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?
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