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The linear trend equation for the following data is

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Unformatted text preview: the significance of the t2 term. 1-1558 Chapter 01 - An Introduction to Business Statistics 115. Consider a time series with 15 quarterly sales observations. Using the quadratic trend model the following partial computer output was obtained. Test the significance of the t2 term at α = .05. State the critical t value (rejection point) and the p-value. Make your decision using a two-sided null hypothesis. 116. Consider a time series with 15 quarterly sales observations. Using the quadratic trend model the following partial computer output was obtained. What is the predicted value of y when t = 20? 1-1559 Chapter 01 - An Introduction to Business Statistics 117. Use the following information for the three grains. Calculate the simple price index for each grain separately. 118. Use the following information for the three grains. Calculate the aggregate price index. 119. Use the following information for the three grains. Calculate the Laspeyres index. 1-1560 Chapter 01 - An Introduction to Business Statistics 120. Use the following information for the three grains. Calculate the Paasche index. 121. The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the simple price index for each product separately. 1-1561 Chapter 01 - An Introduction to Business Statistics 122. The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the aggregate price index. 123. The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the Laspeyres index. 1-1562 Chapter 01 - An Introduction to Business Statistics 124. The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the Paasche index. 125. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. Calculate the 4 period (quarter) moving average for the entire time series. 1-1563 Chapter 01 - An Introduction to Business Statistics 126. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. Calculate the 4 period (quarter) centered moving average for the entire time series. 127. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. Calculate the ratio of actual production to the centered moving average values (sn t * irt) for the entire time series. 1-1564 Chapter 01 - An Introduction to Business Statistics 128. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. Calculate the average seasonal factor for each quarter . Centered moving average values and their respective periods are given below. 129. Based on the quarterly production data (in thousands of units) for the XYZ manufacturing company, the average seasonal factor is .986 for winter, .915 for spring, 1.125 for summer and .925 for fall. Determine the normalized (adjusted) seasonal factors for each quarter. 1-1565 Chapter 01 - An Introduction to Business Statistics 130. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are .9982, .9263, 1.139, .9365 for winter, spring, summer, and fall respectively. Calculate the deseasonalized production value for each observation in the time series. 1-1566 Chapter 01 - An Introduction to Business Statistics 131. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are .9982, .9263, 1.139, .9365 for winter, spring, summer and fall respectively. Based on the following deseasonalized observations (dt) given below, a trend line was estimated. The following MINITAB output gives the straight-line trend equation fitted to the deseasonalized observations. Based on the trend equation given below, calculate the trend value for each period in the time series. The regression equation is Deseasonalized = 10.1 + 1.91 Time 1-1567 Chapter 01 - An Introduction to Business Statistics 132. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are .9982, .9263, 1.139, .9365 for winter, spring, summer and fall respectively. Based on the following deseasonalized observations (dt) given below, a trend line was estimated. The linear regression trend equation is: trt = 10.1 + 1.91 (t). Based on this trend equation, the following trend values are calculated for each period in the time series: Isolate the cyclical and irregular components by calculating the estimate of CLt* IRt for the first four quarters in the time series. 1-1568 Chapter 01 - An Introduction to Business Statistics 133. Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal fa...
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