The following are historical demand data: YEAR 2 years ago last year SEASON Spring Summer Fall Winter Spring Summer Fall Winter ACTUAL DEMAND 203 144 382 565 Forecast for this summer's demand 471 271 686 955 Use regression analysis and seasonal indexes to forecast this summer's demand. Note: Do not round intermediate calculations. Round your answer to the nearest whole number.
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- You estimated the following regression. What value would you predict for Y, if X = 47? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 324 -------------+---------------------------------- F(1, 322) = 354.54 Model | 3686788 1 3686788 Prob > F = 0.0000 Residual | 3348384.74 322 10398.7104 R-squared = 0.5241 -------------+---------------------------------- Adj R-squared = 0.5226 Total | 7035172.74 323 21780.7206 Root MSE = 101.97 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 24.51522 1.301971 18.83 0.000 21.95378 27.07666 _cons | 98.70791 117.4919 0.84 0.401…A company wants to use regression analysis to forecast the demand for the next quarter.In such a regression model, demand would be the independent variable. True or false?a. Trueb. FalseConsider the following log-wage regression results for women (W) and men (M) where wages are predicted by schooling (S) and age (A). wW = 2.23 + 0.077Sw + 0.017Aw and wM = 2.33 + 0.0745SM + 0.026AM. Sample means for the variables by gender are: women average a logged wage of 3.90, 12.7 years of schooling, and 40.8 years-old; men average a logged wage of 4.53, 14.2 years of schooling, and 43.9 years-old. Decompose the raw difference in average logged wages using the Oaxaca-Blinder decomposition. Specifically, decompose the raw difference into the portion due to differences in schooling, differences in age, and the portion left unexplained, possibly due to gender discrimination.
- QUESTION 1 Which of the following is NOT a time-series model? a. Moving averages b. Exponential smoothing c. Linear regression d. Naïve approachGiven the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…How Regression models are used for Forecasting purpose?
- The diagram shows what happened to the consumption of lamb in the UK over the period 1974– 2015. How can we explain this dramatic fall in consumption? One way of exploring this issue is to make use of a regression model, which should help us to see which variables are relevant and how they are likely to affect 140 130 120 110 100 90 80 70 60 50 40 30 20 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 Note: Data from 2015 based on end of financial year. Source: Based on data in Family Food datasets UK consumption of lamb: 1974–2017 The following is an initial model fitted to annual data for the years 1974–2010. QL = 144.0 – 0.137PL – 0.034PB + 0.214PP – 0.00513Y + e (1) where: QL is the quantity of lamb sold in grams per person per week; PL is the ‘real’ price of lamb (in pence per kg, 2000 prices); PB is the ‘real’ price of beef (in pence per…We have estimated the impact of gross domestic product (GDP), energy consumption (ENERGY) and population (POP) on CO2 emiisions (CO2) in Cyprus. The results are as follows;Dependent Variable: CO2Method: Least SquaresDate: 04/20/17 Time: 09:46Sample: 1990 2013Included observations: 24 Write down the economic function for the above estimation by using the information obtained from above table b- Write down the economic model for the above estimation by using the information obtained from above table . c- Write down the econometric model for the above estimation by using the information obtained from above table d- Write down the least squares line by using the information obtained from above tablee- Explain what each coefficient of the least squares line indicatesConsider the following actual and forecast demandlevels for Big Mac hamburgers at a local McDonald’s restaurant:DAY ACTUAL DEMAND FORECAST DEMANDMonday 88 88Tuesday 72 88Wednesday 68 84Thursday 48 80FridayThe forecast for Monday was derived by observing Monday’sdemand level and setting Monday’s forecast level equal to this demand level. Subsequent forecasts were derived by using expo-nential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, what is the forecast for Big Macdemand for Friday?
- Hi this question may have been posted before do not know just looking for assistance and having difficulties, the question comes from my online discussion forum post (SCMG 305 Global demand management) Investigate the cause-and-effect relationships utilizing regression analysis, find one authoritative resource in the form of a U-tube video or Website that explains the use of regression analysis as a prediction model for forecasting. Try not to duplicate a resource already posted by another student. Insert a hyperlink for that resource so others may access it quickly. Finally, provide a quick short paragraph/summary explaining what you learned from the resource you provided strengthening your understanding of the use of regression analysis for forecasting.From a local town record, information is gathered about workers' wages and their educational achievements. You are interested in exploring a possible relationship between the rate of change in workers' wages and the number of education years completed. a. Write down a model (not an estimated equation) that implies percentage changes (or relative changes) in wage due to changes years of education. Indicate which parameter measures the relative change in worker's wageThe following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Jan Feb Mar AprMonth 1 2 3 4Number of Accidents 30 40 70 105 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): y = ? + ?x