Reference no: EM13825887

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?