Question

1. Using and exponential smoothing model, the forecast for next January sales is:

	Sales
January	100
February	200
March	150
April	400
May	300
June	200
July	250
August	350
September	400
October	350
November	400
December	500

a. 150.0

b. 477.3

c. 450.0

d. Not enough information is given to make a forecast

2. Apply regression to the data shown below. The slope of the line estimated using the regression model is:

	Sales
January	100
February	200
March	150
April	400
May	300
June	200
July	250
August	350
September	400
October	350
November	400
December	500

a. 50.0

b. 45.3

c. 27.3

d. 15.4

Answer 1

Q1) To find the exponential smoothing forecast, smoothing constant (alpha) value is also required

where F = The F A-The actual demand in the prior period exponentially smoothed forecast for period r The exponentially smoothed forecast made for the prior period t-1 t-1 α The desired response rate, or smoothing constant

In the question, alpha is not given. Therefore, there is no enough information to solve

Ans:d. not enough information is given to make a forecast

Q2)

Get & Transform Data Queries & Connections Data Types Sort & Filter Об Data Analysis Sales 100 200 150 400 300 200 250 350 400 350 400 500 1 Month 2 January 3 February 4March 5 April Period Analysis Tools OK Covariance Descriptive Statistics Exponential Smoothing F-Test Two-Sample for Variances Fourier Analysis Histogram Moving Average Random Number Generation Rank and Percentile Regression Cancel Help May 7 June 8 July 9 August 10 September 11 October 12 November 13 December 10 12

DataE AlEdit Links anced Regression Get & Transform Data Queries & Conn Input OK Об Input Y Range: Input X Range: Labels SCS2:SC$13 SBS2:SB$13 Constant is Zero Cancel Sales 100 200 150 400 300 200 250 350 400 350 400 500 1 Month 2 January 3 February 4March 5 April Period Help Confidence Level: 95 % Output options O Qutput Range: O New Worksheet Ply O New Workbook Residuals May 7 June 8 July 9 August 10 September 11 October 12 November 13 December Residuals Standardized Residuals Residual Plots Line Fit Plots 10 Normal Probability Normal Probability Plots 12

1 SUMMARY OUTPUT Regression Statistics 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error 8 Observations 0.815336 0.664773 0.63125 73.23685 12 10 ANOVA MS gnificance F 12 Regression 13 Residual 14 Total 1 106363.6 106363.6 19.83051 0.001229 10 53636.365363.636 11 160000 17 Intercept 18 X Variable 1 19 Coefficientandard Err t stat P-value Lower 95%Up er 95%ower 95.0 p er 95.0% 122.7273 45.07417 2.722785 0.021457 22.29577 223.1588 22.29577 223.1588 27.27273 6.124373 4.453146 0.001229 13.62677 40.91868 13.62677 40.91868 21

Slope is C. 27.3