Use the data below to solve for the following:2. Naïve method3. Unweighted 3 month moving average4. Weighted Moving Average (3 months, with the following weights: 0.45, 0.35, 0.20)5. Simple Exponential Smoothing with the following parameters:Ft+1=32 and the α = .156. Time Series RegressionMonths123456789101112Firm's Actual Market share3032333428332927323328337. Which of the following forecasting method will you use?8. What are your basis in choosing for your forecasting techniques? Explain briefly.ONLY DO56

Time series are used to forecast the variable based on the time. There are four components in the…

Answered: 5. Simple Exponential Smoothing with…

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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Suppose that you are a real estate agent in Kyrenia and you need forecasts for monthly demand for apartments in the months of 2021. The past actual monthly sales data for apartments in Kyrenia are given below. Use Time Series Regression Model to obtain forecasts for monthly demand for apartments in August of 2021. Month February March April May June Sales 30 32 32 48 70 Select one: a.42.4 b.71.2 c.80.8 d.61.6
Question 49 Regression analysis was applied and a significant relationship between product demand (Y) and the product price (X) was found. The following estimated regression equation was obtained. (Yhat ) = 120 - 10 X Based on the above estimated regression equation, if price is increased by 10 units, then demand is expected to o decrease by 10 units O decrease by 100 units O increase by 100 units O incease by 10 units O none of these * Previous Quiz saved at 10:26am Sub o ooC
Which of the following is/are true for logistic regression model? I. The independent variable must be continuous. II. The dependent variable can only have 2 levels/categories. I only II only…
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The following table gives the regression results of consumption behaviour between female and male. Consumption (C), in RM Model 1 Model 2 Constant 0.7515 0.6587 (0.024) (0.037) Y 0.8603 0.5442 (0.0301) (0.2881) Gen 0.2001 (0.0004) Y*Gen 0.3105 (0.0076) Adjusted R 0.78 0.78 No. of Observations 47 47 Note: Y=income (RM) Gen= Gender ( It is equal to 1 if person is female; 0 is for male) Figures in the parentheses are p- values a) Identify which are the qualitative, quantitative and interaction variable. Refer to Model 1: b) Write the regression equations for female and male. Draw the regression lines for female and male on the same diagram. What can you c) say? d) Interpret all the estimated slope coefficients. e) Conduct an appropriate test to determine whether variable gender is statistically significant at 5%. Should this variable be dropped from this regression model.
Suppose seafood price and quantity data for the years 2000 and 2009 follow. Use 2000 as the base period. 2000 2009 Seafood 2000 Qty. (lb) Price ($/lb) Price ($/lb) Halibut 75,190 2.01 2.43 Lobster 83,180 3.72 3.09 Tuna 50,779 1.87 1.97 (a) Compute a price relative for each type of seafood. (Round your answers to one decimal place.) Seafood Price Relative Halibut Lobster Tuna L (b) decimal place.) pute a weighted aggregate price index for the seafood catch. (Round your answer to 2009 = Comment on the change in seafood prices over the nine-year period. (Enter your percentage as a positive value. Round your answer to one decimal place.) Seafood prices have increased by % over the 9-year period according to the index.
2
Q10. price based on two features. Suppose we are going to apply multivariate Linear Regression to predict the house x1 = age of the house (0 – 40) x2 = quality of neighbouring schools (0 – 3) h(8) = 0, + 8,x1 + 02x2 Suggest one way to scale the features. Describe what will happen if feature scaling is not performed before the training process is conducted.

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5. Simple Exponential Smoothing with the following parameters: Ft+1=32 and the a = .15 6. Time Series Regression Months 1 2 3 4 5 6 7 8 9 10 11 12 Firm's Actual Market share 30 32 33 34 28 33 29 27 32 33 28 33