A linear regression model is Units = 4,004 – 0.659×Week. For week 46, what is the forecast for the number of units? Round your answer to the nearest whole number.
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A linear regression model is Units = 4,004 – 0.659×Week. For week 46, what is the
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- Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? Is there an indication of a seasonal pattern? b. Use a multiple linear 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. c. Compute the quarterly forecasts for next year.Consider the following time series data: 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 8. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 8. Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.Consider the following time series data: 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 α = 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 α = 0.2. Which appears to provide the better forecast based on MSE? Explain. Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.
- A regression line has an intercept value of 174.71 and slope value of 25.22. What is the predicted value (the y-value) in the 100th period (the x-value is 100)? Round to two decimal places.In a simple linear regression based on 20 observations, it is found b1 = 3.05 and se(b1) = 1.30. Consider the hypothesis : H0 : β1 = 0 and HA : β1 ≠ 0 . Calculate the value of the test statistic.Round each z-score to the nearest hundredth.A data set has a mean of x = 6.2 and a standard deviation of 2.2. Find the z-score for each of the following. (a) x = 6.2(b) x = 7.2(c) x = 9.0(d) x = 5.0
- For the Hawkins Company, the monthly percentages of all shipments received on time over the past 12 months are 80, 82, 84, 83, 83, 84, 85, 84, 82, 83, 84, and 83. Construct a time series plot. What type of pattern exists in the data? Compare a three-month moving average forecast with an exponential smoothing forecast for α = 0.2. Which provides the better forecasts using MSE as the measure of model accuracy? What is the forecast for next month?Refer to the time series data in Problem 1. Using the average of all the historical data as a forecast for the next period, compute the following measures of forecast accuracy: a. Mean absolute error b. Mean squared error c. Mean absolute percentage error d. What is the forecast for week 7? 1. Consider the following time series data: Using the nave method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy: a. Mean absolute error b. Mean squared error c. Mean absolute percentage error d. What is the forecast for week 7?The following 20 observations are for two quantitative variables, x and y. a. Create a scatter chart for these 20 observations. b. Fit a linear trendline to the 20 observations. What can you say about the relationship between the two quantitative variables?
- The following time series shows the sales of a particular product over the past 12 months. Construct a time series plot. What type of pattern exists in the data? Use α = 0.3 to compute the exponential smoothing values for the time series. Use trial and error to find a value of the exponential smoothing coefficient α that results in a relatively small MSE.Consider the following time series data: Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy: Mean absolute error Mean squared error Mean absolute percentage error What is the forecast for week 7?Calculate the standard deviation of the following returns. Year Return 1 0.25 2 0.16 3 0.01 4 0.07 5 -0.11