Question 1

Which of the following forecasting techniques is solely judgment-based?
A) Correlation
B) Econometric
C) Regression
D) Delphi
E) Market testing

Accepted Answer

The Delphi method is solely judgment-based, relying on the expertise and insights of a panel of experts to make forecasts. The other options involve statistical or empirical analysis.

Question 2

Which forecasting method requires HIGHER financial resources?
A) Naive extrapolation
B) Moving average
C) Market survey
D) Exponential smoothing
E) Extrapolation

Accepted Answer

Market survey methods typically require higher financial resources because they involve collecting primary data directly from potential customers through surveys, interviews, or focus groups, which can be costly and time-consuming.

Question 3

Identify the forecasting technique that requires HIGHER levels of quantitative skills.
A) Econometric
B) Moving average
C) Exponential smoothing
D) Sales force
E) Delphi

Accepted Answer

Econometric forecasting techniques require higher levels of quantitative skills because they involve the use of statistical methods and mathematical models to predict future trends based on historical data. This approach often requires a deep understanding of both the economic theory and the statistical tools used to analyze data.

Question 4

Consumers are MORE heavily involved in which of the following forecasting techniques?
A) Market testing
B) Sales force composite method
C) Jury of expert opinion approach
D) Delphi method
E) Naive extrapolation

Accepted Answer

Market testing involves direct input from consumers as they are exposed to the product in a real or simulated market environment, allowing for their reactions and behaviors to be observed and analyzed. This technique relies heavily on consumer involvement, unlike the other methods listed, which primarily depend on expert opinions, sales force estimates, or statistical models.

Question 5

This is a specific form of primary market research in which potential customers are asked to give some indication of their likelihood of purchasing a product.
A) Market testing
B) Rollout
C) Market survey
D) Naive extrapolation
E) Exponential smoothing

Accepted Answer

Market survey is the correct answer because it involves gathering information directly from potential customers about their preferences and likelihood of purchasing a product, which is a form of primary market research.

Question 6

Random variation occurring in extrapolation methods may be "smoothed out," to make the patterns more apparent, by using
A) Chi-square
B) Regression analysis
C) Data points
D) Moving averages
E) Base values

Accepted Answer

Moving averages are used to smooth out short-term fluctuations and highlight longer-term trends or cycles in data, making patterns more apparent, which is particularly useful in time series analysis and forecasting.

Question 7

This is a useful statistic produced by regression analysis and is a measure of fit that is the percentage variance in the dependent variable explained by the independent variable. A) X² B) Mean C) Standard deviation D) SE² E) R²

Accepted Answer

The correct answer is E) R^2. R^2, or the coefficient of determination, is a statistical measure in regression analysis that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables.

Question 8

All of the following forecasting methods are model-based and often termed causal because the techniques utilize one or more variables other than time to predict sales, EXCEPT
A) Regression
B) Delphi method
C) Leading indicators
D) Econometric model
E) Multiple-equation regression

Accepted Answer

The Delphi method is not model-based or causal; it relies on the consensus of expert opinions rather than using variables to predict sales.

Question 9

Changes in the macroeconomic variables, that occur before changes in the economy, are often used by economists for forecasting and are known as
A) R variables
B) Leading indicators
C) Lagging variables
D) Prime variables
E) Dependent variables

Accepted Answer

Leading indicators are variables that change before the economy as a whole changes, making them useful for forecasting future economic conditions.

Question 10

Which of the following methods is MORE heavily used for short / medium-term forecasting?
A) Judgmental approach
B) Straight-line projection
C) Simulation
D) Classical decomposition
E) Box-Jenkins

Accepted Answer

The answer of Which of the following methods is MORE...

Question 11

The most frequently used quantitative method for forecasting is a(n)
A) Econometric model
B) Delphi model
C) Moving average
D) Executive jury panel
E) Sales force panel

Accepted Answer

Moving averages are commonly used for forecasting in various fields due to their simplicity and effectiveness in smoothing out short-term fluctuations to identify longer-term trends or cycles.

Question 12

Which of the following quantitative approaches is used MORE frequently, particularly for long-run forecasts?
A) Regression
B) Straight-line projection
C) Simulation
D) Classical decomposition
E) Box-Jenkins

Accepted Answer

Regression is often used for long-run forecasts as it can model relationships between variables over time, making it suitable for long-term predictions.

Question 13

Research indicates that the most commonly used new product forecasting technique is
A) Customer / market research
B) Diffusion model
C) Precursor curve
D) Box-Jenkins technique
E) Neural network

Accepted Answer

Customer/market research is the most commonly used technique for new product forecasting because it directly involves potential users' feedback and preferences, providing valuable insights into market demand and acceptance.

Question 14

Identify the INCORRECT statement pertaining to regression model.
A) In its first stage, the variables assumed to affect dependent variables are specified
B) In its second stage, a model is specified indicating the form of the relation between the independent variables and sales
C) The nature of the relationship between the independent variables and sales is rarely linear
D) The model is estimated by means of regression analysis, using commonly available computer programs
E) The model can be used to forecast sales

Accepted Answer

The statement "The nature of the relationship between the independent variables and sales is rarely linear" is incorrect because in many applications of regression analysis, a linear relationship is often assumed for simplicity and ease of interpretation, although in reality, relationships can be more complex. Linear regression models, which assume a linear relationship between the independent and dependent variables, are very common and widely used in various fields for forecasting and analysis.

Question 15

In using a regression forecast model, while some of the variables may be under the firm's control. several others need to be forecasted. Here modelers often use _____ as they are themselves easy to forecast.
A) External predictor variables
B) Surged predictor variables
C) Leading predictor variables
D) Forward predictor variables
E) Lagged predictor variables

Accepted Answer

Lagged predictor variables are easy to forecast because they are based on historical data.

Question 16

Identify the final step in the data analysis stage of the regression model-building process.
A) Examine the correlations among the independent variables
B) Run the regression
C) Determine the significant predictors of the dependent variable
D) Develop the forecast and confidence interval
E) Check the signs of the significant independent variables

Accepted Answer

The final step in the data analysis stage of the regression model-building process involves developing the forecast and confidence interval, after determining which predictors are significant and running the regression. This step is crucial for interpreting the model's predictions and understanding the uncertainty around these predictions.

Question 17

The strength of the effect of an independent variable on sales in regression models is measured by A) Chi-square B) The t statistic C) R² D) X² E) X

Accepted Answer

The t statistic is used in regression analysis to determine the significance of each independent variable in the model. It measures how many standard deviations the coefficient is away from zero, indicating the strength and significance of the effect of the independent variable on the dependent variable (in this case, sales).

Question 18

This is important in forecasting models, because fewer independent variables must be predicted to develop the ultimate forecast for a "smaller" model.
A) Estimation
B) Smoothing
C) Parsimony
D) Volume
E) Preference

Accepted Answer

Parsimony in forecasting models refers to the principle of using the smallest number of independent variables necessary to achieve an accurate forecast. A parsimonious model is simpler and requires fewer variables to be predicted, making the forecasting process more straightforward and potentially more accurate.

Question 19

The omission of one or more key variables from a regression model will produce a(n) _____ error.
A) Identification
B) Specification
C) Production
D) Timing
E) Smoothing

Accepted Answer

The omission of one or more key variables from a regression model leads to a specification error. This occurs because the model incorrectly specifies the relationship between the variables, potentially leading to biased and inconsistent estimates.

Question 20

A _____ forecast is typically the best guess minus two standard errors of estimate.
A) Optimistic
B) Best-guess
C) Pessimistic
D) Point
E) Sanguine

Accepted Answer

A pessimistic forecast takes into account the potential for outcomes to be worse than the best guess. Subtracting two standard errors of estimate from the best guess provides a more conservative estimate, aligning with the concept of a pessimistic forecast.

Quiz 6: Market Potential and Sales Forecasting

Which of the following forecasting techniques is solely judgment-based?

Which forecasting method requires HIGHER financial resources?

Identify the forecasting technique that requires HIGHER levels of quantitative skills.

Consumers are MORE heavily involved in which of the following forecasting techniques?

This is a specific form of primary market research in which potential customers are asked to give some indication of their likelihood of purchasing a product.

Random variation occurring in extrapolation methods may be "smoothed out," to make the patterns more apparent, by using

This is a useful statistic produced by regression analysis and is a measure of fit that is the percentage variance in the dependent variable explained by the independent variable.

All of the following forecasting methods are model-based and often termed causal because the techniques utilize one or more variables other than time to predict sales, EXCEPT

Changes in the macroeconomic variables, that occur before changes in the economy, are often used by economists for forecasting and are known as

Which of the following methods is MORE heavily used for short / medium-term forecasting?

The most frequently used quantitative method for forecasting is a(n)

Which of the following quantitative approaches is used MORE frequently, particularly for long-run forecasts?

Research indicates that the most commonly used new product forecasting technique is

Identify the INCORRECT statement pertaining to regression model.

In using a regression forecast model, while some of the variables may be under the firm's control. several others need to be forecasted. Here modelers often use _____ as they are themselves easy to forecast.

Identify the final step in the data analysis stage of the regression model-building process.

The strength of the effect of an independent variable on sales in regression models is measured by

This is important in forecasting models, because fewer independent variables must be predicted to develop the ultimate forecast for a "smaller" model.

The omission of one or more key variables from a regression model will produce a(n) _____ error.

A _____ forecast is typically the best guess minus two standard errors of estimate.