1- Describe the 4 ways of creating competitive advantage.
2- Define and discuss the value chain
3- Describe and discuss TQM
In: Operations Management
MATLAB PROBLEM
convert the for loop to a while loop.
vec= [1 2 3 4 5]
newVec= []
for i=vec
if i>5
new vec=[newvec, i]
end
end
end
In: Computer Science
Consider the following CFG with starting variable S and Σ = {1, 2, 3, 4, 5, 6, 7,
8, 9, 0}:
S → X Y Z
X → 1 | 2
Y → W | ε
Z → Z Z | W
W → 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0
a. [20 marks] Create a derivation tree for your student number. - 84521004
b. [20 marks] Is this grammar ambiguous or unambiguous? Briefly explain (Remember the string is 84521004)
why.
In: Computer Science
Given a sequence x(n) for 0 ≤ n ≤ 3, where x(0)=4, x(1)=3, x(2)=2, and x(3)=1, evaluate your DFT X(k)
In: Electrical Engineering
Year 1 $50
Year 2-6: 4% more than the previous year
Year 7 to forever: 1% more then the previous year
At 9% APR, what is the present value
infinity
978.24
1027.15
1,078.51
1,132.43
In: Finance
In: Finance
Consider the following time series data.
| Quarter | Year 1 | Year 2 | Year 3 |
| 1 | 4 | 6 | 7 |
| 2 | 0 | 1 | 4 |
| 3 | 3 | 5 | 6 |
| 4 | 5 | 7 | 8 |
| (b) | Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. | |||||||||||||||
| If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) If the constant is "1" it must be entered in the box. Do not round intermediate calculation. | ||||||||||||||||
| ŷ = + Qtr1 + Qtr2 + Qtr3 | ||||||||||||||||
| (c) | Compute the quarterly forecasts for next year based on the model you developed in part (b). | |||||||||||||||
| If required, round your answers to three decimal places. Do not round intermediate calculation. | ||||||||||||||||
|
||||||||||||||||
| (d) | Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for Quarter 1 in Year 1, t = 2 for Quarter 2 in Year 1,… t = 12 for Quarter 4 in Year 3. | |||||||||||||||
| If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) | ||||||||||||||||
| ŷ = + Qtr1 + Qtr2 + Qtr3 + t |
In: Statistics and Probability
solve step by step using power series solution, about x = -1, 4 (x+1)^2 y'' - 2(x+1)(x+3)y' + (x+4) y =0
In: Advanced Math
Consider the following time series data.
| Quarter | Year 1 | Year 2 | Year 3 |
| 1 | 4 | 6 | 7 |
| 2 | 2 | 3 | 6 |
| 3 | 3 | 5 | 6 |
| 4 | 5 | 7 | 8 |
Compute seasonal indexes and adjusted seasonal indexes for the four quarters (to 3 decimals).
| Quarter | Seasonal Index |
Adjusted Seasonal Index |
| 1 | (___) | (___) |
| 2 | (___) | (___) |
| 3 | (___) | (___) |
| 4 | (___) | (___) |
| Total | (___) |
Consider the following time series data.
| Quarter | Year 1 | Year 2 | Year 3 |
| 1 | 5 | 5 | 6 |
| 2 | 2 | 4 | 5 |
| 3 | 4 | 6 | 6 |
| 4 | 7 | 5 | 8 |
b. Show the four-quarter and centered moving average values for this time series (to 3 decimals if necessary).
| Year | Quarter | Time Series Value | Four-Quarter Moving Average | Centered Moving Average |
| 1 | 1 | 5 | ||
| 2 | 2 | |||
| (___) | ||||
| 3 | 4 | (___) | ||
| (___) | ||||
| 4 | 7 | (___) | ||
| (___) | ||||
| 2 | 1 | 5 | (___) | |
| (___) | ||||
| 2 | 4 | (___) | ||
| (___) | ||||
| 3 | 6 | (___) | ||
| (___) | ||||
| 4 | 5 | (___) | ||
| (___) | ||||
| 3 | 1 | 6 | (___) | |
| (___) | ||||
| 2 | 5 | (___) | ||
| (___) | ||||
| 3 | 6 | |||
| 4 | 8 |
c. Compute seasonal indexes and adjusted seasonal indexes for the four quarters (to 3 decimals).
| Quarter | Seasonal Index |
Adjusted Seasonal Index |
| 1 | (___) | (___) |
| 2 | (___) | (___) |
| 3 | (___) | (___) |
| 4 | (___) | (___) |
| Total | (___) |
In: Statistics and Probability
Question 1. Consider the following time series data.
|
Week |
1 |
2 |
3 |
4 |
5 |
6 |
|
Value |
118 |
113 |
116 |
111 |
117 |
114 |
Construct a time series plot. What type of pattern exists in the data?
Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7.
Use a =0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7.
Compare the three-week moving average forecast with the exponential smoothing forecast using =0.2. Which appears to provide the better forecast based on MSE? explain.
In: Operations Management