Examine and discuss moral and fiscal dilemmas that healthcare managers’ face in driving revenue, and capital purchases. Give suggestions for how these might be addressed and ameliorate

Forecasting labour costs is a key aspect of hotel revenue management that enables hoteliers to appropriately allocate hotel resources and fix pricing strategies. Mary, the President of Hellenic Hoteliers Federation (HHF) is interested in investigating how labour costs (variable L_COST) relate to the number of rooms in a hotel (variable Total_Rooms). Suppose that HHF has hired you as a business analyst to develop a linear model to predict hotel labour costs based on the total number of rooms per hotel using the data provided. 3.1 Use the least squares method to estimate the regression coefficients b0 and b1 3.2 State the regression equation 3.3 Plot on the same graph, the scatter diagram and the regression line3.4 Give the interpretation of the regression coefficients b0 and b1 as well as the result of the t-test on the individual variables (assume a significance level of 5%) Determine the correlation coefficient of the two variables and provide an interpretation of its meaning in the context of this problem 3.6 Check statistically, at the 0.05 level of significance whether there is any evidence of a linear relationship between labour cost and total number of rooms per hotel

Q8

To prevent the loss of valuable data in the revenue cycle, internal file labels can be used to

Explain the revenue and expense recognition principles with an example. Within this respect, explain aslo why unearned revenues cannot be revenues but liabilities.

1. Which is the largest source of revenue for the Federal government? California State government?

2. What item takes the largest share of the federal government spending? State government spending?

3. What is the most recent outstanding national debt of United States? Assuming a 340 million population in United States, what is the per capita debt of United States?

4. Why are taxes on common items such as gasoline and toiletries considered highly regressive? Explain clearly.

The organizations’ Controller has received a notice from the Canada Revenue Agency (CRA) advising that the organization will be changing from an Accelerated threshold 1 remitter to an Accelerated threshold 2 remitter effective with the first pay of the new year. The Controller has asked you to provide an explanation of why this change has occurred.

In addition, she wants to understand how this will impact the statutory remittance schedule. Using the Current Year calendar in the course material, provide specifics of when the remittances are due for the January and February bi-weekly payrolls starting with the first pay date of the new year which is Friday January 5^th.

Forecasting labour costs is a key aspect of hotel revenue management that enables hoteliers to appropriately allocate hotel resources and fix pricing strategies. Mary, the President of Hellenic Hoteliers Federation (HHF) is interested in investigating how labour costs (variable L_COST) relate to the number of rooms in a hotel (variable Total_Rooms). Suppose that HHF has hired you as a business analyst to develop a linear model to predict hotel labour costs based on the total number of rooms per hotel using the data provided.

3.1 Use the least squares method to estimate the regression coefficients b0 and b1

3.2 State the regression equation

3.3 Plot on the same graph, the scatter diagram and the regression line

3.4 Give the interpretation of the regression coefficients b0 and b1 as well as the result of the t-test on the individual variables (assume a significance level of 5%)

3.5 Determine the correlation coefficient of the two variables and provide an interpretation of its meaning in the context of this problem 3.6 Check statistically, at the 0.05 level of significance whether there is any evidence of a linear relationship between labour cost and total number of rooms per hotel

Total_Rooms   L_COST
412   2.165.000
313   2.214.985
265   1.393.550
204   2.460.634
172   1.151.600
133   801.469
127   1.072.000
322   1.608.013
241   793.009
172   1.383.854
121   494.566
70   437.684
65   83.000
93   626.000
75   37.735
69   256.658
66   230.000
54   200.000
68   199.000
57   11.720
38   59.200
27   130.000
47   255.020
32   3.500
27   20.906
48   284.569
39   107.447
35   64.702
23   6.500
25   156.316
10   15.950
18   722.069
17   6.121
29   30.000
21   5.700
23   50.237
15   19.670
8   7.888
20  
11  
15   3.500
18   112.181
23  
10   30.000
26   3.575
306   2.074.000
240   1.312.601
330   434.237
139   495.000
353   1.511.457
324   1.800.000
276   2.050.000
221   623.117
200   796.026
117   360.000
170   538.848
122   568.536
57   300.000
62   249.205
98   150.000
75   220.000
62   50.302
50   517.729
27   51.000
44   75.704
33   271.724
25   118.049
42  
30   40.000
44  
10   10.000
18   10.000
18  
73   70.000
21   12.000
22   20.000
25   36.277
25   36.277
31   10.450
16   14.300
15   4.296
12  
11  
16   379.498
22   1.520
12   45.000
34   96.619
37   270.000
25   60.000
10   12.500
270   1.934.820
261   3.000.000
219   1.675.995
280   903.000
378   2.429.367
181   1.143.850
166   900.000
119   600.000
174   2.500.000
124   1.103.939
112   363.825
227   1.538.000
161   1.370.968
216   1.339.903
102   173.481
96   210.000
97   441.737
56   96.000
72   177.833
62   252.390
78   377.182
74   111.000
33   238.000
30   45.000
39   50.000
32   40.000
25   61.766
41   166.903
24   116.056
49   41.000
43   195.821
9  
20   96.713
32   6.500
14   5.500
14   4.000
13   15.000
13   9.500
53   48.200
11   3.000
16   27.084
21   30.000
21   20.000
46   43.549
21   10.000

Forecasting labour costs is a key aspect of hotel revenue management that enables hoteliers to appropriately allocate hotel resources and fix pricing strategies. Mary, the President of Hellenic Hoteliers Federation (HHF) is interested in investigating how labour costs (variable L_COST) relate to the number of rooms in a hotel (variable Total_Rooms). Suppose that HHF has hired you as a business analyst to develop a linear model to predict hotel labour costs based on the total number of rooms per hotel using the data provided. 3.1 Use the least squares method to estimate the regression coefficients b0 and b1 3.2 State the regression equation 3.3 Plot on the same graph, the scatter diagram and the regression line 3.4 Give the interpretation of the regression coefficients b0 and b1 as well as the result of the t-test on the individual variables (assume a significance level of 5%) 3.5 Determine the correlation coefficient of the two variables and provide an interpretation of its meaning in the context of this problem 3.6 Check statistically, at the 0.05 level of significance whether there is any evidence of a linear relationship between labour cost and total number of rooms per hotel

TR=TOTAL ROOMS, L COST =LABOUR COST

TR L_COST       Turnover_per_Room
412       2,165,000       21,519.42
313       2,214,985       21,755.04
265       1,393,550       17,937.91
204       2,460,634       37,400.05
172       1,151,600       31,824.30
133       801,469 19,444.46
127       1,072,000       22,551.18
322       1,608,013       18,205.04
241       793,009 8,793.00
172       1,383,854       25,114.16
121       494,566       14,095.35
70       437,684       22,231.59
65       83,000 5,953.85
93       626,000       18,150.99
75       37,735       3,871.67
69       256,658       11,071.70
66       230,000       8,030.30
54       200,000       10,185.19
68       199,000      
57       11,720       2,982.46
38       59,200       6,342.11
27       130,000       25,185.19
47       255,020       18,223.26
32       3,500 1,000.00
27       20,906 2,384.85
48       284,569       14,264.58
39       107,447       10,478.26
35       64,702       10,811.29
23       6,500 3,478.26
25       156,316       22,231.56
10       15,950       8,150.00
18       722,069       81,556.71
17       6,121 2,151.88
29       30,000       4,068.97
21       5,700 4,142.86
23       50,237       5,113.83
15       19,670       10,037.87
8       7,888 4,849.25
20 3,750.00
11 1,753.91
15       3,500 2,666.67
18       112,181       34,260.90
23              
10       30,000 12,000.00
26       3,575 3,001.81
306       2,074,000       19,803.92
240       1,312,601       15,823.58
330       434,237       4,361.65
139       495,000       17,050.36
353       1,511,457       15,370.22
324       1,800,000       15,432.10
276       2,050,000       22,101.45
221       623,117       9,199.82
200       796,026       18,158.06
117       360,000       11,649.57
170       538,848       10,294.08
122       568,536       17,510.12
57       300,000       15,614.04
62       249,205       9,623.61
98       150,000       6,326.53
75       220,000       6,666.67
62       50,302       2,058.19
50       517,729       20,000.00
27       51,000       16,666.67
44       75,704       7,118.52
33       271,724       40,499.76
25       118,049       9,664.80
42              
30       40,000       4,833.33
44 522.73
10       10,000       7,300.00
18       10,000       5,555.56
18 1,338.22
73       70,000       4,958.90
21       12,000       6,904.76
22       20,000       3,636.36
25       36,277       1,489.72
25       36,277       1,489.72
31       10,450       2,348.39
16       14,300       5,000.00
15       4,296       732.00
12 1,083.33
11 2,000.00
16       379,498      
22       1,520 673.36
12       45,000       58,333.33
34       96,619       18,817.53
37       270,000       21,621.62
25       60,000 10,000.00
10       12,500 9,000.00
270       1,934,820       27,977.57
261       3,000,000       36,781.61
219       1,675,995       17,559.77
280       903,000 15,907.14
378       2,429,367       16,666.67
181       1,143,850       22,352.93
166       900,000 20,180.72
119       600,000       31,932.77
174       2,500,000       32,628.43
124       1,103,939       17,559.77
112       363,825 8,054.72
227       1,538,000       16,173.81
161       1,370,968       23,161.53
216       1,339,903       12,503.53
102       173,481       6,795.40
96       210,000       15,833.33
97       441,737       11,759.43
56       96,000       8,000.00
72       177,833       7,501.82
62       252,390       25,266.45
78       377,182       17,409.35
74       111,000       9,891.89
33       238,000       23,848.48
30       45,000       5,919.30
39       50,000       3,846.15
32       40,000       6,250.00
25       61,766       4,237.28
41       166,903       25,266.46
24       116,056       17,409.33
49       41,000       5,102.04
43       195,821       11,759.42
9              
20       96,713       17,409.35
32       6,500       2,953.13
14       5,500       2,500.00
14       4,000       4,285.71
13       15,000       2,307.69
13       9,500       1,538.46
53       48,200       3,528.30
11       3,000       10,909.09
16       27,084       3,652.44
21       30,000       2,380.95
21       20,000       2,380.95
46       43,549       1,314.04
21       10,000       952.38

For February, sales revenue is $597,000, sales commissions are 6% of sales, the sales manager's salary is $81,100, advertising expenses are $92,500, shipping expenses total 4% of sale, and miscellaneous selling expenses are $2,400 plus 1/2 of 1% of sales. Total selling expenses for the month of February are

a.$235,700

b.$238,685

c.$176,000

d.$211,820

Find the break-even point for the revenue and cost. Draw your own graph on a piece of paper.

Fixed cost = $22,000
Variables cost = $15 per item
Price = $23

The break-even point is at ________ items.