Question
In: Statistics and Probability
What is the forecast and MSE using regression? 2019 is the holdout sample and "car sales"...
What is the forecast and MSE using regression? 2019 is the holdout sample and "car sales" is the independent variable.
| Shipments | Car Sales | Fasteners |
| Jan-17 | 17680000 | 335798 |
| Feb-17 | 17650000 | 297853 |
| Mar-17 | 17130000 | 318399 |
| Apr-17 | 17230000 | 311730 |
| May-17 | 17200000 | 363876 |
| Jun-17 | 17200000 | 296832 |
| Jul-17 | 17180000 | 297513 |
| Aug-17 | 17020000 | 321144 |
| Sep-17 | 18380000 | 317677 |
| Oct-17 | 18200000 | 325487 |
| Nov-17 | 17860000 | 272937 |
| Dec-17 | 17700000 | 276282 |
| Jan-18 | 17550000 | 335439 |
| Feb-18 | 17560000 | 310514 |
| Mar-18 | 17690000 | 407754 |
| Apr-18 | 17770000 | 356169 |
| May-18 | 17780000 | 345322 |
| Jun-18 | 17700000 | 331997 |
| Jul-18 | 17380000 | 343059 |
| Aug-18 | 17360000 | 350277 |
| Sep-18 | 17840000 | 265205 |
| Oct-18 | 18000000 | 389332 |
| Nov-18 | 17880000 | 310474 |
| Dec-18 | 17890000 | 308429 |
| Jan-19 | 17240000 | 385807 |
| Feb-19 | 17030000 | 332529 |
| Mar-19 | 17770000 | 407606 |
| Apr-19 | 17050000 | 361946 |
| May-19 | 17930000 | 453432 |
| Jun-19 | 17710000 | 412892 |
| Jul-19 | 17440000 | 447359 |
| Aug-19 | 17510000 | 363769 |
| Sep-19 | 17720000 | 361232 |
| Oct-19 | 17050000 | 451421 |
| Nov-19 | 17450000 | 363724 |
| Dec-19 | 17160000 | 331619 |
Solutions
Expert Solution
Holdout data is a data which doesnot include in actual data to find regression
Fasteners = Dependent
Car Sales = Independent
Excel > Data > Data Analysis > Regression
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.010917236 | |||||||
| R Square | 0.000119186 | |||||||
| Adjusted R Square | -0.045329942 | |||||||
| Standard Error | 35060.75612 | |||||||
| Observations | 24 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 1 | 3223609.046 | 3223609.046 | 0.002622405 | 0.959620764 | |||
| Residual | 22 | 27043645639 | 1229256620 | |||||
| Total | 23 | 27046869248 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 305683.6791 | 368727.7498 | 0.829022712 | 0.415996031 | -459010.8707 | 1070378.229 | -459010.8707 | 1070378.229 |
| Car Sales(X) | 0.001071567 | 0.020925192 | 0.051209426 | 0.959620764 | -0.042324625 | 0.044467759 | -0.042324625 | 0.044467759 |
Fasteners(Y) = 305683.6791 + 0.0011 * Car Sales(X)
MSE = 1229256620
orchestra answered 2 years agoRelated Solutions
What is the forecast and MSE using Additive Seasonal Forecasting? 2019 is the holdout sample. Shipments...
What is the forecast and MSE using Additive Seasonal
Forecasting? 2019 is the holdout sample.
Shipments
Fasteners
Jan-17
335798
Feb-17
297853
Mar-17
318399
Apr-17
311730
May-17
363876
Jun-17
296832
Jul-17
297513
Aug-17
321144
Sep-17
317677
Oct-17
325487
Nov-17
272937
Dec-17
276282
Jan-18
335439
Feb-18
310514
Mar-18
407754
Apr-18
356169
May-18
345322
Jun-18
331997
Jul-18
343059
Aug-18
350277
Sep-18
265205
Oct-18
389332
Nov-18
310474
Dec-18
308429
Jan-19
385807
Feb-19
332529
Mar-19
407606
Apr-19
361946
May-19
453432
Jun-19
412892
Jul-19
447359
Aug-19...
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2
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xi
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8
6
12
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Compute the mean square error using
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xi
1
2
3
4
5
yi
4
6
5
12
13
(a)
Compute the mean square error using equation
s2 = MSE =
SSE
n −
2
. (Round your answer to two decimal places.)
(b)
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