Question 77.1Consider the following quarterly demand levels for electricity (1000 megawatts) in Cape Townfrom 1988 to 1991.1988 1989 1990 1991Jan-Mach 21353976Apr-June425482114July-Sept 6091136160Oct-Dec12142840a) Find the least squares trend line for electricity demand usingEx = 0.b) Find the seasonal index for each Quarter.c) Estimate the demand for electricity for Quarter 4, 1992.

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Answered: Question 7 7.1 Consider the following…

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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Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPI) and the cost of a slice of tuna. We would like to build a model using the CPI to predict the cost of a slice of pizza in a given year. Year 1960 1973 1986 1995 2002 2003 CPI (x) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of a slice of tuna(y) 0.15 0.35 1.00 1.25 1.75 2.00 Plot the data. Are we justified in creating a Linear Regression model to predict the price of a pizza slice using the CPI? Why or why not? Calculate the descriptive statistics. Calculate the sums of squares Calculate the slope, intercept, and correlation. What is this correlation value telling us? Assemble the prediction equation, and interpret the slope within the context of the problem. State the hypotheses for the hypothesis test for regression.
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Month Actual demand Forecasted Demand 1 950 1000 2 1070 1000 3 1100 1000 4 960 1000 5 1090 1000 6 1050 1000 For the dataset above, compute the overall MAPE.
Assuming equal effort (# of nets in the water has not changed and the fishing season dates have stayed the same) identify trends in the squid population you see in the data. Based on your textbook reading this week, speculate what processes or dynamics could produce the population trends seen in your data.
Illustration 12.2. The sale of a commodity in tonnes varied from January 1961 to December 1961 in the following manner: 280 300 280 280 270 240 230 230 220 200 210 200 Fit a trend by the method of semi-average.
The following table shows the average monthly production of coal in tonnes for the year 2010-2019. Year Production (in tonnes) Year Production (in tonnes) 2010 50.0 2015 38.1 2011 36.5 2016 32.6 2012 43.0 2017 41.7 2013 44.5 2018 41.1 2014 38.9 2019 33.8 Compute the price indices from 2010 to 2019 by taking 2019 as the base year. Compare the price of 2014 and 2017. What is the percentage (%) change in the production from 2016 to 2019?
The following are historical demand data: ACTUAL YEAR 2011 SEASON DEMAND Spring 201 Summer 142 Fall 380 Winter 578 2012 Spring 469 Summer 273 Fall Winter 683 963 Use regression analysis on deseasonalized demand to forecast demand in summer 2013. (Do not round intermediate calculations. Round your answer to the nearest whole number.) Forecast for summer 2013
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Need a Net balance graph for the given data

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