The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. QUARTER SALES 1 220 2 232 3 201 4 193 5 163 6 194 7 155 8 136 Use regression and seasonal indexes to forecast quarterly sales for the next year. Note: Do not round intermediate calculations. Round your answers to the nearest whole number.

The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers):   A.What would be the typical linear regression equation for the number of sales?    B.Make the sales forecast for period 15 of next year.    C. Make the sales forecast for period 17 of next year.      D. What is the standard error for the data?

Graph the scatterplot and find the equation of the regression line GDP.       CO2 emissions (in millions metric tons) 1.7.          552.6 1.2.          462.3 2.5.          475.4 2.8.          374.3 3.6.          748.5 2.2.          400.9 0.8.          253.0 1.5.           318.6 2.4.           496.8 5.9.           1180.6

Eloise needs to finish a 516-page report on international trade so she can present it at the big conference this weekend. She's been tracking her page count at the end of each business day to see if she's on track. Use Excel to fit a trendline to Eloise's data to create a model of her productivity. According to your model, will Eloise be able to finish her report by the end of the day next Friday? Assume she works only on weekdays. Day Pages Monday 425 Tuesday 431 Wednesday 449 Thursday 464 Friday 471 A: Use Excel to fit a trendline to Eloise's data. What are the slope and intercept for this line? The slope is The intercept is B: Use your model (trendline) to answer the question. Will Eloise finish her report by the end of next friday? Yes

Table 3 gives the market value of a luxury sedan (in dollars) xyears after its purchase. Find an exponential regression model of the form y= ab* for this data set. Estimate the purchase price of the sedan. Estimate the value of the sedan 10 years after its purchase. Round answers to the nearest dollar. TABLE 3 Value ($) 1 23,125 19,050 3 15,625 11,875 5 9,450 6 7,125 2.

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Life expectancy in the United States has been rising since the nineteenth century. The table shows the U.S. life expectancy in selected years. Year 1950 1960 1970 1980 1990 2000 Life expectancy at birth 68.2 69.7 70.8 73.7 75.4 77 a. Let t represent the number of years after 1950, and plot the data. Draw a line a best fit for the data points. 80+ 76- 5 10 15 20 25 30 35 40 45 50 Draw: 72+ 64- 60+ 56 52 48- 44 Clear All

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. Fill in the "Years since 1970" column for the data to the right. Make a scatter plot in Excel of the data using Years since 1970 and Total Waste in Millions of Tons (Title your graph and label your axes appropriately). How would you best describe the trend? Add an exponential regression model to the data. Write the equation below. Is it a good fit? Explain how you know.  Based on the exponential model, what is the growth rate per year? Explain the meaning of the growth rate in the context of the data. Using the exponential model, make a prediction of how much plastic will be in landfills (and the ocean gyres) by Year Years since 1970 Total Waste in Millions of Tons 1970 0 249 1980 10 798 1985 15 1200 1990 20 1740 1995 25 2440 2000 30 3390 2005 35 4600 2010 40 6060 2015 45 7820     Do you think recycling alone will solve this problem (See Q11 and Q12)? What are some of the challenges in recycling plastic that might hinder the world from recycling all plastic…

What is the explanatory variable,   what is the response variable. What is the regression equation for this data.   Plot the regression line in your calculator and draw it on your graph.   Interpret in context the slope.   Interpret in context the y-intercept.   What is the residual at 45 years of age.   Plot the residual plot on the axis to the right