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?
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?
