Consider the following time series data.Time Series ValueQuarter Year 1O116-15-14-13-12-11+10-9-8723411(a) Construct a time series plot.910121 2 3 4Year 1quarter 3 forecastquarter 4 forecastYear 213101214Year 31 2 3 4Year 2Year/Quarter141313151 2 3 4Year 3Time Series Value161514-131211-10+98+71 2 3 4Year 11 2 3 4Year 2Year/QuarterWhat type of pattern exists in the data?O The time series plot shows a horizontal pattern and no seasonal pattern in the data.The time series plot shows a linear trend and a seasonal pattern in the data.O The time series plot shows a linear trend and no seasonal pattern in the data.O The time series plot shows a horizontal pattern, but there is also a seasonal pattern in the data.(c) Compute the quarterly forecasts for next year. (Round your answers to one decimal place.)quarter 1 forecastquarter 2 forecast1 2 3 4Year 3Time Series Value16 T15141312111016 T15141312mim11109-8-78-71 2 3 4 1 2 3 4 1 2 3 4Year 1Year 3Year 2Year/Quarter(b) Use the following dummy variables to develop an estimated regression equation to account for any seasonal and linear trend effects in the data. (Round your numerical values to two decimal places.)x₁ = 1 if quarter 1, 0 otherwise; x₂ = 1 if quarter 2, 0 otherwise; x3 = 1 if quarter 3, 0 otherwiseX31 2 3 4 1 2 3 4 1 2 3 4Year 1Year 2Year 3Year/Quarter

a) Time Series plot: Excel Procedure: Enter the data for Period and Value in Excel sheet Select the…

Answered: the following time series dat Quarte…

A First Course in Probability (10th Edition)

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Chapter1: Combinatorial Analysis

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Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Which of the following time series forecasting methods would not be used to forecast seasonal data?
1. Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
consider the following time series data.Month 1 2 3 4 5 6 7Value 24 13 20 12 19 23 15a. compute MSe using the most recent value as the forecast for the next period. Whatis the forecast for month 8?b. compute MSe using the average of all the data available as the forecast for the nextperiod. What is the forecast for month 8?c. Which method appears to provide the better forecast?
Which of the time series forecasting methods would not be used to forecast seasonal data?
Define the term 'time series data' and continue to discuss the components of a time series.
Consider the following gasoline sales time series data. Click on the datafile logo to reference the data. Week Sales (1000s of gallons) 1 16 2 20 3 20 4 23 5 18 6 17 8 19 9 23 10 19 11 14 12 21 a. Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and 1/6 the most recent observation, compute a three-week weighted moving average for the time series (to 2 decimals). Enter negative values as negative numbers. Week Time-Series Value Weighted MovingAverage Forecast ForecastError (Error)2 1 2 3 4 5 6 7 8 9 10 11 12 Total b. Compute the MSE for the weighted moving average in part (a).MSE = Do you prefer this weighted moving average to the unweighted moving average? Remember that…
Consider the following time series data: 1 2 3 4 5 6 7 26 15 22 14 21 25 17 PART 1.Compute MSE using the most recent value as the forecast for the next period and then calculate the forecast for month 8. PART 2.Compute MSE using the average of all the data available as the forecast for the next period. What is the forecast for month 8?
The table below contains the average price paid for a new home in a certain area from 2000 to 2010. a. Construct a time-series plot of new home prices. b. What pattern, if any, is present in the data? Year Average_Price_($_thousands)2000 351.12001 330.52002 310.52003 296.72004 229.72005 182.32006 154.52007 156.32008 154.72009 154.52010 154.5
The following data set provides the total number of shipments of core major household appliances in the U.S. from 2000 to 2016 (in millions): Year Shipments (millions) 2000 38.4 2001 38.2 2002 40.8 2003 42.5 2004 46.1 2005 47.0 2006 46.7 2007 44.1 2008 39.8 2009 36.5 2010 38.2 2011 36.0 2012 35.8 2013 39.2 2014 41.5 2015 42.9 2016 44.7 a. Plot the time series. b. Fit a three-year moving average to the data and plot the results. c. Fit a five-year moving average to the data and plot the results. d. Compute a linear trend forecasting equation and plot the trend line. e. Compute a quadratic trend forecasting equation and plot the results.
Consider the following gasoline sales time series data. Click on the datafile logo to reference the data. Week. Sales (1000s of gallons) 1 16 2. 22 3. 18 4. 23 5. 18 6. 17 7 21 8 17 9 21 10. 21 11 14 12 23 Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and third the most recent observation, 1/6 compute a three-week weighted moving average for the time series (to 2 decimals). Enter negative values as negative numbers. Week Time-Series Value Weighted MovingAverage Forecast ForecastError (Error)2 Total b.…
Calculate the first centered moving average for this time series data
#4) Commuter ridership in Athens, Greece, during the summer months is believed to be heavily tied to the number of tourists visiting the city. During the past 12 years, the data are given in the following table. Year Number of Tourists (millions) Ridership (hundreds of thousands) 1 6 11 2 11 16 3 8 16 4 10 14 5 19 28 6 18 26 7 16 21 8 20 25 9 24 45 10 18 28 11 11 18 12 19 35 a) Create a time series plot for the ridership. b) Using linear regression to see if using the year is a good predictor for the ridership. What is the regression equation? How accurate is the model? c) Using linear regression to see if using the number of tourists is a good predictor for the ridership. What is the regression equation? How accurate is the model? d) Which linear regression equation is better? What is the expected ridership if 10 million tourists visit the city next year? e) Excel File

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the following time series dat Quarte Year 11 Time Series Value 2 (a) Construct a time series plot. 22HD987 10 12 Year 2 13 10 12 14 Year 3 14 13 Time Series Value 2543N-C807 12 Time Series Value ==== NOO DOM Time Series Value 1960-NW+54 -