fairs, schools for fundraisers and in bulk on the internet. 2018 summer is approaching and Baci is preparing its budget for the December. All Baci’s lollipops are hand-made, mostly out of sugar, and attached to wooden sticks. Expected sales are based on past experience.

Other information for December 2018 is as follows:

Input prices :

Direct materials:

Sugar $0.50 per kg

Sticks $0.30 each

Direct manufacturing labour $8 per direct manufacturing labour hour (DMLH)

Input quantities per unit of output

Regular Large

Direct materials:

Sugar 0.25 kg 0.5 kg

Sticks 1 1

Direct manufacturing labour hour (DMLH) 0.2 hour 0.25 hour

Set-up hours per batch 0.08 hour 0.09 hour

Inventory data for direct materials1

Sugar Sticks

Beginning inventory 125 kg 350

Target ending inventory 240 kg 480

Cost of beginning inventory $64 $105

1: Baci accounts for direct materials using a FIFO cost flow assumption.

Sales and inventory data for finished goods2

Regular Large

Expected sales in units 3,000 1,800

Selling price $3 $4

Target ending inventory in units 300 180

Beginning inventory in units 200 150

Beginning inventory in dollars $500 $474

2: Baci uses a FIFO cost flow assumption for finished goods inventory.

All the lollipops are made in batches of 10. Baci incurs manufacturing overhead cost, and marketing and general administration costs, but customers pay for shipping. Other 3 than manufacturing labour costs, monthly processing costs are very small. Baci uses activity-based costing (ABC) and has classified all overhead costs for December 2018 as follows:

Cost type Denominator activity Rate

Manufacturing:

Set-up Set-up hours $20 per set-up hour

Processing Direct manufacturing labour hour (DMLH) $1.70 per DMLH

Non-manufacturing:

Marketing & general admin Sales revenue 10%

Required 1. Prepare each of the following for December 2018:

(a) Revenue budget

(b) Production budget in units

(c) Direct materials usage budget and direct materials purchases budget

(d) Direct manufacturing labour cost budget

(e) Manufacturing overhead cost budgets for processing and set-up activities

(f) Budgeted unit cost of ending finished goods inventory and ending inventories budget

(g) Cost of goods sold budget

(h) Marketing and general administration costs budget

The following data was collected from 1 bag of Hershey Kisses®. Each Kiss® was weighed (in grams) and recorded in the table below. Hershey claims that there is 368 grams of chocolate in one bag.

To help you answer the questions below use your scientific calculator. Your scientific calculator is capable of doing calculations on entire data sets by first entering the data and then pressing combinations of keys to find the average and standard deviation etc... You should check with your calculator manual to see how this special data handling feature works. Let the instructor know if you have any questions. You will need to learn how to do this for testing purposes. Note: Instructions for several brands of calculators in included in the folder Course Overview/Excel & Calculator Instructions.

1. What is the Mean and Median? (you may want to use your calculator!)

2. In general, each Kiss® is approximately how many grams? Explain what measure you used and why.

3. What is the Range? Are you surprised at this? Why or why not?

4. What could be some reasons for variation in the weights of the Kisses®? NOTE: Take time answering this one. There are lots of thingsto consider here and I'll be looking for a well thought out answer with several given reasons contributing to the variation. Of course, the wrappers and tags could vary but what about the drops of chocolate themselves? Why aren't they all the same?

5. Would you say that there are any two Kisses that could have exactly the same weight? (I mean exactly the same weight!)

6. How many Kisses® were there in the bag?

7. Based on Hersheys® claim for 368 total net grams of chocolate in the bag, approximately how many Kisses® too many or too few are there? Give some possible explanations for this difference.

8. EXCEL: Click on and print out one of the following: Excel Descriptive Statistics 2016/2013 to see how to enter the Kiss data into a worksheet and obtain a list of descriptive statistics and a histogram with no more than 12 classes. Also, make sure to sort your data using the Sort command under Data on the menu bar. Submit your Excel file to the Lab1 Part 1 Dropbox.

9. Standard Deviation & Empirical Rule: Variation is a big factor in the analysis of most any data set and it will be very important to have a way of measuring it. Standard Deviation is one such measure that you will study and learn to calculate in an upcoming section. For now, find the Standard Deviation number on your Descriptive Statistics read-out from Excel. There is a rule for "mound-shaped" distributions that can help you have some feeling for what this standard deviation number is telling you. It's called the Empirical Rule and is stated below:
For any data set having a bell-shaped (or mound-shaped) distribution the following are true:
- Approximately 68% of the data values will be within one standard deviation of the mean.
- Approximately 95% of the data values will be within two standard deviation of the mean.
- Almost all of the data values will be within three standard deviation of the mean.
Use the standard deviation value given in Excel and the Empirical Rule (stated above) to find answers to the following:
a) Find the percentage of all the Kisses in the bag that fell within 1 standard deviation of the mean? ... within 2?, … within 3?
(Show how you calculated these percentages!)
b) How close is the Empirical Rule in predicting the percentages that you calculated above?
c) If your calculated percentages did not line up with the percentages claimed by the Empirical Rule, speculate on some possible reasons for this.

10. How might standard deviation and the shape of the distribution indicate how consistent Hershey® is in the manufacturing of their Kisses®?

PART 2 - Data Collection & Discussion

Task 1: Answer the following Questions

In Lab 1 Part 1 you have constructed a Histogram for the Hershey Kisses by using Excel. Think about the following questions then answer them thoroughly.

Would you consider the distribution of the weights to be roughly mound-shaped? Why or why not?

Is the shape of the distribution what you might have expected? Why or why not?
(In other words, give a non-technical explanation as to why you might have thought that the weights from a bag of Hershey Kisses would produce a mound-shaped histogram.)
If you answered 'no' to this question, explain why you should have expected it to be mound-shaped.

Do you think that Hershey^® collects and analyzes the same kind of data we have collected thus far? Why? Of what value could this be to them?

Task 2: Collect your own data!

The mound shaped distribution is a very common distribution. Find something other than Kisses^® to collect data on that would produce a mound-shaped distribution. Also, don't use weights nor candy, make it something totally different than the Hershey Kisses. Follow the steps below.

Step 1) Think of other things you could collect data on that might produce a mound-shaped distribution (histogram). There are lots of possibilities here and there are lots of other measurements besides weight such as quantity, length, time, dollars, etc ... .
You shouldn't have to do anything that costs you money!

Step 2) Collect data on this.

Step 3) Do an analysis similar to what was done with the Hershey Kisses^®. In other words, Put your data into Excel, get a Descriptive Statistics output and create a histogram. Instructions for Excel are located in the Excel & Calculator Instructions folder under Course Overview.

Step 4) Put together a small report that explains what the data was taken from and how you collected it.

Make sure to upload the answers to all the questions in Task 1, Excel file (with data, descriptive statistics, histogram) and report from Task 2 to the Lab 1 Part 2 Dropbox located in this folder. Actually you can just put everything into one Excel file and upload it!

CASE_2

COMPARING UK AND MALAYSIAN ONLINE SHOPPING BEHAVIOUR

How did I end up here? Daniel mused as he sat staring at his computer screen. Six months earlier, as he left family and friends in Malaysia to complete his master’s degree on an exchange in UK, the future seem bright. Hundreds of kilometers away from home, with a deadline looming that could make or break his future career, he did not know where to start. He had left university with a bachelor’s degree in Marketing from Northern University four years before and since that time had been on a graduate recruitment scheme with one of the largest supermarket chains in Malaysia. His performance in the early stages of a part time master’s programme in Retail Management sponsored by his employer had been good.

This, combined with his high standard of English, meant he had been offered the opportunity to travel to the UK to study for one year on a full-time basis and obtain a double qualification from both his Malaysian and a British institution. The taught classes in the first semester had complemented his previous studies and he had soon identified the area he wanted to investigate for his research project – online supermarket shopping. He decided the aim of his project would be to compare and contrast UK and Malaysian consumer’s behavior of supermarket’s online shopping offerings.

From his observation, almost every student he knew had bought something on eBay, Lelong.com, or Amazon, and many chose to do their supermarket shopping online to avoid wasting time standing in checkout queues. In addition, many supermarkets had diversified their offer away from daily consumables into white goods such as washing machines and even financial services (Colgate & Alexander, 2001); meaning consumers were now looking to these organisations for more than just the weekly household shopping. In other words, shopping convenience and variety of product offerings can give strong attitude towards online shopping.

In class, Daniel’s supervisor had identified the effect of perceived usefulness and perceived ease of use on the attitude towards online shopping (Juniwati, 2014; Suwunniponth, 2014; Ramayah & Ignatius, 2005; Yu et al., 2005). He also emphasized the issue of consumer concerns around security and trust (Beldad et al., 2010; Koufari & Hampton-Sosa, 2004) that can influence the attitude of online shopper.

It has been reported that the intention to purchase online strongly link to the positive attitude on online shopping. When consumers have the intention to purchase online the tendency of making the purchase online is high (Li & Zhang, 2002; Wu, 2003; Yang, Lester, & James, 2007). In this study, Daniel also wants to find out whether consumer characteristics have significant impact on the relationship between attitude and intention to purchase online.

It had been a revelation to see how well developed the retail websites were in the UK, compared with those in Malaysia, and how different the webscape was in the two countries. Statistics he had found showed that in June 2010 there were 51.4 million Internet users in the UK, 82.5% of the population, an increase of 234% between 2000 and 2010. In Malaysia, the figures were 17.7 million users, 60.7% of the population, an increase of 356.8% between 2000 and 2012 (Internet Usage Stats and Marketing Report, 2015).

Daniel decided he needed to identify his population, and from that draw a representative sample. He thought his organisation’s database of existing online customers would be a useful place to start, but he was unsure whether he would be allowed access. In addition, he did not simply want to undertake a large-scale quantitative survey of existing customers as did not think it would produce a picture of the wider situation. One of his objectives was, after all, to identify the Malaysian consumers’ expectations of these online offerings. How could he ensure he limited bias in the respondents’ answer, which would be a threat to the reliability of his findings?

His initial idea to use Facebook to gain access to a bigger population had not been received enthusiastically by his project tutor but he had anticipated the need to justify this suggestion. He argued that using this informal network, and building simple instructions and collecting demographic data in an Internet questionnaire, he would be able to categorize respondents and identify which supermarket website they were evaluating.

So here he was, with an interesting project that fitted perfectly with the need of his employer, which wanted to develop its presence on the Web. His intention was to get an overview of online consumer perceptions and expectations of the online supermarket sites available in the UK and France. He then planned to compare the data collected from the participants to identify the differences between the online activities of the British and Malaysian supermarkets and produce some guidelines to help his employer develop this side of its activities. However, the problem he came back to gain was how to select his sample.

Daniel thought a non-probability approach would fit this exploratory research but, if he was honest, the research methods lectures had totally confused him. A great deal of time had been spent on the explanation of the formula to work out the optimum sample size for a survey, and population. His concern was how to build the argument for using non-probability sampling and be able to justify it to his project tutor, whose own research activity involved large-scale marketing research projects run in conjunction with companies in Malaysia.

So here he was preparing his argument to produce a questionnaire using Survey Monkey and post the link on Facebook. In addition, he would send the link via emails to other friends who did not use Facebook. He would make the request that they all pass the link on their friends and family who fell within the parameters that would be defined in the message accompanying the link.

One point he felt that was in his favour was he had been able to develop a network of contacts during his time in the UK. As one of the few Malaysian students on campus he had taken advantage of his generous nature and tried to meet as many people as possible. His Facebook wall had thousands of postings from his ‘friends’. He was convinced this was the place to start but really needed to get his head around non-probability sampling if he was going to do it well.

Based on the given scenario, answer the following questions:

1. Identify the broad problem area.
2. Develop theoretical framework.
3. Suggest TWO (2) theories that related to the framework.
4. Develop THREE (3) research objectives and research questions.
5. Develop THREE (3) directional alternative hypotheses.
6. Suggest appropriate research design in terms of (please justify each answer):
   1. Purpose of study
   2. Researcher interference
   3. Study setting
   4. Research strategies
   5. Time horizon
   6. Unit of analysis
7. State the population for the study.
8. Identify the proper sampling design (probability or non-probability). Justify you answer.

Pro forma for 2018:

2017: sales = 2.5 million

2018: sales = (expected) 3 mill

               Expected net profit margin: 4%

No dividends are paid.

Assets= 1.1 mil (50k cash, 250k AR, 550k inventory, 250k net fixed assets)

Liabilities/equity= 1.1 mil ( 300k AP, 75k NP, 150k debt, 575k equity)

NFA must increase 100k, NP to 25k, and 50k in debt. Addi. Financing to come from new debt (debt:asset ratio must stay at or below 1:2)

1. Make a balance sheet for 2018

2. How much addit. Financing do we need?

Pro forma for 2018:

2017: sales = 2.5 million

2018: sales = (expected) 3 mill

               Expected net profit margin: 4%

dividends are 50k

Assets= 1.1 mil (50k cash, 250k AR, 550k inventory, 250k net fixed assets)

Liabilities/equity= 1.1 mil ( 300k AP, 75k NP, 150k debt, 575k equity)

NFA must increase 100k, NP to 25k, and 50k in debt. Addi. Financing to come from new debt (debt:asset ratio must stay at or below 1:2)

1. Make a balance sheet for 2018

2. How much addit. Financing do we need?

it is related