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Unformatted text preview: ctors are .9982, .9263,
1.139, .9365 for winter, spring, summer and fall respectively. Based on the following deseasonalized observations (dt) given below, a trend line was
estimated. The linear regression trend equation is: trt = 10.1 + 1.91 (t). Use the forecasting
equation
and calculate the forecasted demand for the fall quarter of 1998 and
summer quarter of 2000. 134. Two forecasting models were used to predict the future values of a time series. The
forecasts are shown below with the actual values: Calculate the mean absolute deviation (MAD) for Model 1. 11569 Chapter 01  An Introduction to Business Statistics 135. Two forecasting models were used to predict the future values of a time series. The
forecasts are shown below with the actual values: Calculate the mean squared deviation (MSD) for Model 1. 136. Two forecasting models were used to predict the future values of a time series. The
forecasts are shown below with the actual values: Calculate the mean absolute deviation (MAD) for Model 2. 11570 Chapter 01  An Introduction to Business Statistics 137. Two forecasting models were used to predict the future values of a time series. The
forecasts are shown below with the actual values. Calculate the mean squared deviation (MSD) for Model 2 138. Two forecasting models were used to predict the future values of a time series. The
forecasts are shown below with the actual values: Which model is the most accurate? Why? 11571 Chapter 01  An Introduction to Business Statistics 139. Listed below are the price of a pair of men's boots over a 50 year time period. Find the simple index numbers for the data with 1950 as the base year. 140. Using the price of the following food items, compute the aggregate index numbers for
the four type of cheeses. Let 1990 be the base year for this market basket of goods. 11572 Chapter 01  An Introduction to Business Statistics 141. The price and quantity of several food items are listed below for the years 1990 and
2000. Compute the Laspeyres index using 1990 as the base year. 142. The price and quantity of several food items are listed below for the years 1990 and
2000. Compute the Paasche index using 1990 as the base year. 11573 Chapter 01  An Introduction to Business Statistics 143. Given the following data Compute the total error (sum of the error terms) 144. Given the following data Compute the mean squared deviation (error) 11574 Chapter 01  An Introduction to Business Statistics 145. Given the following data Compute the mean absolute deviation. 146. Consider the regression equation and the data below: Compute the predicted value for sales for period 6 and 7. 11575 Chapter 01  An Introduction to Business Statistics 147. Consider the regression equation and the data below: Compute the residuals (error terms) for period 6 and 7. 148. The linear regression trend model was applied to a time series of sales data based on the
last 16 months of sales. The following partial computer output was obtained: Write the prediction equation. 11576 Chapter 01  An Introduction to Business Statistics 149. The linear regression trend model was applied to a time series of sales data based on the
last 16 months of sales. The following partial computer output was obtained: Test the significance of the time term at α = .05. State the critical t value and make your
decision using a twosided alternative. 150. The linear regression trend model was applied to a time series of sales data based on the
last 16 months of sales. The following partial computer output was obtained: What is the predicted value of y when t = 17? 11577 Chapter 01  An Introduction to Business Statistics Chapter 16 Time Series Forecasting Answer Key True / False Questions 1. A univariate time series model is used to predict future values of a time series based only
upon past values of a time series.
TRUE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Medium
Learning Objective: 1
Topic: Time series model 2. Trend refers to a longrun upward or downward movement of a time series over a period of
time.
TRUE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Medium
Learning Objective: 1
Topic: Time series model 11578 Chapter 01  An Introduction to Business Statistics 3. Exponential smoothing is a forecasting method that applies equal weights to the time series
observations.
FALSE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Medium
Learning Objective: 5
Topic: Exponential Smoothing 4. The smoothing constant is a number that determines how much weight it is attached to each
observation.
TRUE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Medium
Learning Objective: 5
Topic: Exponential Smoothing 11579 Chapter 01  An Introduction to Business Statistics 5. Simple exponential smoothing is an appropriate method for prediction purposes when there
is a significant trend present in a time series data.
FALSE AACSB: Reflective Thinking
Bloom's: Knowledge
Difficulty: Medium
Learning Objective: 5
Topic: Exponential Sm...
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 Winter '14

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