Use regression analysis on deseasonalized demand to forecast demand in summer 2013.Forecast for summer 2023 ______ The following are historical demand data:ACTUALYEAR SEASONDEMAND2011Spring204Summer148Fall380Winter5652012Spring482Summer265Fall680Winter966

The question is about time series analysis.The following table shows the historical data for…

Answered: The following are historical demand…

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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You estimate a regression equation that estimates individuals’ yearly income as a function of age (measured in years), years of education, and their score on a standardized intelligence exam (IQ test). The estimated coefficients are: I = 26.3 + 4.21*Age + 7.21*Education + .23 * IQ Where I is income measured in thousands of dollars. Based on the regression results, how much more should an individual earn after attending graduate school for two years? Explain your reasoning.
Forecast the percentage of tax returns that will be electronically filed for 2010 using exponential smoothing with α=0.1.
Please complete parts b and c
Use the following information to compute a single exponential smoothing model forecast with a smoothing constant (α = 0.30) to produce the actual demand for the period.) Use 115 as the initial forecast. Click the icon to view the demand for the previous 9 periods. Fill-in the table below for a single exponential smoothing forecast (enter your responses rounded to one decimal place). Period F(SES) 1 2 3 4 5 6 7 8 9 The forecast for period 10 is Period 1 115 117 182 23456789 133 107 96 177 176 (Enter your response rounded to one decimal place.) 144 U at 115 117 182 133 107 96 177 176 144 forecast for period 10. (Note: a, is
Use the cubic regression model above to answer this question. Predict instantaneous rate of change of the percentage of female high school graduates in 2034. Round to the nearest hundredth (2 decimal places). % per year NOTE: Your answer may be negative.
Please please solve all parts of the question. Thank you so much.
Describe the patterns in quantity sold and own and rival prices during this time period using basic descriptive statistics. Graphs are welcome as well. Take the logs of the variables, and estimate the demand function. Interpret the R-square. Interpret the coefficients for logP and logPsub Interpret the p-values associated with each independent variable Are consumers price sensitive? Why or why not? (be as precise as you can – you have estimates!). Does this price sensitivity make sense given the good we are examining? How sensitive are our consumers to changes in the rival good’s price? Explain in detail. Suppose we decide to charge a per ounce price of $2, while at the same time our rival charges a price of $2.15. All else equal, what would you expect sales to be? How confident are you in your forecast? Suppose we are charging a price of $2 and our current marginal cost is $1.50 Are we maximizing profits at this price? If not, should we raise or lower price? Why?
A principal amount of $375 is placed in a savings account that earns 8% annual simple interest. Which graph best models the growth of the account balance.
Month Sales Forecast January 1000 1043 February 3000 March 5000 April 4000 May 5000 June 6000 Compute the forecast for the month of July using simple exponential smoothing. Take alpha = 0.2
If actual period demand varies from the forecast by more than 3MAD, this indicates: there is 3% chance that the forecast is wrong. there is a 98% chance that the forecast is wrong. there is a 97% chance that the forecast is wrong. there is a 3% chance that the forecast is correct Moving to the next question prevents changes to this answer:
Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.
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

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The following are historical demand data: ACTUAL YEAR SEASON DEMAND 2011 Spring 204 Summer 148 Fall 380 Winter 565 2012 Spring 482 Summer 265 Fall 680 Winter 966