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?