For the attached data set, 1. create a 3-month and 6-month moving average forecast. 2. Calculate...

For the attached data set,

1. create a 3-month and 6-month moving average forecast.

2. Calculate the standard errors

3. compare their forecast accuracy

Month/Year	Unemployment rate
Jan-17	5.1
Feb-17	4.9
Mar-17	4.6
Apr-17	4.1
May-17	4.1
Jun-17	4.5
Jul-17	4.6
Aug-17	4.5
Sep-17	4.1
Oct-17	3.9
Nov-17	3.9
Dec-17	3.9
Jan-18	4.5
Feb-18	4.4
Mar-18	4.1
Apr-18	3.7
May-18	3.6
Jun-18	4.2
Jul-18	4.1
Aug-18	3.9
Sep-18	3.6
Oct-18	3.5
Nov-18	3.5

Expert Solution

I had done this using MINITAB as follows-

Stat- Time series -moving average . This gives output as follows-

From above standard error for 3 - month Moving Average is 0.16 and 6 - month moving average is 0.08902.

Forecast for 6 month moving average is accurate. Because it's standard error is minimum.

orchestra answered 2 years ago

Find the MAD for the 3-month and the 12-month moving average forecast. Year Month Rate(%) 2009 ...

Find the MAD for the 3-month and the 12-month moving average forecast. Year Month Rate(%) 2009 Jan 7.9 2009 Feb 8.5 2009 Mar 8.7 2009 Apr 9.1 2009 May 9.4 2009 Jun 9.4 2009 Jul 9.7 2009 Aug 9.5 2009 Sep 9.9 2009 Oct 9.9 2009 Nov 9.9 2009 Dec 9.7 2010 Jan 9.7 2010 Feb 9.6 2010 Mar 9.8 2010 Apr 9.7 2010 May 9.5 2010 Jun 9.4 2010 Jul 9.4 2010 Aug 9.4 2010 Sep 9.4 2010 Oct ...

a. Show the naive forecast, an exponential smoothing forecasts using α = 0.2, and a 3-month moving average forecast.

Month137244335450534630750829936103511411245a. Show the naive forecast, an exponential smoothing forecasts using α = 0.2, and a 3-month moving average forecast.b. Compare the MFE, MSE, and MAPE on the modelsc. Make a conclusion on which model to use.d. Find the alpha (smoothing constant) that minimizes the MSE.

Given the attached data. Answer the following questions for a 6 period moving average. MAD =...

Given the attached data. Answer the following questions for a 6 period moving average. MAD = Average(|A-F|) TS =SUM(A-F)/MAD MSE = Average(A-F)2 1. Compute your forecast for period 51. The potential answers are: A: 4414 units. B: 10290.67 units. C: 8020.83 units. D: 6324.8 units. E: 6351.86 units. 2. Compute the MAD value for period 50. The potential answers are: A: 2655.35 units. B: 3753.86 units. C: 3892.54 units. D: 3732.56 units. E: 3205.7 units. 3. Compute standard deviation of...

For the following data set [ 1, 4, 3, 6, 2, 7, 18, 3, 7, 2,...

For the following data set [ 1, 4, 3, 6, 2, 7, 18, 3, 7, 2, 4, 3, 5, 3, 7] please compute the following 1. measures of central tendency (3 points) 2. standard deviation ( 5 points) 3. is 18 an outlier? (5 points) 4. describe the shape of the distribution (2 points)

Using the stock prices in Problem 3, calculate the exponential three-month moving average for both stocks...

Using the stock prices in Problem 3, calculate the exponential three-month moving average for both stocks where two-thirds of the average weight is placed on the most recent price. Amazon Google $169.64 $600.36 $173.29 $613.40 $180.13 $586.76 $195.81 $544.10 $196.69 $529.02 $204.49 $506.38 $222.52 $603.69 $215.23 $540.96 $216.23 $515.04 $213.51 $592.64 $192.29 $599.39 $173.10 $645.90

1.at is the root mean square error (RMSE) for a "next period forecast" using 3-month moving...

1.at is the root mean square error (RMSE) for a "next period forecast" using 3-month moving average model for these three years of demand? Give your answer as an integer. 2.at is the root mean square error (RMSE) for a "next period forecast" using naive model for these three years of demand? Give your answer as an integer 3.What is the root mean square error (RMSE) for a "next period forecast" using cumulative model for these three years of demand?...

Table 6-3. Calculation of MAD Time Period Actual Sales 3 Month Moving Average Absolute Deviation

Table 6-3. Calculation of MAD Time Period Actual Sales 3 Month Moving Average Absolute Deviation Weighted Moving Average W=.1,.3,.6 Absolute Deviation Exponential Smoothing Ft = 350 Absolute Deviation 1 230 350 2 238 326 3 260 308 4 275 243 250 299 5 300 258 267 294 6 285 278 289 295 7 270 287 289 293 8 290 285 278 289 9 305 282 284 289 10 320 288 297 292 11 335 305 313 298 12 320 328 305 SUM = n = MAD 73) Refer to Table 6-3. Calculate MAD for all forecasting models. The model you would use for your forecast, based on MAD is _____________________________________. Please show work.

Consider the following two sample data sets. Set 1: 5 3 2 8 6 Set 2:...

Consider the following two sample data sets. Set 1: 5 3 2 8 6 Set 2: 3 12 13 2 7 a. Calculate the coefficient of variation for each data set. b. Which data set has more variability? a. The coefficient of variation for set 1 is nothing %. (Round to one decimal place as needed.)

(a) Develop a five month average forecast. Compute MSE and a forecast for month 13.

Consider the following time series data Month 1 2 3 4 5 6 7 8 9 10 11 12 Value 90 89 86 91 90 91 88 86 91 93 90 88 (a) Develop a five month average forecast. Compute MSE and a forecast for month 13. (b) Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for month 13. ( c) Compare the result for the five month...

3 dplyr Let’s work with the data set diamonds : data(diamonds) head(diamonds) A) Calculate the average...

3 dplyr Let’s work with the data set diamonds : data(diamonds) head(diamonds) A) Calculate the average price of a diamond: [your code here] B) Use group_by() to group diamonds by color, then use summarise() to calculate the average price and the standard deviation in price by color: [your code here) C) Use group_by() to group diamonds by cut, then use summarise() to count the number of observations by cut: [your code here] D) Use filter() to remove observations with a...

Question