Example 18.1 Suppose we are given a time series data for 12 years–1989 to 2000 relating to sales of acertain business firm. These data are given below:YearSales (Million Rs)YearSales (Million Rs)198910199515199015199624199120199715199225199821199315199915199412200024We are asked to find out the three-year moving averages, starting from 1989.

Answered: Image /qna-images/answer/d1b99a14-8aec-440b-b5b4-8bc8f5960c74.jpg

Answered: Example 18.1 Suppose we are given a…

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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The quarterly earnings of a large soft drink company have been recorded for the period 2014 - 2017. These data (in millions of rupees) are shown in the accompanying table. Year Quarter Sales Centered moving average 2014 1 52 2 67 3 85 62.03 4 54 63.38 2015 1 57 66.63 2 75 65.16 3 90 67.99 4 61 70.94 2016 1 60 70.55 2 77 73.61 3 94 71.57 4 63 72.84 2017 1 66 73.68 2 82 76.02 3 98 4 67 Find the seasonal index values using the moving averages. The trend line with de-seasonalised series was found as: Y = 62.09 + 1.14t where t is the quarter number ( t=1 for the first quarter 2014). Find the forecasted sales for the four quarters of year 2017.
Consider the following data: Month Jan-14 Feb-14 Profit ($) 16,113 16,263 Monthly Profit of an Auto Repair Shop Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 15,055 17,117 18,763 17,210 19,141 18,468 20,254 Step 3 of 4: Determine the exponential smoothing forecast for the next time period using a smoothing constant of 0.35. If necessary, round your answer to one decimal place.
a. Compute a three-period moving average for these stock prices (in R, order = 3). What are the first two moving average values? (Round your answer to two decimal points) First: $ Second: $
What year is the title of the grapth (image attached) most likely to be referring to when it says the future (Likelihood of industries becoming automated in the future")?
Need help with A & C.
Part D. Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data are used to estimate MODEL 1: The variables used in the analysis are: infmort is number of deaths within the year per 1,000 live births Ipcinc is natural log of per capita income Ipopul is natural log of the population (the population is in thousands) Iphysic is natural log of physicians per 100,000 inhabitants d90 is year dummy for 1990. For questions 1 to 4 you can assume that MLR 1-4 are satisfied. A 1. Use the Stata output below to interpret ß3. Test at a 5% significance level whether the number of physicians per capita has any effect on infant mortality rate. reg infmort 1pcinc 1popul 1physic d90 Source Model Residual Total infmort infmort = Po + B₁lpcinc + B₂lpopul + ß3lphysic + 8₁ d90 + u SS 78.0499129 350.452136 428.502049 df . 19.5124782 4 97 3.61290862 Coef. Std. Err. MS 101 4.24259454 1pcinc -4.693354 1.638132 1popul - .0551426…
Example 20.4 Given below are the price and quantity data for two years relating to three commodities. We are asked to calculate the Passche index. Commodity Ро 90 P1 10 40 15 60 Y 15 80 20 100 20 20 25 40
Based on the following graph, what is the year when the largest decrease in GDP took place ? GDP (change YoY) 6% 5% 4% 4% 3% 3% 3% 3% 2% 2% 1% -1% -1% -8% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010o 2011 2012 2013 2009 2001 2005 2007
Problem #1b: Use a two-month weighted moving average to create a forecast for May, using the weights: 0.6 and 0.4 (largest weight is for the most recent data). Calculate the forecast errors associated the weighted moving average forecasts. Month Demand January 18 February 23 March 20 April 16 May ???
Consider the data sets shown. Year 1970 1975 1980 1981 1982 1983 1984 1985 Data Set Percentage Growth for Set J J 4 370 752 834 910 996 1056 1126 Data Set K 3 20 128 186 270 391 567 822 Percentage Growth for Set K |||
Question2 The following data relate to the percentage unemployment and percentage change in wages over several years. % Unemployment (x) % Change in wages (y) 1.6 5.0 3.2 2.7 2.2 2.3 1.7 1.6 2.1 4.1 2.1 2.7 2.6 2.9 1.7 4.6 3.5 4.4 1.5 1.6 (a) Calculate the product-moment correlation coefficient between x and y. (Use Ex = 18.9, Ey = 35.2, Ex? = 37.01, Ey? = 132.22, Exy = 64.7)

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