A B 2 D E F G H α β 0.2 0.2 Trend Forecast 3 Exponential Smoothing with Trend 4 5 Week 0 Sales Level 6 1 56 7 2 59 8 3 53 9 4 60 10 5 63 11 6 12 13 Fill in the yellow boxes with the appropriate constants and formulas. 3. The following data are for computer sales in units at an electronics store over the past five weeks: WEEK 1 2 3 4 5 DEMAND 56 59 53 60 63 Use trend-adjusted exponential smoothing with a = 0.2 and ẞ= 0.2 to forecast sales for weeks 3 through 6. Assume that the smoothed average is 55 units and that the average trend was +6 units per week just before week 1.

Table 4 gives the population of a town (in thousand) from 2000 to 2008. What was the average rate of change of population (a) between 2002 and 2004, and (b) between 2002 and 2006?

The U.S. Census tracks the percentage of persons 25 years or older who are college graduates. That data forseveral years is given in Table 4[14]. Determine whether the trend appears linear. If so, and assuming the trendcontinues. in what year will the percentage exceed 35%?

Table 3 gives the annual sales (in millions of dollars) of a product from 1998 to 20006. What was the average rate of change of annual sales (a) between 2001 and 2002, and (b) between 2001 and 2004?

Based on the scatterplot and residual plot, what type of model would be appropriate for summarizing the relationship between days and number of views? A popular television show recently released a video preview for the upcoming season on its website. As fans of the show discover the video, the number of views of the preview video has grown each day during the last 2 weeks. The number of days since the release of the video and the natural log of the number of video views is shown in the scatterplot. O An exponential model is appropriate because the residual plot does not show a clear pattern. O A linear model is appropriate because the scatterplot shows a strong, positive linear relationship. In(Views) vs. Days O A power model is appropriate because the scatterplot of days and natural log of views is strong and linear. 10 9.5 An exponential model is appropriate because the relationship between days and the natural log of views is linear, and the residual plot does not show a clear…

Monthly sales have been found to follow a linear trend of y = 10 + 8x, where y is the number of items sold and x is the number of the month. Monthly deviations from the trend have been calculated and follow an additive model. In month 12, the seasonal variation is estimated to be -6. What is the forecasted number of items to be sold in month 12?

The follow table gives the approximate economic value associated with various levels of oil recovery in Texas. Find the regression line, and use it to estimate the economic value associated with a recovery level of 70%.

A coffee shop owner wants to estimate demand for the next four quarters for hot chocolate. Sales data consist of trend and seasonality. Quarter relatives are 1.06 for the first quarter, 1.03 for the second quarter, 0.61 for the third quarter, and 1.30 for the fourth quarter. Using the appropriate values of quarter relatives and the equation Ft = 187 +9.2t for the trend component, estimate demand for periods 9-12. (Round all calculations to two decimal points. Demand Period 9 Demand Period 10 Demand Period 11. Demand Period 12

A popular television show recently released a video preview for the upcoming season on its website. As fans of the show discover the video, the number of views of the preview video has grown each day during the last 2 weeks. The number of days since the release of the video and the natural log of the number of video views is shown in the scatterplot.     Based on the scatterplot and residual plot, what type of model would be appropriate for summarizing the relationship between days and number of views? An exponential model is appropriate because the residual plot does not show a clear pattern. A linear model is appropriate because the scatterplot shows a strong, positive linear relationship. A power model is appropriate because the scatterplot of days and natural log of views is strong and linear. An exponential model is appropriate because the relationship between days and the natural log of views is linear, and the residual plot does not show a clear pattern.

While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. After performing a regression, they find that the relationship is (claims per year) = 0.17* (age) + 2.04. If a customer is 47 years old and they make an average of 14.72 claims per year, what is the residual?