Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Using exponential smoothing with a smoothing constant (α) of0.30 and a starting forecast of 410.00, the following sales forecast has been developed:YearYearForecasted Sales20052006200720082009Sales450495518563584The MAD for a forecast developed using exponential smoothing with a = 0.30 is 74.74 sales (round your response to two decimal places).Forecasted sales for years 2006 through 2010 using exponential smoothing with α = 0.60 and a starting forecast of 410.00 are (round your responses to two decimal places):20062005410.00ForecastedSales2007410.00422.00443.90466.13495.1920082009 2010

Exponential Smoothing: Exponential smoothing is a technique used in time series forecasting to…

Answered: Sales of Volkswagen's popular Beetle…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Data contains brain mass in different species versus glia-neuron ratio, the latter being a measurement of brain metabolism as the glia provides the metabolic needs of the neurons. The relationship between THE LOGARITHM of the brain mass (in the third column) and Glia-neuron ratio (fourth column) appears linear and it is these two variables that we wish to analyze via linear regression. We would like to know if the human brain fits the trend from the other species. Towards this end we will perform the regression on all species EXCEPT humans (Homo sapiens). Again, throw out the human data from your analysis. You will however need the human numbers for some of the questions. What is the coefficient of determination (R2) for the data? What is the standard error of the slope of the regression line (SEb) ?
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?
The following is a 7-month-demand-history for a certain product. Assuming that linear trend appears, develop the equation of regression line. Round values of coefficients to two decimal places. Use the equation to prepare a forecast for December 2004.
(d) If the life expectancy is increased by 3.5 years in a certain country, how much will the happiness index change? Round to two decimal places. (e) Use the regression line to predict the happiness index of a country with a life expectancy of 84 years. Round to two decimal places.
Suppose a firm has had the following historic sales figures. Year: 2016 2017 2018 2019 2020 Sales $2,570,000 $3,750,000 $4,380,000 $5,010,000 $5,520,000 What would be the forecast for next year’s sales using regression to estimate a trend?
Most people know that their chances of getting cancer increase as they age. In fact, by looking at data compiled by the National Cancer Institute you can readily see that incidence of cancer increases dramatically between the ages of 35 and 80. Medicine Students initially assumed that this relationship is linear with R = 0.80. Are the students correct? Test if the data fits better to other models.
Eloise needs to finish a 516-page report on international trade so she can present it at the big conference this weekend. She's been tracking her page count at the end of each business day to see if she's on track. Use Excel to fit a trendline to Eloise's data to create a model of her productivity. According to your model, will Eloise be able to finish her report by the end of the day next Friday? Assume she works only on weekdays. Day Pages Monday 425 Tuesday 431 Wednesday 449 Thursday 464 Friday 471 A: Use Excel to fit a trendline to Eloise's data. What are the slope and intercept for this line? The slope is The intercept is B: Use your model (trendline) to answer the question. Will Eloise finish her report by the end of next friday? Yes
Table 3 gives the market value of a luxury sedan (in dollars) xyears after its purchase. Find an exponential regression model of the form y= ab* for this data set. Estimate the purchase price of the sedan. Estimate the value of the sedan 10 years after its purchase. Round answers to the nearest dollar. TABLE 3 Value ($) 1 23,125 19,050 3 15,625 11,875 5 9,450 6 7,125 2.
Smith is studying the relationship between length (X) and weight (Y) of infants. Please help him fit an exponential growth model to find the relationship between length and weight. Write down the model and explain how to fit the model (i.e., how to estimate regression coefficients) and what the intercept is for the relationship between X and Y (in symbolic form).
Suppose that a simple linear regression model is appropriate for describing the relationship between y = house price (in dollars) and x = house size (in square feet) for houses in a large city. The population regression line is y = 22,500 + 43x and ?e = 4,000. b) Approximately what proportion of 2,000 sq ft homes would be priced over $110,000? (You may need to use a table. Round your answer to four decimal places.) Approximately what proportion of 2,000 sq ft homes would be priced under $100,000? (You may need to use a table. Round your answer to four decimal places.)
Please help with correct answers in detail as last answer given was incorrect: Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 3 4 5 6 Value 19 14 17 12 18 15 Does a smoothing constant of 0.2 or 0.4 appear to provide more accurate forecasts based on MSE? choose correct answer The exponential smoothing using ? = 0.2 provides a better forecast since it has a smaller MSE than the exponential smoothing using ? = 0.4. The exponential smoothing using ? = 0.2 provides a better forecast since it has a larger MSE than the exponential smoothing using ? = 0.4. The exponential smoothing using ? = 0.4 provides a better forecast since it has a smaller MSE than the exponential smoothing using ? = 0.2. The exponential smoothing using ? = 0.4 provides a better forecast since it has a larger MSE than the exponential smoothing using ? = 0.2.
An economist at Nedbank ran a study of the relationship between FTSE/JSE All Shares index return (JALSH) and consumer price index (CPI) from 2006 to 2017, the data collected is shown in the Table 1 below. FTSE/JSE All Shares index return (JALSH) and consumer price index (CPI) from 2006 to 2017. Year JALSH (Y) CPI (X) 2006 0.41 4.7 2007 0.19 7.1 2008 -0.23 11.5 2009 0.32 7.1 2010 0.19 4.3 2011 0.03 5.0 2012 0.27 5.6 2013 0.21 5.7 2014 0.11 6.1 2015 0.05 4.6 2016 0.00 6.4 2017 0.21 5.3 The estimated regression…

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