Consider the following time series data. I JUST NEED PART F!! Quarter Year 1 Yea
ID: 3290679 • Letter: C
Question
Consider the following time series data.
I JUST NEED PART F!!
Quarter Year 1 Year 2 Year 3 1 4 6 7 2 2 3 6 3 3 5 6 4 5 7 8 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) Value = + Qtr1t + Qtr2t + Qtr3t (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. Quarter 1 forecast Quarter 2 forecast Quarter 3 forecast Quarter 4 forecast (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) Value = + Qtr1t + Qtr2t + Qtr3t + t (e) Compute the quarterly forecasts for next year based on the model you developed in part (d). Round your interim computations and final answers to three decimal places. Quarter 1 forecast Quarter 2 forecast Quarter 3 forecast Quarter 4 forecast (f) Is the model you developed in part (b) or the model you developed in part (d) more effective? If required, round your intermediate calculations and final answer to three decimal places. Model developed in part (b) Model developed in part (d) MSEExplanation / Answer
Answer:
(f)
Is the model you developed in part (b) or the model you developed in part (d) more effective?
If required, round your intermediate calculations and final answer to three decimal places.
Model developed in part (b)
Model developed in part (d)
MSE
2.833
0.220
Model we developed in part (d) is more effective.
Regression Analysis
R²
0.398
Adjusted R²
0.173
n
12
R
0.631
k
3
Std. Error
1.683
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
15.0000
3
5.0000
1.76
.2314
Residual
22.6667
8
2.8333
Total
37.6667
11
Regression output
confidence interval
variables
coefficients
std. error
t (df=8)
p-value
95% lower
95% upper
Intercept
6.6667
0.9718
6.860
.0001
4.4256
8.9077
Q1
-1.0000
1.3744
-0.728
.4876
-4.1693
2.1693
Q2
-3.0000
1.3744
-2.183
.0606
-6.1693
0.1693
Q3
-2.0000
1.3744
-1.455
.1837
-5.1693
1.1693
Regression Analysis
R²
0.959
Adjusted R²
0.936
n
12
R
0.979
k
4
Std. Error
0.469
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
36.1250
4
9.0313
41.01
.0001
Residual
1.5417
7
0.2202
Total
37.6667
11
Regression output
confidence interval
variables
coefficients
std. error
t (df=7)
p-value
95% lower
95% upper
Intercept
3.4167
0.4284
7.975
.0001
2.4036
4.4297
Q1
0.2188
0.4029
0.543
.6040
-0.7339
1.1714
Q2
-2.1875
0.3921
-5.580
.0008
-3.1146
-1.2604
Q3
-1.5938
0.3854
-4.135
.0044
-2.5051
-0.6824
t
0.4063
0.0415
9.794
2.45E-05
0.3082
0.5043
(f)
Is the model you developed in part (b) or the model you developed in part (d) more effective?
If required, round your intermediate calculations and final answer to three decimal places.