FAU QBM 360 - Quiz 4

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

 
 What 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?