Question 1

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 2

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 implies a perfect positive linear relationship between the two variables in a linear regression model. This means that as one variable increases, the other variable increases in a consistent and linear manner.

Question 3

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 4

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 smaller value of alpha will put more weight on the past data and less weight on the recent data. This will result in a smoother and more stable forecast. Therefore, the value of 0.10 would generate the most stable forecast among the given options.

Question 5

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 will always be between 0 and 1, inclusive. Therefore, |r| is always greater than or equal to 0 and less than or equal to 1. Choice B correctly reflects this relationship.

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

Naïve forecasting assumes that next period's forecast is equal to this period's actual value.

Question 7

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

The four-period simple moving average method will not be able to capture the decreasing trend in sales. Since the method takes an average of the past four periods, it will still include periods where sales were higher. Therefore, the forecasts will be higher than actual sales.

Question 8

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 horizontal (random fluctuations). "Vertical" is not a recognized term in time series analysis.

Question 9

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

A correlation coefficient of 0 indicates no linear relationship between the two variables in the linear regression model.

Question 10

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, also known as alpha (α), must be between 0 and 1. Therefore, the correct range of values for alpha is [0,1]. Choice D includes this correct range of values.

Question 11

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 12

The following sales figures show actual sales over the identified time period. What can be determined by comparing a simple mean forecast and a six month moving average forecast? December 4,000
January 5,000
February 4,000
March 4,500
April 5,500
May 5,000
A)moving average develops a smoother forecast
B)4.7, 5
C)4.7, 4
D)4, 4
E)4, 4.7

Accepted Answer

The answer of The following sales figures show actual sales...

Question 13

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

An r² of .984 indicates a strong positive correlation between the independent variable and the dependent variable. However, correlation does not imply causation, so we cannot say that the independent variable explains the variability of the dependent variable. Instead, we can say that 98.4% of the variability of the dependent variable is explained by the independent variable. Therefore, the correct answer is B.

Question 14

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 15

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 16

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 measures the direction and strength of the linear relationship between two variables. It ranges from -1 to 1, where -1 indicates a perfect negative correlation, 0 indicates no correlation, and 1 indicates a perfect positive correlation.

Question 17

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 18

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, meaning it finds the line that has the least amount of deviation between the actual data points and the predicted values on the line. This is why the sum of squared errors is minimized.

Question 19

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 dependent variable based on the values of the independent variable(s). We are essentially trying to find a line of best fit that will allow us to make accurate predictions of the dependent variable given certain values of the independent variable(s). Therefore, the answer is B, the dependent variable.

Question 20

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

A trend smoothing constant of 0.10 is a commonly used value in practice for trend-adjusted exponential smoothing. It strikes a balance between giving enough weight to the trend component while still being responsive to changes in the level component. Higher values of the trend smoothing constant (such as 0.50, 0.75, or 0.90) would place more emphasis on the trend component, which may not be appropriate in all situations. A value of 1.00 would imply that the trend component is the only component of importance, resulting in a completely flat forecast.

Quiz 8: Forecasting

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?

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 are the most frequently used forecasting techniques?

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?

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

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

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

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

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?

The following sales figures show actual sales over the identified time period. What can be determined by comparing a simple mean forecast and a six month moving average forecast? December 4,000 January 5,000 February 4,000 March 4,500 April 5,500 May 5,000

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

Which is typically the most difficult data pattern to predict?

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

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 does the linear regression line do?

In linear regression, what are we trying to forecast?

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?