# Review Test Submission: Quiz4 Course QMBLC Summer14 Test Quiz4 • Question 1 Shown below is a portion of an Excel output for regression analysis relating Y (dependent variable) and X

Review Test Submission: Quiz4 Course QMBLC Summer14 Test Quiz4 • Question 1 Shown below is a portion of an Excel output for regression analysis relating Y (dependent variable) and X (independent variable). The percent of the variability in the prediction of Y that can be attributed to the variable X Regression Statistics Multiple R 0.7732 R Square 0.5978 Adjusted R Square 0.5476 Standard Error 3.0414 Observations 10 ANOVA df SS MS F Significance F Regression 1 110 110 11.892 0.009 Residual 8 74 9.25 Total 9 184 Coefficients Standard Error t Stat P-value Intercept 39.222 5.942 6.600 0.000 X -0.556 0.161 -3.448 0.009 • Question 2 Shown below is a portion of an Excel output for regression analysis relating Y (dependent variable) and X (independent variable). Is this model significant at the 0.05 level? Regression Statistics Multiple R 0.1347 R Square 0.0181 Adjusted R Square -0.0574 Standard Error 3.384 Observations 15 ANOVA df SS MS F Significance F Regression 1 2.750 2.75 0.2402 0.6322 Residual 13 148.850 11.45 Total 14 151.600 Coefficients Standard Error t Stat p-value Intercept 8.6 2.2197 3.8744 0.0019 X 0.25 0.5101 0.4901 0.6322 • Question 3 A regression analysis between sales and price resulted in the following equation Y=50,000 – 8000X The above equation implies that an • Question 4 The actual demand for a product and the forecast for the product are shown below. Calculate the MAD. Observation Actual Demand (A) Forecast (F) 1 35 — 2 30 35 3 26 30 4 34 26 5 28 34 6 38 28 • Question 5 Below you are given the first two values of a time series. You are also given the first two values of the exponential smoothing forecast. Time Period (t) Time Series Value (Y t) Exponential Smoothing Forecast (F t) 1 22 22 2 26 22 If the smoothing constant equals .3, then the exponential smoothing forecast for time period three is • Question 6 is the forecast for June based on a three-month weighted moving average applied to the following past demand data and using the weights: .5, .3, and .2 (largest weight is for the most recent data)? Month Demand Forecast January 40 February 45 March 57 April 60 May 75 June 87 • Question 7 The following time series shows the number of units of a particular product sold over the past six months. Compute the MSE for the 3-month moving average. Month Units Sold (Thousands) 1 8 2 3 3 4 4 5 5 12 6 10 • Question 8 Given an actual demand of 61, forecast of 58, and an alpha factor of .2, what would the forecast for the next period be using simple exponential smoothing?