A small business owner visits his bank to ask for a loan. The owner states that he can repay a loan at $2,100 per month for the next three years and then $1,100 per month for two years after that. If the bank is charging customers 10.00 percent APR, how much would it be willing to lend the business owner?

(a) The 2018 New Zealand Conceptual Framework states that “an asset is a present economic resource controlled by the entity as a result of past events.”

Discuss this statement in relation to inventory items that are in transit between the buyer and seller.

(b) Raymond Traders is a small business, and it undertakes periodical stock-takes to determine its inventory value. On 30 June 2020, Raymond Traders completed a physical stock-take, and inventory on hand as at 30 June 2020 had a cost of $39,600. However, some of the inventory items were deemed to be obsolete and Net Realisable value was determined to be $36,000.

(i)   Based on the information above, what inventory management system is Raymond Traders currently using? Outline one advantage and one disadvantage of the inventory management system.

(ii)   Advice Raymond Traders on the value of inventories to be shown in the Statement of Financial Position as at 30 June 2020, with reference to NZ IAS 2. Explain.

(iii)   In light of your answer (ii) above, prepare a journal entry to record any required adjustments on 30 June 2020.

(c)   NZ IAS 2, paragraph 36 requires companies to make disclosures to present inventory fairly in their financial statements. List six disclosures that companies must include in the financial statements as additional disclosures.

This assignment features an exponential function that is closely related to Moore’s Law, which states that the numbers of transistors per square inch in Central Processing Unit (CPU) chips will double every 2 years. This law was named after Dr. Gordon Moore.

Table 1 below shows selected CPUs from this leading processor company introduced between the years 1982 and 2008 in relation to their corresponding processor speeds of Million Instructions per Second (MIPS).

Table 1: Selected CPUs with corresponding speed ratings in MIPS.

Processor Year t Years After 1982 When Introduced Million Instructions per Second (MIPS)

4 1982 0 1.28

5 1985 3 2.15

6 1989 7 8.7

7 1992 10 25.6

8 1994 12 188

9 1996 14 541

10 1999 17 2,064

11 2003 21 9,726

12 2006 24 27,079

13 2008 26 59,455

(Instructions per second, n.d.) This information can be mathematically modeled by the exponential function:

MIPS(t) = (0.112)(1.405^(1.14t+9.12))

NOTE: This function is created as a “best fit” function for a table of empirical data and, therefore, does not exactly match many (or any) of the data values in the table above. Rather, the total cumulative differences from all of the data points is at a minimum for this function.

Be sure to show your work details for all calculations and explain in detail how the answers were determined for critical thinking questions. Round all value answers to three decimals.

Generate a graph of this function, MIPS(t) = (0.112)(1.405^(1.14t+9.12)), t years after 1982, using Excel or another graphing utility. (There are free downloadable programs like Graph 4.4.2 or Mathematics 4.0; or, there are also online utilities such as this site and many others.) Insert the graph into your Word document that contains all of your work details and answers. Be sure to label and number the axes appropriately. (Note: Some graphing utilities require that the independent variable must be “x” instead of “t”.)

Find the derivative of MIPS(t) with respect to t. Show your work details.

Choose a t-value between 10 and 26. Calculate the value of MIPS'(t). Show your work details.

Interpret the meaning of the derivative value that you just calculated from part 3 in terms of the MIPS(t) function and this scenario.

If the MIPS(t) function is reasonably accurate, for what value of t will the rate of increase in MIPS per year reach 6,000,000 MIPS? Approximately which year does that correspond to? Show your work details.

For the t-value you chose in part 3 above, find the equation of the tangent line to the graph of MIPS(t) at that value of t. What information about the MIPS(t) function can be obtained from the tangent line? Show your work details.

Using Web or Library resources research to find the years of introduction and the processor speeds for both the CPU A and the CPU B. Be sure to cite your creditable resources for these answers. Convert the years introduced to correct values of t by subtracting 1982 from each year. Then, determine how well the MIPS(t) function predicts the forecast CPUs’ processor speeds by comparing the calculated values with the actual MIPS ratings of these two CPUs. Show your work details.

​​​​MARKETING

• Consider this week's reading by Kotler, where he states that "... needs are not created by advertising, but are a basic part of human make-up" (1983, p.8)
• Do you agree with Kotler? Why or why not?

You are required to think deeply about these questions, and form an opinion whether you either agree or disagree with Kotler. We don't mind whether you agree or disagree, it's all about how you justify your position.

In a far away and long ago, there are only two weather states, rain and sun. If it's sunny today the probability it's be sunny tomorrow is 0.8. If it's rainy today the probability it'll be sunny tomorrow is 0.4. Weather changes are well described by a Markov chain. a) It's sunny today, and tomorrow is the start a 4-day holiday. What is the probability that all 4 days are sunny? b) A parade scheduled for the last day of the holiday. What is the probability of rain on the parade? c) A royal wedding is planned for one year (365) from today. If it's an outdoor wedding what is the probability the wedding gets rained out.

Hint: This is the steps. Thank you

n a land far away and long ago, there are only two weather states, rain and sun. If it's sunny today the probability it'll be sunny tomorrow is 0.8. If it's rainy today the probability it'll be sunny tomorrow is 0.4. Weather changes are well described by a Markov chain. (a) It's sunny today, and tomorrow is the start a 4 - day holiday. What is the probability that all 4 days are sunny? (b) A parade scheduled for the last day of the holiday. What is probability of rain on the parade? (c) A royal wed ding is planned for one year (365 days) from today. If it's an outdoor wedding what is the probability the wedding gets rained out

Using Excel.

On its municipal website, the City of Tulsa states that the rate it charges per 5 CCF of residential water is $21.62. How do the residential water rates of other U.S. public utilities compare to Tulsa's rate? The data set contains the rate per 5 CCF of residential water for 42 randomly selected U.S. cities. Formulate hypotheses that can be used to determine whether the population mean rate per 5CCF of residential water charged by U.S. public utilities differs from the $21.62 rate charged by Tulsa. Conduct a T-Test at α = 0.05 to test your hypotheses. Use Excel.

1.

2. State the null and alternative hypotheses. What type of test is this, right-tailed, left-tailed or two-tailed?

3. Do you reject or fail to reject the null hypothesis. Explain why. Use the α = 0.01 level of significance.  State your conclusion

Census data was collected on the 50 states and Washington, D.C. We are interested in determining whether average lifespan (LIFE) is related to the ratio of males to females in percent (MALE), birth rate per 1,000 people (BIRTH), divorce rate per 1,000 people (DIVO), number of hospital beds per 100,000 people (BEDS), percentage of population 25 years or older having completed 16 years of school (EDUC) and per capita income (INCO).

"STATE" "MALE" "BIRTH" "DIVO" "BEDS" "EDUC" "INCO" "LIFE"
AK 119.1 24.8 5.6 603.3 14.1 4638 69.31
AL 93.3 19.4 4.4 840.9 7.8 2892 69.05
AR 94.1 18.5 4.8 569.6 6.7 2791 70.66
AZ 96.8 21.2 7.2 536.0 12.6 3614 70.55
CA 96.8 18.2 5.7 649.5 13.4 4423 71.71
CO 97.5 18.8 4.7 717.7 14.9 3838 72.06
CT 94.2 16.7 1.9 791.6 13.7 4871 72.48
DC 86.8 20.1 3.0 1859.4 17.8 4644 65.71
DE 95.2 19.2 3.2 926.8 13.1 4468 70.06
FL 93.2 16.9 5.5 668.2 10.3 3698 70.66
GA 94.6 21.1 4.1 705.4 9.2 3300 68.54
HW 108.1 21.3 3.4 794.3 14.0 4599 73.60
IA 94.6 17.1 2.5 773.9 9.1 3643 72.56
ID 99.7 20.3 5.1 541.5 10.0 3243 71.87
IL 94.2 18.5 3.3 871.0 10.3 4446 70.14
IN 95.1 19.1 2.9 736.1 8.3 3709 70.88
KS 96.2 17.0 3.9 854.6 11.4 3725 72.58
KY 96.3 18.7 3.3 661.9 7.2 3076 70.10
LA 94.7 20.4 1.4 724.0 9.0 3023 68.76
MA 91.6 16.6 1.9 1103.8 12.6 4276 71.83
MD 95.5 17.5 2.4 841.3 13.9 4267 70.22
ME 94.8 17.9 3.9 919.5 8.4 3250 70.93
MI 96.1 19.4 3.4 754.7 9.4 4041 70.63
MN 96.0 18.0 2.2 905.4 11.1 3819 72.96
MO 93.2 17.3 3.8 801.6 9.0 3654 70.69
MS 94.0 22.1 3.7 763.1 8.1 2547 68.09
MT 99.9 18.2 4.4 668.7 11.0 3395 70.56
NC 95.9 19.3 2.7 658.8 8.5 3200 69.21
ND 101.8 17.6 1.6 959.9 8.4 3077 72.79
NE 95.4 17.3 2.5 866.1 9.6 3657 72.60
NH 95.7 17.9 3.3 878.2 10.9 3720 71.23
NJ 93.7 16.8 1.5 713.1 11.8 4684 70.93
NM 97.2 21.7 4.3 560.9 12.7 3045 70.32
NV 102.8 19.6 18.7 560.7 10.8 4583 69.03
NY 91.5 17.4 1.4 1056.2 11.9 4605 70.55
OH 94.1 18.7 3.7 751.0 9.3 3949 70.82
OK 94.9 17.5 6.6 664.6 10.0 3341 71.42
OR 95.9 16.8 4.6 607.1 11.8 3677 72.13
PA 92.4 16.3 1.9 948.9 8.7 3879 70.43
RI 96.2 16.5 1.8 960.5 9.4 3878 71.90
SC 96.5 20.1 2.2 739.9 9.0 2951 67.96
SD 98.4 17.6 2.0 984.7 8.6 3108 72.08
TN 93.7 18.4 4.2 831.6 7.9 3079 70.11
TX 95.9 20.6 4.6 674.0 10.9 3507 70.90
UT 97.6 25.5 3.7 470.5 14.0 3169 72.90
VA 97.7 18.6 2.6 835.8 12.3 3677 70.08
VT 95.6 18.8 2.3 1026.1 11.5 3447 71.64
WA 98.7 17.8 5.2 556.4 12.7 3997 71.72
WI 96.3 17.6 2.0 814.7 9.8 3712 72.48
WV 93.9 17.8 3.2 950.4 6.8 3038 69.48
WY 100.7 19.6 5.4 925.9 11.8 3672 70.29

Suppose we are interested in fitting a regression model using LIFE as the response variable and some subset of the variables (MALE, BIRTH, DIVO, and INCO) as predictor.

lm(life ~ male + birth + divo + inco, data = DATA)

(i.1) Perform variable selection by finding the subset model that minimizes the AIC criteria. State the ’best model’.
(i.2) Perform variable selection using forward selection. State the ’best model’.

Please answer the above question using R command. List all the R commands you have used, the screenshot of the data you referring to, and which is the best model referring to the data. Thanks!

Census data was collected on the 50 states and Washington, D.C. We are interested in determining whether average lifespan (LIFE) is related to the ratio of males to females in percent (MALE), birth rate per 1,000 people (BIRTH), divorce rate per 1,000 people (DIVO), number of hospital beds per 100,000 people (BEDS), percentage of population 25 years or older having completed 16 years of school (EDUC) and per capita income (INCO).

"STATE" "MALE" "BIRTH" "DIVO" "BEDS" "EDUC" "INCO" "LIFE"
AK 119.1 24.8 5.6 603.3 14.1 4638 69.31
AL 93.3 19.4 4.4 840.9 7.8 2892 69.05
AR 94.1 18.5 4.8 569.6 6.7 2791 70.66
AZ 96.8 21.2 7.2 536.0 12.6 3614 70.55
CA 96.8 18.2 5.7 649.5 13.4 4423 71.71
CO 97.5 18.8 4.7 717.7 14.9 3838 72.06
CT 94.2 16.7 1.9 791.6 13.7 4871 72.48
DC 86.8 20.1 3.0 1859.4 17.8 4644 65.71
DE 95.2 19.2 3.2 926.8 13.1 4468 70.06
FL 93.2 16.9 5.5 668.2 10.3 3698 70.66
GA 94.6 21.1 4.1 705.4 9.2 3300 68.54
HW 108.1 21.3 3.4 794.3 14.0 4599 73.60
IA 94.6 17.1 2.5 773.9 9.1 3643 72.56
ID 99.7 20.3 5.1 541.5 10.0 3243 71.87
IL 94.2 18.5 3.3 871.0 10.3 4446 70.14
IN 95.1 19.1 2.9 736.1 8.3 3709 70.88
KS 96.2 17.0 3.9 854.6 11.4 3725 72.58
KY 96.3 18.7 3.3 661.9 7.2 3076 70.10
LA 94.7 20.4 1.4 724.0 9.0 3023 68.76
MA 91.6 16.6 1.9 1103.8 12.6 4276 71.83
MD 95.5 17.5 2.4 841.3 13.9 4267 70.22
ME 94.8 17.9 3.9 919.5 8.4 3250 70.93
MI 96.1 19.4 3.4 754.7 9.4 4041 70.63
MN 96.0 18.0 2.2 905.4 11.1 3819 72.96
MO 93.2 17.3 3.8 801.6 9.0 3654 70.69
MS 94.0 22.1 3.7 763.1 8.1 2547 68.09
MT 99.9 18.2 4.4 668.7 11.0 3395 70.56
NC 95.9 19.3 2.7 658.8 8.5 3200 69.21
ND 101.8 17.6 1.6 959.9 8.4 3077 72.79
NE 95.4 17.3 2.5 866.1 9.6 3657 72.60
NH 95.7 17.9 3.3 878.2 10.9 3720 71.23
NJ 93.7 16.8 1.5 713.1 11.8 4684 70.93
NM 97.2 21.7 4.3 560.9 12.7 3045 70.32
NV 102.8 19.6 18.7 560.7 10.8 4583 69.03
NY 91.5 17.4 1.4 1056.2 11.9 4605 70.55
OH 94.1 18.7 3.7 751.0 9.3 3949 70.82
OK 94.9 17.5 6.6 664.6 10.0 3341 71.42
OR 95.9 16.8 4.6 607.1 11.8 3677 72.13
PA 92.4 16.3 1.9 948.9 8.7 3879 70.43
RI 96.2 16.5 1.8 960.5 9.4 3878 71.90
SC 96.5 20.1 2.2 739.9 9.0 2951 67.96
SD 98.4 17.6 2.0 984.7 8.6 3108 72.08
TN 93.7 18.4 4.2 831.6 7.9 3079 70.11
TX 95.9 20.6 4.6 674.0 10.9 3507 70.90
UT 97.6 25.5 3.7 470.5 14.0 3169 72.90
VA 97.7 18.6 2.6 835.8 12.3 3677 70.08
VT 95.6 18.8 2.3 1026.1 11.5 3447 71.64
WA 98.7 17.8 5.2 556.4 12.7 3997 71.72
WI 96.3 17.6 2.0 814.7 9.8 3712 72.48
WV 93.9 17.8 3.2 950.4 6.8 3038 69.48
WY 100.7 19.6 5.4 925.9 11.8 3672 70.29

Suppose we are interested in fitting a regression model using LIFE as the response variable and some subset of the variables (MALE, BIRTH, DIVO, and INCO) as predictor.
(i.1) Perform variable selection by finding the subset model that minimizes the AIC criteria. State the ’best model’.
(i.2) Perform variable selection using forward selection. State the ’best model’.
(i.3) Perform variable selection using backward selection. State the ’best model’.

Please answer the above question using R command. List all the R commands you have used, the screenshot of the data you referring to, and which is the best model referring to the data. Thanks!

Census data was collected on the 50 states and Washington, D.C. We are interested in determining whether average lifespan (LIFE) is related to the ratio of males to females in percent (MALE), birth rate per 1,000 people (BIRTH), divorce rate per 1,000 people (DIVO), number of hospital beds per 100,000 people (BEDS), percentage of population 25 years or older having completed 16 years of school (EDUC) and per capita income (INCO).

"STATE" "MALE" "BIRTH" "DIVO" "BEDS" "EDUC" "INCO" "LIFE"
AK 119.1 24.8 5.6 603.3 14.1 4638 69.31
AL 93.3 19.4 4.4 840.9 7.8 2892 69.05
AR 94.1 18.5 4.8 569.6 6.7 2791 70.66
AZ 96.8 21.2 7.2 536.0 12.6 3614 70.55
CA 96.8 18.2 5.7 649.5 13.4 4423 71.71
CO 97.5 18.8 4.7 717.7 14.9 3838 72.06
CT 94.2 16.7 1.9 791.6 13.7 4871 72.48
DC 86.8 20.1 3.0 1859.4 17.8 4644 65.71
DE 95.2 19.2 3.2 926.8 13.1 4468 70.06
FL 93.2 16.9 5.5 668.2 10.3 3698 70.66
GA 94.6 21.1 4.1 705.4 9.2 3300 68.54
HW 108.1 21.3 3.4 794.3 14.0 4599 73.60
IA 94.6 17.1 2.5 773.9 9.1 3643 72.56
ID 99.7 20.3 5.1 541.5 10.0 3243 71.87
IL 94.2 18.5 3.3 871.0 10.3 4446 70.14
IN 95.1 19.1 2.9 736.1 8.3 3709 70.88
KS 96.2 17.0 3.9 854.6 11.4 3725 72.58
KY 96.3 18.7 3.3 661.9 7.2 3076 70.10
LA 94.7 20.4 1.4 724.0 9.0 3023 68.76
MA 91.6 16.6 1.9 1103.8 12.6 4276 71.83
MD 95.5 17.5 2.4 841.3 13.9 4267 70.22
ME 94.8 17.9 3.9 919.5 8.4 3250 70.93
MI 96.1 19.4 3.4 754.7 9.4 4041 70.63
MN 96.0 18.0 2.2 905.4 11.1 3819 72.96
MO 93.2 17.3 3.8 801.6 9.0 3654 70.69
MS 94.0 22.1 3.7 763.1 8.1 2547 68.09
MT 99.9 18.2 4.4 668.7 11.0 3395 70.56
NC 95.9 19.3 2.7 658.8 8.5 3200 69.21
ND 101.8 17.6 1.6 959.9 8.4 3077 72.79
NE 95.4 17.3 2.5 866.1 9.6 3657 72.60
NH 95.7 17.9 3.3 878.2 10.9 3720 71.23
NJ 93.7 16.8 1.5 713.1 11.8 4684 70.93
NM 97.2 21.7 4.3 560.9 12.7 3045 70.32
NV 102.8 19.6 18.7 560.7 10.8 4583 69.03
NY 91.5 17.4 1.4 1056.2 11.9 4605 70.55
OH 94.1 18.7 3.7 751.0 9.3 3949 70.82
OK 94.9 17.5 6.6 664.6 10.0 3341 71.42
OR 95.9 16.8 4.6 607.1 11.8 3677 72.13
PA 92.4 16.3 1.9 948.9 8.7 3879 70.43
RI 96.2 16.5 1.8 960.5 9.4 3878 71.90
SC 96.5 20.1 2.2 739.9 9.0 2951 67.96
SD 98.4 17.6 2.0 984.7 8.6 3108 72.08
TN 93.7 18.4 4.2 831.6 7.9 3079 70.11
TX 95.9 20.6 4.6 674.0 10.9 3507 70.90
UT 97.6 25.5 3.7 470.5 14.0 3169 72.90
VA 97.7 18.6 2.6 835.8 12.3 3677 70.08
VT 95.6 18.8 2.3 1026.1 11.5 3447 71.64
WA 98.7 17.8 5.2 556.4 12.7 3997 71.72
WI 96.3 17.6 2.0 814.7 9.8 3712 72.48
WV 93.9 17.8 3.2 950.4 6.8 3038 69.48
WY 100.7 19.6 5.4 925.9 11.8 3672 70.29

Fit the MLR model with LIFE (y) as the response variable, and MALE (x1), BIRTH (x2), DIVO (x3), BEDS (x4), EDUC (x5), and INCO (x6), as predictors.
Compute and report the terms in the decomposition SSreg(β1,β2,β3|β0)=SSreg(β3|β0)+SSreg(β2|β0,β3)+SSreg(β1|β0,β3,β2) using R command

almost three-fourths of us pecans are grown in the states of GA,NM, and TX it is known that the average yield of pecans in DA county is mean=1625 and standard dev.= 200 if a random sample of 18 acres of a land is taken the probability of the average yield of these 18 acres being at most 1700 lbs would be
.9941?