Question 1

Suppose that you are using the simple mean to make a forecast. This period's forecast was equal to 100 units, and it was based on 6 periods of demand. This period's actual demand was 86 units. What is your forecast for next period?
A) 98
B) 100
C) 93
D) 86
E) Not enough information is given to answer the question.

Accepted Answer

100 + 100 + 100 + 100 + 100 + 86 = 586/6 = 98

Question 2

What are the most frequently used forecasting techniques?
A) Linear regression
B) Simple mean
C) Exponential smoothing
D) Weighted moving average
E) Econometric models

Accepted Answer

The answer of What are the most frequently used forecasting...

Question 3

What value of the correlation coefficient implies that there is no relationship between the two variables of a linear regression model?
A) -1
B) 0
C) 0.5
D) 1
E) $\infty$

Accepted Answer

The correlation coefficient ranges from -1 to 1, with 0 indicating no linear relationship between the two variables.

Question 4

Which of the following values of the correlation coefficient implies that the value of the dependent variable decreases as the value of the independent variable increases?
A) -0.2
B) 0
C) 0.2
D) 1
E) 0.5

Accepted Answer

A negative correlation coefficient implies that the value of the dependent variable decreases as the value of the independent variable increases. A value of -1 indicates a perfect negative correlation, while a value close to -1 indicates a strong negative correlation. Therefore, choice A, with a correlation coefficient of -0.2, is the best choice that implies a negative relationship between the two variables.

Question 5

What is the statistic that measures the direction and strength of the linear relationship between two variables? A) r² B) Coefficient of variation C) Variance D) Coefficient of kurtosis E) Correlation coefficient

Accepted Answer

The correlation coefficient is a statistical measure that measures the direction and strength of the linear relationship between two variables. It is denoted by "r" and its value ranges from -1 to +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation.

Question 6

Which forecasting method assumes that next period's forecast is equal to this period's actual value?
A) Simple mean
B) Ignorant
C) Basic
D) Naïve
E) Nescient

Accepted Answer

The Naïve forecasting method assumes that next period's forecast is equal to this period's actual value. It is a very simple and common method for time series forecasting.

Question 7

What value of the correlation coefficient implies that there is a perfect positive linear relationship between the two variables of a linear regression model?
A) -1
B) 0
C) 0.5
D) 1
E) $\infty$

Accepted Answer

A correlation coefficient of 1 indicates a perfect positive linear relationship between the two variables in a linear regression model.

Question 8

Suppose that you are using the four-period weighted moving average forecasting method to forecast sales and you know that sales will be increasing every period for the foreseeable future. What of the following would be the best set of weights to use (listed in order from the most recent period to four periods ago, respectively)?
A) 0.25, 0.25, 0.25, 0.25
B) 0.40, 0.30, 0.20, 0.10
C) 1.00, 0.00, 0.00, 0.00
D) 0.10, 0.20, 0.30, 0.40
E) 0.00, 0.00, 0.00, 1.00

Accepted Answer

The answer of Suppose that you are using the four-period...

Question 9

What does the linear regression line do?
A) Minimizes sum of errors
B) Minimizes product of squared errors
C) Minimizes sum of squared errors
D) Minimizes product of errors
E) Minimizes sum of absolute value of errors

Accepted Answer

The linear regression line minimizes the sum of squared errors, which means the distance between the actual data points and the predicted values is minimized by finding the line that produces the smallest sum of the squared distances. This is the most commonly used method for finding the best fit line in linear regression.

Question 10

Suppose that you are interested in trend-adjusted exponential smoothing. Which of the following values of the trend smoothing constant, $\beta$, would most likely be seen in practice?
A) 0.10
B) 0.50
C) 0.75
D) 0.90
E) 1.00

Accepted Answer

Trend-adjusted exponential smoothing typically uses a smoothing constant (β) less than 1, often between 0.1 and 0.3, to ensure that the model does not overreact to recent trends. A value of 0.10 is within this common range, making it a likely choice in practice.

Question 11

In linear regression, an r² of .984 implies what? A) 98.4% of the variability of the independent variable is explained by the dependent variable B) 98.4% of the variability of the dependent variable is explained by the independent variable C) 1.6% of the variability of the independent variable is explained by the dependent variable D) 1.6% of the variability of the dependent variable is explained by the independent variable E) 99.2% of the variability of the dependent variable is explained by the independent variable

Accepted Answer

A correlation coefficient (r) of .984 indicates a strong positive relationship between the independent variable and dependent variable. However, since it is not an r-squared value, it cannot indicate the percentage of variability explained by either variable alone. An r-squared value would be necessary for this interpretation. Therefore, choice B is correct because it is the only option that correctly interprets the given correlation coefficient without making any assumptions about the percentage of variability explained.

Question 12

In linear regression, what are we trying to forecast?
A) Beta parameter
B) Dependent variable
C) Independent variable
D) Y-intercept of the line
E) Slope of the line

Accepted Answer

In linear regression, we are trying to forecast the value of the dependent variable based on the values of the independent variables. The dependent variable is the variable we are trying to predict or explain, while the independent variables are the variables used to predict or explain the dependent variable. Therefore, the best choice is B, the dependent variable.

Question 13

Which of the following is not considered to be one of the four basic patterns of time series data?
A) Horizontal
B) Trend
C) Vertical
D) Seasonality
E) Cycle

Accepted Answer

The four basic patterns of time series data are trend, seasonality, cycle, and irregularity. "Vertical" is not a recognized pattern.

Question 14

Suppose that you are using the four-period simple moving average method to forecast sales, and sales have been decreasing by 10% every period. How will your forecasts perform?
A) Forecasts will be lower than actual.
B) Forecasts will be higher than actual.
C) Forecasts will equal actual.
D) Forecasts will be increasing.
E) Forecasts will be decreasing by 2.5% every period.

Accepted Answer

Forecasts will be higher than actual because a simple moving average method lags behind in a consistently decreasing trend, causing the forecasted values to be higher than the actual decreasing values.

Question 15

Which is typically the most difficult data pattern to predict?
A) Horizontal
B) Trend
C) Level
D) Seasonality
E) Cycle

Accepted Answer

Cycles are typically the most difficult data pattern to predict because they involve irregular fluctuations that do not follow a fixed schedule, unlike seasonality or trends, making them unpredictable and challenging to model accurately.

Question 16

Suppose that you are using the naïve forecasting method with trend to forecast sales. If sales have been declining by 20% per week, and this week's sales amounted to $200, what would your forecast be for next week?
A) $200
B) $ 40
C) $240 
D) $180
E) $160

Accepted Answer

200 x .20 = 40; 200 - 40 = 160

Question 17

The OM supervisor informs you, the researcher, that the data has a large standard deviation. What data pattern would you expect to observe once you generated a time series trend?
A) horizontal
B) seasonal
C) positive/negative trend
D) cycle
E) insufficient information to derive a valid response

Accepted Answer

A large standard deviation indicates that the data points are spread out over a wide range of values, but it does not provide specific information about the pattern of the data over time, such as whether it is horizontal, seasonal, shows a positive/negative trend, or cycles. Therefore, without additional information, it is not possible to predict the time series trend accurately.

Question 18

In exponential smoothing, what values can the smoothing constant, $\alpha$, have?
A) [-1, 1]
B) [1, $\infty$]
C) [0, $\infty$]
D) [0, 1]
E) [-$\infty$, $\infty$]

Accepted Answer

The smoothing constant, denoted by α, is a value between 0 and 1 that determines the weight given to the most recent observation when forecasting future values. Therefore, the possible values of α are [0, 1].

Question 19

In exponential smoothing, which of the following values for $\alpha$ would generate the most stable forecast?
A) 0.10
B) 0.25
C) 0.50
D) 0.75
E) 1.00

Accepted Answer

A lower value of alpha (such as 0.10) would generate a more stable forecast as it would place more weight on past data and less on recent observations. This smoothing approach would result in less volatility and a smoother forecast. Higher values of alpha (such as 0.75 or 1.00) would place more weight on recent observations and result in a more volatile forecast.

Question 20

Which of the following is true with respect to the correlation coefficient r? A) r² $\le$ r B) r²$\le$ | r | C) r² $\ge$ r D) r² $\ge$ | r | E) r² can never equal r

Accepted Answer

The absolute value of the correlation coefficient (|r|) is always between 0 and 1, so option B is true. Option A and C have incorrect inequality signs. Option D has the correct inequality, but it also includes absolute value signs, which is incorrect. Option E is false since the correlation coefficient can be equal to itself.

Quiz 8: Forecasting

Suppose that you are using the simple mean to make a forecast. This period's forecast was equal to 100 units, and it was based on 6 periods of demand. This period's actual demand was 86 units. What is your forecast for next period?

What are the most frequently used forecasting techniques?

What value of the correlation coefficient implies that there is no relationship between the two variables of a linear regression model?

Which of the following values of the correlation coefficient implies that the value of the dependent variable decreases as the value of the independent variable increases?

What is the statistic that measures the direction and strength of the linear relationship between two variables?

Which forecasting method assumes that next period's forecast is equal to this period's actual value?

What value of the correlation coefficient implies that there is a perfect positive linear relationship between the two variables of a linear regression model?

What does the linear regression line do?

Suppose that you are interested in trend-adjusted exponential smoothing. Which of the following values of the trend smoothing constant, $\beta$ , would most likely be seen in practice?

In linear regression, an r² of .984 implies what?

In linear regression, what are we trying to forecast?

Which of the following is not considered to be one of the four basic patterns of time series data?

Suppose that you are using the four-period simple moving average method to forecast sales, and sales have been decreasing by 10% every period. How will your forecasts perform?

Which is typically the most difficult data pattern to predict?

Suppose that you are using the naïve forecasting method with trend to forecast sales. If sales have been declining by 20% per week, and this week's sales amounted to $200, what would your forecast be for next week?

The OM supervisor informs you, the researcher, that the data has a large standard deviation. What data pattern would you expect to observe once you generated a time series trend?

In exponential smoothing, what values can the smoothing constant, $\alpha$ , have?

In exponential smoothing, which of the following values for $\alpha$ would generate the most stable forecast?

Which of the following is true with respect to the correlation coefficient r?