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BUSI 405 Quiz 1 The Forecast Process, Data Consideration, and Model Selection solutions answers
BUSI 405 Quiz 1 The Forecast Process, Data Consideration, and Model Selection solutions complete answers
The above equation is used to estimate a Gompertz curve. The “L” in the equation refers to
The arithmetic average of all the numbers in the data set is called?
Autocorrelation is the evaluation of __________ observations of a time series of data over time.
Consider the following formula:
This formula represents
A data pattern that has both a trend and seasonality would be a good candidate for which forecasting method?
The Delphi Method uses what type of subjective information?
The difference between seasonal and cyclical components is
“Events” in an Event model could include
Exponential smoothing gives more weight to the _______________ observations and less to the _______________ observations.
An exponential smoothing technique that adds a trend smoothing constant to the single-parameter exponential smoothing technique is known as
The forecasting process should begin with a clear statement of?
Forecasts based solely on the most recent observation of the variable of interest
For what type of data pattern would a simple exponential smoothing model be good as a forecast method?
For which data frequency is seasonality not a problem?
Ft = At-1 represents
Given demands, D1 = 20, D2 = 16, and D3 = 12, what is F5 using the naïve forecasting method?
Growth models like those used in ForecastX usually model situations well where a process grows
Holt’s Exponential Smoothing model has __________ equations and __________ smoothing constant(s).
Holt’s model accounts for any growth factor present in a time series by
How are the t-distribution and the normal distribution similar?
If a Theil’s U calculation is reported as “1”, this could be explained as
If housing starts are always stronger in the spring and summer than during the fall and winter. This is a result of what type of data pattern?
If we were to know the true population correlation, confidence intervals for the population correlation can be constructed using the _______ distribution.
In a moving average model, every observation is given what type of weight?
In exponential smoothing, the equation involves what type of value that is not part of the moving averages equation?
In the Winters model above, “Decomposition Type”
In the Winters model shown above, index 1 refers to calendar month 1 in the data.
In using moving-average smoothing to generate forecasts, a three-month moving average will be preferred to a six-month moving average
A k-period plot of autocorrelations is called
A large sample of X-Y data values are analyzed and reveal a correlation coefficient of −.88. Which statement is correct?
The “L” independent variable in the growth models we examined represents
The Model used to estimate the above medfly model was probably
PLC was used in the first chapter to represent
A portion of the estimate using ForecastX for forecasting GAP sales is shown. Keep in mind that the first quarter appearing in the data is calendar quarter 1. “Index 4” of 1.33 means
A “product life cycle” includes what different stages?
A random sample of bolts is taken from inventory, and their lengths are measured. The average length in the sample is 5.3 inches with a standard deviation of .2 inches. The sample size was 50. The point estimate for the mean length of all bolts in inventory is
The same benefits/criticisms apply to moving average and exponential smoothing with the exception of
A simple-centered 3-point moving average of the time-series variable Xt is given by:
The Slutsky-Yule Effect
Subjective or qualitative forecasting methods may be used effectively in
Surveys of customers and the general population is an example of what kind of forecasting method?
This book has been organized around into what three major sections?
A time series whose 24-quarter lag correlogram shows no tendency to diminish towards zero can be said to
Under what circumstances may it make sense not to prepare a business forecast?
What does correlation measure?
What does the MAPE tell a forecaster?
What does the term exponential mean in exponential smoothing models?
What is one of the biggest challenges to overcome in new product forecasting?
What is the earliest phase in the evolution of forecasting?
What type of time series data pattern contains a long-term change in the level of data?
When a product is new and there is no historical data, the most promising method to forecast this new product is?
When a time series contains no trend, it is said to be
When discussing the Delphi procedure, Rowe and Wright suggest a number of guidelines that include
When evaluating a forecast model, fit refers to how well the model works _________.
When forecasting the adoption of cellular telephones with the Bass Model,
When specifying the model used above, some limits were probably set by the forecaster. These would probably have been
When using growth curves such as the Gompertz model or the Logistics model,
When using quarterly data to forecast domestic car sales, how can the simple naïve forecasting model be amended to model seasonal behavior of new car sales, i.e., patterns of sales that arise at the same time every year?
When we test the significance of a correlation coefficient, the null hypothesis is usually
Which frame of the correlation diagram (A through F) represents an imperfect negative linear correlation?
Which frame of the correlation diagram (A through F) represents a perfect inverse linear correlation?
Which method is used to develop a simple model that assumes that weighted averages of past periods are the best predictors of the future?
Which method uses an arithmetic mean to forecast the next period?
Which of the following forecasting methods requires use of large and extensive data sets?
Which of the following is a factor in the decision to use exponential smoothing rather than moving-average smoothing to forecast a given time series?
Which of the following is not a major problem with exponential smoothing?
Which of the following is not correct concerning choosing the appropriate size of the level smoothing constant (α or alpha) in the simple exponential smoothing model?
Which of the following is not typically part of the traditional forecasting textbook?
Which of the following is true about naïve forecasting?
Which of the following is true concerning the smoothing parameter (α) used in exponential smoothing?
Which of the following measures of forecast fit can correctly be used to compare “goodness of fit” across different sized random variables?
Which of the following measures of forecast fit may correctly be used to compare different forecast models of a given data series?
Which of the following statements are true regarding exponential smoothing and moving averages?
Which one of the following situations would lend itself well to event modeling?
Which one of the following statements correctly contrasts qualitative and quantitative forecasting?
Which one of the following statements is correct about statistical hypothesis testing?
Which one of the following statements is true about supply chain efficiency?
Which one of the following would be an advantage of using qualitative forecasting over quantitative forecasting?
Which statement is incorrect?
The Winters model above
With which type of time-series data should moving-average smoothing methods produce the best forecasts?
You are given a time series of sales data with 10 observations. You construct forecasts according to last period’s actual level of sales plus the most recent observed change in sales. How many data points will be lost in the forecast process relative to the original data series?
You are provided five quarters of sales (Q1 = $2,500, Q2 = $2,100, Q3 = $1,900, Q4 = $2,000, and Q5 = $2,300). The moving average for those 5 quarters of sales would be?
The Akaike Information Criterion (AIC)
The Akaike rule of thumb is
An “Analog Forecast”
Assume that income is used to predict savings. For the regression equation Y = 1,000 + .10X, which of the following is true?
Autocorrelation refers to the correlation between a variable and
The Bayesian Information Criterion (BIC)
Choosing the appropriate size of the level smoothing constant (α) in the simple exponential smoothing model
The coefficient of determination in an ordinary least squares regression tells us
Combining individual forecasts into one composite forecast is a way to
A company has computed a seasonal index for its quarterly sales. Which of the following statements about the index is not correct?
Consider the following multiple regression model of domestic car sales (DCS) where:
DCS = domestic car sales in units sold
DCSP = domestic car sales price (in dollars)
PR = prime rate as a percent (i.e., 10% would be entered as 10)
Q2 = quarter 2 dummy variable
Q3 = quarter 3 dummy variable
Multiple Regression — Result Formula
DCS = 3,000 + -0.09DCSP -20.0PR + 293Q2 + 149Q3
Durbin Watson = 1.92 AIC = 492.5
MAPE = 5.30% BIC = 495.9
Adj R-Square = 75.64% SEE = 100
For the domestic car sales regression above, assume that:
DCSP = $10,000
PR = 10 percent
and that it is the first quarter of the year.
What will be the approximate 95% confidence interval for the DCS prediction?
Consider the following multiple regression model of domestic car sales (DCS) where:
DCS = domestic car sales in units sold
DCSP = domestic car sales price (in dollars)
PR = prime rate as a percent (i.e., 10% would be entered as 10)
Q2 = quarter 2 dummy variable
Q3 = quarter 3 dummy variable
Multiple Regression — Result Formula
DCS = 3,000 + -0.09DCSP -20.0PR + 293Q2 + 149Q3
Durbin Watson = 1.92 AIC = 492.5
MAPE = 5.30% BIC = 495.9
Adj R-Square = 75.64% SEE = 100
In the domestic car sales function, there is evidence of seasonality. How does the regression model show this evidence?
Consider the following multiple regression model of domestic car sales (DCS) where:
DCS = domestic car sales in units sold
DCSP = domestic car sales price (in dollars)
PR = prime rate as a percent (i.e., 10% would be entered as 10)
Q2 = quarter 2 dummy variable
Q3 = quarter 3 dummy variable
Multiple Regression — Result Formula
DCS = 3,000 + -0.09DCSP -20.0PR + 293Q2 + 149Q3
Durbin Watson = 1.92 AIC = 492.5
MAPE = 5.30% BIC = 495.9
Adj R-Square = 75.64% SEE = 100
The domestic car sales model
The correlation between age and health of a person is found to be −1.19. On the basis of this, you would tell the doctors that
The correlation coefficient (r) represents
A cyclical pattern
The disadvantages of subjective forecasting methods includes the consideration that
The Durbin-Watson statistic
“Events” in an Event model could include
The following is an estimated demand function:
Q= 875 + 6XA +15Y −5P
Where Q is quantity sold, XA is advertising expenditure (in thousands of dollars), Y is income (in thousands of dollars), and P is the good’s price. The equation has been estimated from 10 years of quarterly data. The adj. R2 was 0.92. The t-ratios are: for advertising, 1.98; for income 2.12; and for price -2.31.
According to the common 95 percent level of significance for the regression above,
The following is an estimated demand function:
Q = 875 + 6XA +15Y−5P
Where Q is quantity sold, XA is advertising expenditure (in thousands of dollars), Y is income (in thousands of dollars), and P is the good’s price. The equation has been estimated from 10 years of quarterly data. The adj. R2 was 0.92. The t-ratios are: for advertising, 1.98; for income 2.12; and for price -2.31.
For the above regression, approximately what percent of the variation in sales would be explained by this model?
For children, there is approximately a linear relationship between “height” and “age”. One child was measured monthly. Her height was 75 cm at 3 years of age and 85 cm when she was measured 18 months later. A least squares line was fit to her data. The slope of this line would be approximately (m represents “month”):
Graphically, a linear least squares multiple regression model with two independent variables looks like a
Holt’s exponential smoothing is likely to be a good model to try when
Holt’s forecasted values
Holt’s model accounts for any growth factor present in a time series by
Holt Winters’ exponential smoothing model uses three smoothing constants. Which of the following is not one of those constants?
How many parameters must the forecaster (or the software) set using Winter’s exponential smoothing?
how to tell if there is a trend
If a Theil’s U calculation is reported as “1,” this could be explained as
If the plot of the residuals is fan shaped, which assumption of ordinary least squares regression is violated?
If you look at monthly data for most retail sales you see strong sales in December. Therefore, which of the following is likely to be the best model to forecast retail sales?
In a cross-sectional study of sales in different cities, the following relationship between sales revenue (S = sales revenue in dollars) and city size as measured by population (POP = population in thousands) was estimated: S = 3,702 + 0.67(POP). What is the point estimate for sales in a city of 100,000 people?
In cross-sectional regression analysis which of the following is true,
In linear least squares regression (OLS), we use “squares” because:
In the classical time-series decomposition model, relatively smooth up-and-down swings of a variable around the trend (typically lasting from one to several years each and differing in length and amplitude from one occurrence to the next) are known as
In the Gap Sales Case at the end of Chapter One, it was suggested that we could use a “modified naïve forecasting method” to make a prediction. What “modification” was being suggested to the naïve model?
In time-series decomposition analysis, decomposition refers to
In time-series decomposition, seasonal factors are calculated by (subscripts for the time period are omitted for clarity)
In using moving-average smoothing to generate forecasts, a three-month moving average will be preferred to a six-month moving average
In using quarterly time series data, which quarter can serve as the base period for interpretation of dummy variables?
Lackland Ski Resort uses multiple regression to forecast ski lift revenues for the next week based on the forecasted number of days with temperatures above 10 degrees and predicted number of inches of snow. The following function has been developed:
Revenue = 10,902 + 255 (number days predicted above 10 degrees) + 300 (number of inches of snow predicted)
Assume that the management predicts the number of days above 10 degrees for the next week to be 6 and the number of inches of snow to be 12. Calculate the predicted amount of revenue for the next week.
The least squares procedure minimizes the sum of
Measures of forecast accuracy based upon a quadratic error cost function, notably root mean square error (RMSE), tend to treat
Model A has an AIC number of 300 whereas model B has an AIC number of 400 (both models have the same dependent variable). This suggests that which model is more correctly specified?
Multicollinearity in a regression model occurs when
The notion of a product life cycle can be applied to
p < significance level
Perfect multicollinearity is the
A potential cure for the multicollinearity problem is
Qualitative or subjective forecasting methods include
Quarterly time-series data with a trend can be applied to models that assume stationary data by
A regression of retail sales on disposable income and two interest rates, the prime rate and the short-term savings rate, is likely to have the problem of
A residual is
The results from a regression method of combining forecasts is best described as
The seasonal indexes above are (monthly data showed)
A simple-centered 3-point moving average of the time-series variable Xt is given by:
The simple exponential smoothing model can be expressed as
Simple-exponential smoothing models differ from moving average models in that
sing the significance levels reported by ForecastX (or Excel), at what level can we reject a one-sided null relating to a slope coefficient’s statistical significance such that we are 95% confident?
The Sky-Is-Falling forecasting firm is predicting a deep recession next year. What would be the average value of the forecasted cycle factor for next year if you believe such a forecast?
The strength of the linear relationship between two numerical variables may be measured by the
A study was conducted to examine the quality of fish after seven days in ice storage. For this study
Y = measurement of fish quality (on a 10-point scale with 10 = BEST.)
X = # of hours after being caught that the fish were packed in ice.
The linear regression line is: Y = 8.5 - .5X. From this we can say that:
The sum of seasonal index numbers should equal
Suppose that you mistakenly move the decimal point to the right one digit in data from a normal population with a mean of zero. What happens to the standard deviation?
Suppose two random variables X and Y are related as follows: Y = 1/X^2. The population Pearson correlation coefficient should be
Suppose you are attempting to forecast a variable that is independent over time such as stock rates of return. A potential candidate-forecasting model is
Suppose you observe the entire population of a random variable and you wish to test some hypothesis about the mean. To perform your hypothesis test, you
The t-Distribution (also called the Student’s t-Distribution)
There is an approximate linear relationship between the height of children and their age (from 5 to 18 years) described by:
Height = 50.3 + 6.01(age)
where height is measured in cm and age in years. Which of the following is not correct?
The time-series decomposition model is best described as a
A trend in a time series
The two regressions above show
Under which of the following conditions will a forecast combination be most likely to lead to an increases in forecast accuracy?
Using the regression approach to selecting optimal forecast combination weights, no combination model bias would be likely if
What do moving-average smoothing and exponential smoothing have in common?
What factors do the exponential smoothing techniques presented in Chapter Three have in common?
What is not likely to be a problem when applying ordinary least squares to cross-sectional data?
What methods seem suited to forecasting new-product sales?
What values of Theil’s U statistic are indicative of an improvement in forecast accuracy relative to the no-change naïve model?
When calculating centered moving-averages using a 4-period moving average, how many data points are lost at each end of the original series?
When calculating centered moving-averages using a 4-period moving average, how many data points are lost at the beginning of the original series?
When serial correlation is present we might believe
When serial correlation is present, which of the following is not true?
When the level smoothing constant of an estimated simple exponential smoothing model is close to one,
When using regression to select the optimal weights for use in a composite forecast process, one can test for bias using which of the following?
Which data series is not used in the calculation of cycle factors?
Which forecasting model identifies and forecasts component factors that influence the level of a time series?
Which functions are not appropriate for use of the Pearson correlation coefficient to estimate the correlation between a pair of random variables?
Which measure of dispersion in a data set is the most intuitive and represents an average?
Which method is used to develop a simple model that assumes that weighted averages of past periods are the best predictors of the future?
Which method uses an arithmetic mean to forecast the next period?
Which of the following does not become unreliable when serial correlation is present?
Which of the following “goodness-of-fit” measures should not be used in the context of multiple regression?
Which of the following is a measure of central tendency in a population?
Which of the following is a possible cause of serial correlation?
Which of the following is a similarity between seasonal and cycle factors?
Which of the following is not a descriptive statistic?
Which of the following is not a foundation of classical statistics?
Which of the following is not a major problem with exponential smoothing?
Which of the following is NOT a measure of central tendency in a population?
Which of the following is not an aspect of the Winters’ exponential smoothing model?
Which of the following is not a reason for testing if the population correlation coefficient is zero?
Which of the following is not a reason why time-series decomposition has gained favor with forecasters and their managers?
Which of the following is not considered a smoothing model?
Which of the following is not consistent with the presence of a trend in a time series?
Which of the following is not correct about multicollinearity?
Which of the following is not correct about using moving averages to deseasonalize a time series?
Which of the following is not correct concerning choosing the appropriate size of the level smoothing constant (α or alpha) in the simple exponential smoothing model?
Which of the following is not correct? Seasonality in a time series data set containing quarterly observations can be handled by
Which of the following is not helpful in generating forecasts of cycle factors?
Which of the following is not recommended in selecting the correct set of independent variables for multiple regression?
Which of the following is not true regarding simple exponential smoothing?
Which of the following is not true regarding testing for serial correlation?
Which of the following is not useful in using multiple regression to generate forecasts?
Which of the following is probably not a potential cause of data seasonality?
Which of the following is true about the convention, used by some, in which all forecasts are equally weighted in a combination?
Which of the following is true concerning the smoothing parameter (α) used in exponential smoothing?
Which of the following methods is not useful for forecasting sales of a new product?
Which of the following statements about the cyclical component of a classical time series decomposition model is false?
Which of the following would indicate a perfect model fit based on a bivariate regression?
Which of the following would not be an appropriate use of forecast errors to assess the fit of a particular forecasting model?
Which subjective sales forecasting method may have the most information about the spending plans of customers for a specific firm?
Which subjective sales forecasting technique may have problems with individuals who have a dominant personality?
Which time-series component is said to fluctuate around the long-term trend and is fairly irregular in appearance?
Which trend model (equation) would most likely to be correct if the variable you are seeking to forecast was increasing at a constant rate?
Winter’s Exponential Smoothing
With which type of time-series data should moving-average smoothing methods produce the best forecasts?
Which of the following measures of forecast accuracy may correctly be used to compare different forecast models of a given data series?
Which of the following measures of forecast accuracy is unit-free (does not depend on the unit of measurement of the data series)?
What values of Theil's U statistic are indicative of an improvement in forecast accuracy relative to the naïve model?
Because of different units being used for various data series, which fit statistic can be used across different series that are in fact measured in different units?
The data above represents the total houses sold in thousands of units per month through December of 2004. Use an appropriate naïve model to forecast January 2005 sales.
The difference between seasonal and cyclical components is
For which data frequency is seasonality not a problem?
When a time series contains no trend, it is said to be
Which of the following is not consistent with the presence of a trend in a time series?
Autocorrelation refers to the correlation between a variable and
Autocorrelation Function for GDP
Autocorrelation function for GDP with second differencing
Refer to the autocorrelation functions for Gross Domestic Product (GDP) presented above. The shape of these autocorrelation functions
A cyclical pattern
A seasonal pattern
Moving-average smoothing may lead to misleading inference when applied to
Simple-exponential smoothing models are useful for data which have
Simple-exponential smoothing models differ from moving average models in that
The error-correction form of the simple exponential smoothing model states that if the current forecast
If a smoothing constant of .3 is used, what is the exponentially smoothed forecast for period 4?
What is the forecast error for period 3?
If a three-month moving-average model is used, what is the forecast for period 4?
Holt's forecasted values
Holt's smoothing is best applied to data that are
Holt's model accounts for any growth factor present in a time series by
Winter's exponential smoothing
First-differencing the data is a way to
Perfect multicollinearity is the
In a regression of sales on income and seasonal dummy variables for a quarterly time series, a negative sign of the quarter 3 dummy variable means
Consider the following multiple regression model of domestic car sales (DCS) where:
DCS = domestic car sales
DCSP = domestic car sales price (in dollars)
PR = prime rate as a percent (i.e., 10% would be entered as 10)
Q2 = quarter 2 dummy variable
Q3 = quarter 3 dummy variable
Q4 = quarter 4 dummy variable
What will DCS be predicted to be by the regression model?
Seasonal Indexes of sales revenue of People's Bank are:
Total revenue for People's Bank in 1999 is forecasted to be $60,000. Based on the seasonal indexes above, sales in the first three months of 1999 should be
If December 1999 revenue for People's Bank amounted to $5,000, a reasonable estimate of revenue for January 2000, based on the seasonal indexes given above, would be
