Question 1

Which of the following is a drawback of mean absolute deviation (MAD)?

Accepted Answer

Mean Absolute Deviation (MAD) does not provide information about the direction of errors (forecasting bias), as it only measures the average magnitude of errors without considering their direction (positive or negative).

Question 2

Which of the following variations cannot be predicted and can be identified only after occurrence?

Accepted Answer

Random variations are inherently unpredictable and their occurrence can only be identified after the fact. This is because they do not follow any discernible pattern or trend and can occur at any point in time without warning.

Question 3

Which of the following statements is true about the error sum of squares (SSE)?

Accepted Answer

The error sum of squares (SSE) measures the variation not explained by the regression but resulting from other factors or variables. It represents the difference between the actual Y values and the predicted Y values obtained from the regression equation.

Question 4

The tendency to consistently produce a particular type of forecast (high or low)that isn't accurate is called a(n)______.

Accepted Answer

Bias refers to a consistent tendency to produce a particular type of forecast error. This means that the forecasts consistently overestimate or underestimate the actual values. Options A and B refer to specific instances of forecasting errors, while option D is too general and not specific to the concept of bias.

Question 5

The correlation coefficient takes on a value between ______.

Accepted Answer

The correlation coefficient represents the degree of linear relationship between two variables, and can take on values between -1 and +1. A correlation of +1 indicates a perfect positive linear relationship, while a correlation of -1 indicates a perfect negative linear relationship. A correlation of 0 indicates no linear relationship between the variables. Therefore, the best choice is option B, which indicates that the correlation coefficient can range from -1 to +1.

Question 6

Mean absolute percentage error (MAPE)is the ______.

Accepted Answer

MAPE calculates the average of the absolute percentage error between actual and forecasted demand values. The other choices involve either squared versions or simple differences, which do not give an accurate representation of the forecasting error in terms of percentage.

Question 7

Which of the following statements is true about the coefficient of determination (R²)?

Accepted Answer

The coefficient of determination (R²) measures the proportion of the variation in the dependent variable that is explained by the independent variable(s). The higher the value of R² (closer to 1), the stronger the relationship between the variables, and the more confidence we have that the estimate is accurate. Therefore, option A is the correct choice. Option B is incorrect because the lower the value of R², the weaker the relationship between the variables, and the less confidence we have in our estimate. Option C is incorrect because R² indicates the quality of the estimate. Option D is incorrect because an R² of 0 indicates that the independent variable(s) do not explain any of the variation in the dependent variable.

Question 8

The ______ measures the strength of the relationship between the independent and dependent variable in regression analysis.

Accepted Answer

The correlation coefficient measures the strength and direction of the linear relationship between two variables, in this case the independent and dependent variable in regression analysis. The other choices are important in regression analysis as well but do not specifically measure the strength of the relationship between the variables.

Question 9

"The demand for a product is a function of both the price of the product and the dollars spent on promotions by the company that produced it." Which of the following methods is appropriate to forecast demand for the product?

Accepted Answer

Since the demand for the product is affected by both the price of the product and the dollars spent on promotions, a multiple linear regression analysis would be appropriate to forecast the demand for the product. This method allows for the consideration of multiple variables in predicting the outcome.

Question 10

Which of following approaches should forecasters follow while choosing the best forecasting method?

Accepted Answer

Selecting the method that results in the fewest forecasting errors is a fundamental approach in forecasting. This approach aims to improve accuracy and reliability of forecasts by minimizing errors, which is crucial for effective decision-making.

Question 11

Which of the following statements is true about the forecasting error measures?

Accepted Answer

Cumulative sum error (CSE) is a measure that tracks the forecasting bias or whether errors are consistently positive or negative. Mean absolute deviation (MAD) is a simpler and more straightforward measure that calculates the average absolute deviation between forecasted values and actual values. MAD does not provide a percentage of error. Mean squared errors (MSE) is a measure that calculates the average of the squared differences between forecasted values and actual values. However, MSE can track the accuracy of the forecast.

Question 12

The coefficient of determination has a value between ______.

Accepted Answer

The coefficient of determination (R-squared) is a measure of how well a regression model fits the data, and its value ranges from 0 to 1, where 0 indicates no correlation between the predictor and response variables, and 1 indicates a perfect fit. Therefore, the best choice is A. The other options (-1 to +1 and -∞ to +∞) refer to different measures of correlation, but not specifically to the R-squared value. Similarly, the range of -1 to 0 is not valid for R-squared, as it implies a negative correlation between the variables.

Question 13

The ______ is the average of the sum of the squared differences between the actual and the forecasted demand values.

Accepted Answer

The mean squared error (MSE) measures the average of the squared differences between the actual and forecasted demand values. It is a commonly used measure of forecasting accuracy. MAD and MAPE are alternative measures of forecasting accuracy, but they do not involve squaring the differences between actual and forecasted values. CSE is not a commonly used measure of forecasting accuracy.

Question 14

The ______ measures the random variation,which is the variation of the actual (observed)y values from the predicted y values (ŷᵢ).

Accepted Answer

The standard error of the estimate measures the random variation or the scatter of the actual y values around the predicted y values. The error sum of squares (SSE) measures the total variation, not just the random variation. The correlation coefficient and coefficient of determination measure the strength and direction of the linear relationship between two variables.

Question 15

Which of the following is NOT a way to measure forecasting errors?

Accepted Answer

Error sum of squares (SSE) is not a way to measure forecasting errors. The other options - mean squared error (MSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE) - are all commonly used methods to measure forecasting errors.

Question 16

The regression sum of squares (SSR)is the ______.

Accepted Answer

The regression sum of squares (SSR) measures the amount of variation in the dependent variable that is explained by the independent variables in the model, hence it is referred to as the explained variation.

Question 17

______ is the average of the sum of the absolute differences between the actual and forecasted demand values.

Accepted Answer

The described measurement is known as the Mean absolute deviation (MAD), which calculates the average deviation between actual and forecasted values independent of sign. This is different from the Mean squared error (MSE) and the Mean absolute percentage error (MAPE), which consider the square or percentage differences between actual and forecasted values, respectively. The Cumulative sum error (CSE) is not a commonly used measure of forecast error.

Question 18

Which of following assumptions related to the validity of the linear regression model is false?

Accepted Answer

Normally, the error terms in a linear regression model are assumed to be Normally distributed.

Question 19

A large positive value of cumulative sum error (CSE)implies ______.

Accepted Answer

A large positive value of cumulative sum error (CSE) indicates that the forecasted values are consistently lower than the actual demand, meaning the forecast is understating the actual demand.

Question 20

"Cancellation of the positive and negative forecast errors can distort the information." This is a drawback of ______.

Accepted Answer

Cumulative sum error (CSE) can be distorted by the cancellation of positive and negative forecast errors, as it accumulates the errors over time without considering their direction.

Quiz 16: Demand Forecasting Methods

Which of the following is a drawback of mean absolute deviation (MAD) ?

Which of the following variations cannot be predicted and can be identified only after occurrence?

Which of the following statements is true about the error sum of squares (SSE) ?

The tendency to consistently produce a particular type of forecast (high or low) that isn't accurate is called a(n) ______.

The correlation coefficient takes on a value between ______.

Mean absolute percentage error (MAPE) is the ______.

Which of the following statements is true about the coefficient of determination (R²) ?