Exam 12: Time Series Analysis and Forecasting

We compare the percent of variation explained R² for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.

(True/False)

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Question 61

Rite Aid pharmacy in Big Rapids, Michigan is using simple exponential smoothing to predict monthly birthday card sales. At the end of October 2015, the pharmacy's forecast for December 2015 sales was 400. In November, 420 cards were sold, and during December, 425 cards were sold. At the end of December 2015, what is the pharmacy's forecast for the total number of cards that will be sold during March and April of 2016? Use .

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Question 62

Which summary measure for forecast errors does not depend on the units of the forecast variable?

(Multiple Choice)

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Question 63

A meandering pattern is an example of a random time series.

(True/False)

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Question 64

You will always get more accurate forecasts by using more complex forecasting methods.

(True/False)

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Question 65

Econometric models can also be called:

(Multiple Choice)

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Question 66

A moving average is the average of the observations in the past few periods, where the number of terms in the average is the span.

(True/False)

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Question 67

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

(True/False)

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Question 68

Econometric forecasting models, also called causal models, use regression to forecast a time series variable by using other explanatory time series variables.

(True/False)

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Question 69

The smoothing constants in exponential smoothing models are effectively a way to assign different weights to past levels, trends and cycles in the data.

(True/False)

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Question 70

What changes, if any, would you suggest to improve the forecast?

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Question 71

Every form of exponential smoothing model has at least one smoothing constant, which is always between 0 and 1.

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Question 72

In an additive seasonal model, we add an appropriate seasonal index to a "base" forecast. These indexes, one for each season, typically average to 0.

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Question 73

If we use a value close to 1 for the smoothing constant in a simple exponential smoothing model, then we expect the model to respond very slowly to changes in the level.

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Question 74

When using Holt's model, choosing values of the smoothing constant that are near 1 will result in forecast models that:

(Multiple Choice)

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Question 75

The time series component that reflects a long-term, relatively smooth pattern or direction exhibited by a time series over a long time period, is called seasonal.

(True/False)

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Question 76

Consider a random walk model with the following equation: , where is a normally distributed random series with mean of 0 and standard deviation of 12. -(A) Use Excel to generate a time series of 25 values using this random walk model with a starting value of 200. (B) Conduct a runs test on the series you generated for (A). Is it random? Explain. (C) Conduct a runs test on the differences between successive values for the series you generated for (A). Is it random? Explain. (D) Use the time series you constructed in (A) to forecast the next observation.

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Question 77

The data below represents sales for a particular product. If you were to use the moving average method with a span of 3 periods, what would be your forecast for period 5?

(Multiple Choice)

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Question 78

A time series is any variable that is measured over time in sequential order.

(True/False)

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Question 79

Suppose that a simple exponential smoothing model is used (with = 0.40) to forecast monthly sandwich sales at a local sandwich shop. The forecasted demand for September was 1560 and the actual demand was 1480 sandwiches. Given this information, what would be the forecast number of sandwiches for October?

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Question 80

Showing 61 - 80 of 106

We compare the percent of variation explained R² for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.

Which summary measure for forecast errors does not depend on the units of the forecast variable?

A meandering pattern is an example of a random time series.

You will always get more accurate forecasts by using more complex forecasting methods.

Econometric models can also be called:

A moving average is the average of the observations in the past few periods, where the number of terms in the average is the span.

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

Econometric forecasting models, also called causal models, use regression to forecast a time series variable by using other explanatory time series variables.

The smoothing constants in exponential smoothing models are effectively a way to assign different weights to past levels, trends and cycles in the data.

What changes, if any, would you suggest to improve the forecast?

Every form of exponential smoothing model has at least one smoothing constant, which is always between 0 and 1.

In an additive seasonal model, we add an appropriate seasonal index to a "base" forecast. These indexes, one for each season, typically average to 0.

If we use a value close to 1 for the smoothing constant in a simple exponential smoothing model, then we expect the model to respond very slowly to changes in the level.

When using Holt's model, choosing values of the smoothing constant that are near 1 will result in forecast models that:

The time series component that reflects a long-term, relatively smooth pattern or direction exhibited by a time series over a long time period, is called seasonal.

The data below represents sales for a particular product. If you were to use the moving average method with a span of 3 periods, what would be your forecast for period 5?

A time series is any variable that is measured over time in sequential order.

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 12: Time Series Analysis and Forecasting

We compare the percent of variation explained R2 for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.

Which summary measure for forecast errors does not depend on the units of the forecast variable?

A meandering pattern is an example of a random time series.

You will always get more accurate forecasts by using more complex forecasting methods.

Econometric models can also be called:

A moving average is the average of the observations in the past few periods, where the number of terms in the average is the span.

An autocorrelation is a type of correlation used to measure whether the values of a time series are related to their own past values.

Econometric forecasting models, also called causal models, use regression to forecast a time series variable by using other explanatory time series variables.

The smoothing constants in exponential smoothing models are effectively a way to assign different weights to past levels, trends and cycles in the data.

What changes, if any, would you suggest to improve the forecast?

Every form of exponential smoothing model has at least one smoothing constant, which is always between 0 and 1.

In an additive seasonal model, we add an appropriate seasonal index to a "base" forecast. These indexes, one for each season, typically average to 0.

If we use a value close to 1 for the smoothing constant in a simple exponential smoothing model, then we expect the model to respond very slowly to changes in the level.

When using Holt's model, choosing values of the smoothing constant that are near 1 will result in forecast models that:

The time series component that reflects a long-term, relatively smooth pattern or direction exhibited by a time series over a long time period, is called seasonal.

The data below represents sales for a particular product. If you were to use the moving average method with a span of 3 periods, what would be your forecast for period 5?

A time series is any variable that is measured over time in sequential order.

Introduction to Business Analytics

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, Binomial, Poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Introduction to Optimization Modeling

Optimization Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

We compare the percent of variation explained R² for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.