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# Forecasting And Predictive Analytics With Forecast X 7Th Edition by J. Holton Wilson – Test Bank

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Forecasting And Predictive Analytics With Forecast X 7Th Edition by J. Holton Wilson – Test Bank

Sample Questions

Chapter 2   The Forecast Process, Data Considerations, and Model Selection

1) Why are forecasting textbooks full of applied statistics?

1. A) Statistics is the study of uncertainty.
2. B) Real-world business decisions involve risk and uncertainty.
3. C) Forecasting attempts to generate certainty out of uncertain events.
4. D) Forecasting ultimately deals with probability.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Forecast Process

Learning Objective:  2-01 Explain a process for developing forecasts.

2) Which of the following is not part of the recommended nine-step forecast process?

1. A) What role do forecasts play in the business decision process?
2. B) What exactly is to be forecast?
3. C) How urgent is the forecast?
4. D) Is there enough data?
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Forecast Process

Learning Objective:  2-01 Explain a process for developing forecasts.

3) Of the following model selection criteria, which is often the most important in determining the appropriate forecast method?

1. A) Technical background of the forecast user
2. B) Patterns the data have exhibited in the past
3. C) How much money is in the forecast budget?
4. D) What is the forecast horizon?
5. E) When is the forecast needed?

Difficulty: 1 Easy

Topic:  Data Patterns and Model Selection

Learning Objective:  2-03 Identify forecasting methods that would be good candidates for a given series to be forecast.

4) Time series data of a typical The GAP store should show which of the following data patterns?

1. A) Trend
2. B) Seasonal
3. C) Cyclical
4. D) Random
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

5) Which of the following is incorrect?

1. A) The forecaster should be able to defend why a particular model or procedure has been chosen.
2. B) Forecast errors should be discussed in an objective manner to maximize management’s confidence in the forecast process.
3. C) Forecast errors should not be discussed since most people know that forecasting is an inexact science.
4. D) You should tailor your presentation to the sophistication of the audience to maximize credibility in the forecast process.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Forecast Process

Learning Objective:  2-03 Identify forecasting methods that would be good candidates for a given series to be forecast.

6) In the model-testing phase of the nine-step process, which of the following refers to that portion of a sample used to evaluate model-forecast accuracy?

1. A) Fit
2. B) Forecast horizon
3. C) Holdout period
4. D) Accuracy
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Forecast Process

Learning Objective:  2-01 Explain a process for developing forecasts.

7) Your authors present a guide to selecting an appropriate forecasting method based on

1. A) data patterns.
2. B) quantity of historical data available.
3. C) forecast horizon.
4. D) quantitative background of the forecast user.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Forecast Process

Learning Objective:  2-01 Explain a process for developing forecasts.

8) Which time-series component is said to fluctuate around the long-term trend and is fairly irregular in appearance?

1. A) Trend.
2. B) Cyclical.
3. C) Seasonal.
4. D) Irregular.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

9) Forecasting January sales based on the previous month’s level of sales is likely to lead to error if the data are _______.

1. A) stationary
2. B) non-cyclical
3. C) seasonal
4. D) irregular
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

10) The difference between seasonal and cyclical components is

1. A) duration
2. B) source
3. C) predictability
4. D) frequency
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

11) For which data frequency is seasonality not a problem?

1. A) Daily.
2. B) Weekly.
3. C) Monthly.
4. D) Quarterly.
5. E) Annual.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

12) One can realistically not expect to find a model that fits any data set perfectly due to the _______ component of a time series.

1. A) Trend
2. B) Seasonal
3. C) Cyclical
4. D) Irregular
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

13) When a time series contains no trend, it is said to be

1. A) nonstationary.
2. B) seasonal.
3. C) nonseasonal.
4. D) stationary.
5. E) filtered.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

14) Stationarity refers to

1. A) the size of the RMSE of a forecasting model.
2. B) the size of variances of the model’s estimates.
3. C) a method of forecast optimization.
4. D) lack of trend in a given time series.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Trend, Seasonal and Cyclical Data Patterns

Learning Objective:  2-02 Distinguish between trend, seasonal, and cyclical data patterns.

15) Which of the following is not a measure of central tendency in a population?

1. A) Mean.
2. B) Mode.
3. C) Median.
4. D) Range.

Difficulty: 1 Easy

Topic:  A Statistical Review

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

16) Which of the following is not a descriptive statistic?

1. A) Expected value.
2. B) Mean.
3. C) Range.
4. D) Variance.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

17) Which of the following is not a foundation of classical statistics?

1. A) Summary measures of probability distributions called descriptive statistics
2. B) Probability distribution functions, which characterize all outcomes of a variable
3. C) The use of sampling distributions, which describe the uncertainty in making inference about the population on the basis of a sample
4. D) The concept of expected value
5. E) None of the options are correct.

Difficulty: 2 Medium

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

18) The standard normal probability table

1. A) is equivalent to a t distribution if the sample size is less than 30.
2. B) shows a normal distribution with standard deviation equal to zero.
3. C) is used to make inference for all normally distributed random variables.
4. D) All of the options are correct.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Normal Distribution

Learning Objective:  2-05 Explain the most common measures of dispersion in data.

19) The median and mode may be more accurate than the sample mean in forecasting the populations mean when

1. A) the sample size is small.
2. B) the sample size is large.
3. C) the sample has one large outlier.
4. D) the population is assumed to be normally distributed.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

20) The arithmetic average of the relative frequency of the occurrence of some random variable is also called the _______.

1. A) range
2. B) mean
3. C) variance
4. D) standard deviation
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

21) In finance, an investor who ignores risk is termed “risk neutral.” What descriptive statistic is our risk neutral investor ignoring when she generates stock portfolios?

1. A) Median.
2. B) Mean.
3. C) Mode.
4. D) Standard deviation.
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

22) In calculating the sample variance, we subtract one from the sample size. This is because

1. A) the population mean is unknown.
2. B) of using the sample mean to estimate the population mean.
3. C) the sum of deviations about the sample mean is zero.
4. D) the sample mean is employed.
5. E) All of the options are correct.

Difficulty: 3 Hard

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

23) Which statistic is correctly interpreted as the “average” spread of data about the mean?

1. A) Mode.
2. B) Range.
3. C) Variance.
4. D) Standard deviation.
5. E) Mean.

Difficulty: 1 Easy

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

24) Which measure of dispersion in a data set is the most intuitive and represents an average?

1. A) Range.
2. B) Mode.
3. C) Standard deviation.
4. D) Variance.
5. E) Mean.

Difficulty: 1 Easy

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

25) Which of the following is not an attribute of a normal probability distribution?

1. A) It is symmetrical about the mean.
2. B) Most observations cluster around the mean.
3. C) Most observations cluster around zero.
4. D) The distribution is completely determined by the mean and variance.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Normal Distribution

Learning Objective:  2-06 Discuss the normal and Student’s t distributions.

26) Which of the following is not a foundation of classical statistics?

1. A) Summary measures of probability distribution called descriptive statistics.
2. B) Probability distribution function which characterizes all possible outcomes of a random variable.
3. C) The knowledge of thousands and thousands of normal probability tables required for statistical inference of normally distributed random variables.
4. D) The concept of expected value, which is the average value of a random variable taken over a large number of samples.

Difficulty: 3 Hard

Topic:  A Statistical Review

Learning Objective:  2-06 Discuss the normal and Student’s t distributions.

27) A company claims that the rubber belts, which it manufactures, have a mean service life of at least 800 hours. A random sample of 36 belts from a very large shipment of the company’s belts shows a mean life of 760 hours and a standard deviation of 90 hours. Which of the following is the most appropriate on the basis of the sample results?

1. A) The sample results do not warrant rejection of the company’s claim if the risk of a Type I error is specified at .05.
2. B) The sample results do warrant rejection of the company’s claim if the risk of Type I error is specified at .05.
3. C) Since the sample mean falls below the company’s claim, the sample results indicate that the company claim is incorrect.
4. D) The sample results are indeterminate since the magnitude of the sample standard deviation is greater than the difference between the company’s claimed figure and the sample mean.

Difficulty: 1 Easy

Topic:  Hypothesis Testing

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

28) Based upon ten years of monthly data, the monthly rate of return of the DOW Jones 30 composite stock portfolio was normally distributed with mean .0084 and variance .0014. What is the probability, that in any given month, we observe a rate of return on the DOW above 10 percent?

1. A) Less than one percent.
2. B) Two percent.
3. C) Three percent.
4. D) Not enough information is provided to answer the question.

Difficulty: 2 Medium

Topic:  A Statistical Review

Learning Objective:  2-06 Discuss the normal and Student’s t distributions.

29) 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

1. A) apply a sampling distribution to the problem.
2. B) obtain sample estimates of population parameters.
3. C) simply find the population mean and compare it to the hypothesized value.
4. D) apply the t distribution.
5. E) There is no answer to this question.

Difficulty: 1 Easy

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

30) If two large random samples are drawn from two populations, each having a mean of \$100, the relevant sampling distribution of their difference has a mean of

1. A) \$200.
2. B) the sum of the two sample means.
3. C) 0.
4. D) the difference between the two sample means.

Difficulty: 1 Easy

Topic:  Descriptive Statistics

Learning Objective:  2-04 Explain the differences between the mean, median, and mode for a set of data.

31) Type I error

1. A) is said to arise when we reject a true null hypothesis.
2. B) has a probability value equal to the significance level of any statistical test.
3. C) is a measure of the uncertainty associated with rejecting any null hypothesis on the basis of sample data.
4. D) Both A and B are correct.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Hypothesis Testing

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

32) Sampling distributions

1. A) are the distributions of all possible values of a sample statistic based upon repeated sampling.
2. B) are used to make inference when the population of a variable is unobservable.
3. C) exhibit important properties for the ranking of alternative estimators such as unbiasedness and efficiency.
4. D) All of the options are correct.

Difficulty: 1 Easy

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-06 Discuss the normal and Student’s t distributions.

33) An unbiased model

1. A) is one that does not consistently over-estimate or under-estimate the true value of a parameter.
2. B) is one that consistently produces estimates with the smallest RMSE.
3. C) is one which contains no independent variable; it depends solely on time-series pattern recognition.
4. D) is one made up by a team of forecasters.

Difficulty: 2 Medium

Topic:  Data Patterns and Model Selection

Learning Objective:  2-03 Identify forecasting methods that would be good candidates for a given series to be forecast.

34) 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?

1. A) Data with mistake has standard error ten times the original.
2. B) Data with mistake has same standard error as the original.
3. C) Data with the mistake has twice the standard error of the original.
4. D) Data with the mistake has one hundred times the standard error of the original.
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  A Statistical Review

Learning Objective:  2-05 Explain the most common measures of dispersion in data.

35) Which statement is incorrect?

1. A) Confidence intervals depend on sample size.
2. B) The sample mean is the best estimator if sampling from a normal population.
3. C) The sample mean is an unbiased estimator.
5. E) The sample variance is an unbiased estimator.

Difficulty: 2 Medium

Topic:  A Statistical Review

Learning Objective:  2-05 Explain the most common measures of dispersion in data.

36) A machine fills ketchup bottles. One of the requirements is that the mean content of the bottles should be 10 ounces. Management wishes to set up a decision rule to decide whether or not this is true based on a random sample of bottles. The risk of type I error is specified at .05. A sample of 100 bottles will be taken; it is believed that the standard deviation of fills is .3 ounces. If it is decided that Z = 2, the decision rule boundary values are

1. A) 9.60 and 10.40.
2. B) 9.10 and 9.90.
3. C) 9.94 and 10.05.
4. D) 9.40 and 10.60.
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  A Statistical Review

Learning Objective:  2-05 Explain the most common measures of dispersion in data.

37) Last year’s midterm results showed a mean of 51 points and a variance of 46. An approximate confidence interval is

1. A) 44.2 to 57.8.
2. B) 37.4 to 64.6.
3. C) 5 to 97.
4. D) None of the options are correct.

Difficulty: 2 Medium

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

38) A difference between the population standard deviation of the random variable X and the standard deviation of the sampling distribution of the sample mean is

1. A) one is based upon the other.
2. B) dependence on sample size.
3. C) the possibility of sampling error.
4. D) application to the t distribution.
5. E) All of the options are correct.

Difficulty: 3 Hard

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

39) Which probability distribution is appropriate for testing hypotheses concerning an unknown population mean when the sample variance is used to estimate the population variance?

1. A) The normal distribution with mean μ and variance σ2.
2. B) The normal distribution with mean 0 and variance 1.
3. C) The standard normal distribution.
4. D) The t distribution with n-1 degrees of freedom.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Student’s t-Distribution

Learning Objective:  2-06 Discuss the normal and Student’s t distributions.

40) 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

1. A) 5.3 inches.
2. B) .2 inches.
3. C) 4.908 to 5.692 inches.
4. D) 5.3 inches plus or minus .2

Difficulty: 2 Medium

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

41) 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.

A 95% confidence interval for the unknown population mean is

1. A) 5.3 inches.
2. B) 4.9 to 5.7 inches.
3. C) 5.3 inches plus or minus .056.
4. D) 4.784 to 5.816 inches.
5. E) None of the options are correct.

Difficulty: 2 Medium

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

42) Which of the following statements about the probability of Type I and Type II error is not correct?

1. A) Type I error cannot occur if the null hypothesis is false.
2. B) Type II error cannot occur if the null hypothesis is true.
3. C) If the null hypothesis is true, the results of the test will either be a correct conclusion or a Type I error.
4. D) It is not possible to specify both the probabilities of Type I and II errors since only one of them can occur.

Difficulty: 2 Medium

Topic:  Hypothesis Testing

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

43) A sample of 100 are selected at random from a process with a mean of 500.52 and a standard deviation of 4.0. Estimate the probability that a sample of 100 would have a mean equal to or greater than 500.52 if the true population mean is really 500.0.

Difficulty: 2 Medium

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

44) A random sample of employee files is drawn revealing an average of 2.8 overtime hours worked per week with a standard deviation of .7; the sample size is 500. The resulting 90% confidence interval is

1. A) 2.1 to 3.5.
2. B) 2.6 to 3.5.
3. C) 2.75 o 2.85.
4. D) 2.6 to 3.0.
5. E) None of the options are correct.

Difficulty: 2 Medium

Topic:  From Sample to Population: Statistical Inference

Learning Objective:  2-07 Describe three common forms of statistical hypotheses.

45) A medical researcher has just calculated a correlation coefficient of zero for two particular random variables. Which of the following statements is most accurate?

1. A) There is no significant linear difference between the two variables.
2. B) There is no significant relationship between the two variables.
3. C) There is no significant linear relationship between the two variables.
4. D) There is a significant linear relationship between the two variables.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

46) The correlation coefficient (ρ) is an extremely important descriptive statistic because

1. A) It provides a unit-free measure of how two random variables move together.
2. B) It provides a measure of the linear association between a pair of random variables.
3. C) It provides the forecaster with a diagnostic tool of when regression analysis is appropriate for the business-forecasting problem.
4. D) All of the options are correct.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

47) A large sample of X-Y data values are analyzed and reveal a correlation coefficient of −.88. Which statement is correct?

1. A) If r had been +.88, the correlation would have been much stronger.
2. B) The correlation is weak because r is less than -1.
3. C) A fairly strong negative linear relationship exists.
4. D) A weak negative relationship exists.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

48) Suppose two random variables X and Y are related as follows: Y = 1/X2. The population Pearson correlation coefficient should be

1. A) +1.
2. B) 0.
3. C) −1.
4. D) .5.
5. E) None of the options are correct.

Difficulty: 2 Medium

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

49) Which functions are not appropriate for use of the Pearson correlation coefficient to estimate the correlation between a pair of random variables?

1. A) Cubic polynomials.
3. C) Higher-order polynomials.
4. D) Functions involving a variable raised to the one-half power.
5. E) Reciprocal functions.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

50) If we were to know the true population correlation, confidence intervals for the population correlation can be constructed using the _______ distribution.

1. A) t distribution
2. B) standard normal distribution
3. C) chi-square distribution
4. D) F distribution
5. E) All of the options are correct.

Difficulty: 2 Medium

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

51) If the scatterplot of two variables has a circular pattern, this suggests the two variables have a population correlation coefficient of

1. A) −1.
2. B) −.5.
3. C) 0.
4. D) +.5.
5. E) +1.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

52) Which of the following is not used to calculate the sample Pearson correlation coefficient for the variables X and Y?

1. A) Sample mean of X.
2. B) Sample mean of Y.
3. C) Sample covariance of X and Y.
4. D) Sample standard deviation of X.
5. E) All of the options are used to calculate correlation coefficients.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

53) Which of the following is not a benefit of a scatter diagram?

1. A) The nature of the X-Y relationship (linear of nonlinear) may be revealed.
2. B) The strength of the relationship may be revealed.
3. C) The sign of the correlation coefficient will be revealed.
4. D) Displaying the population size.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

54) In order to conduct a correlation analysis, the collected data must be

1. A) related to the real world.
2. B) numerical.
3. C) constructed of categories.
4. D) highly correlated.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Correlation

Learning Objective:  2-08 Explain what a statistical correlation measures.

Forecasting and Predictive Analytics with Forecast X, 7e (Keating)

Chapter 4   Extrapolation 2. Introduction to Forecasting with Regression Trend Models

1) The least squares procedure minimizes the sum of

1. A) the residuals.
2. B) squared maximum error.
3. C) absolute errors.
4. D) squared residuals.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

2) A residual is

1. A) the difference between the mean of Y conditional on X and the unconditional mean.
2. B) the difference between the mean of Y and its actual value.
3. C) the difference between the regression prediction of Y and its actual value.
4. D) the difference between the sum of squared errors before and after X is used to predict Y.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

3) The condition expectation of a random variable

1. A) is denoted by E(Y | X = x) and tells us the expected value of Y given a particular value of X.
2. B) is the foundation of the simple regression model.
3. C) is modeled using a linear function in the simple regression model.
4. D) is represented graphically by the regression line in the simple bivariate model.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

4) The Y-intercept of the simple regression model

1. A) rarely has a useful interpretation.
2. B) almost always has a useful interpretation.
3. C) is always a positive number.
4. D) is always positive when the correlation between the dependent and independent variable is positive.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

5) The Y-intercept of a regression line is −14, and the slope is 3.5. Which of the following is not correct?

1. A) When Y increases by one, X increases by 3.5.
2. B) When X increases by one, Y increases by 3.5.
3. C) The regression line crosses the Y-axis at −14.
4. D) X and Y are positively related.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

6) Income is used to predict savings. For the regression equation Y = 1,000 + .10X, which of the following is true?

1. A) Y is income, X is savings, and income is the independent variable.
2. B) Y is income, X is savings, and savings is the independent variable.
3. C) Y is savings, X is income, and savings is the independent variable.
4. D) Y is savings, X is income, and income is the independent variable.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

7) The sign on the slope estimate in a regression problem

1. A) is the same as the sign of the Y-intercept.
2. B) is the opposite of the sign of the correlation of Y and X.
3. C) has no relationship to the sign of the correlation of Y and X.
4. D) always has the same sign as the correlation of Y and X.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

8) X-Y data have been collected in which X ranges between 50 and 100 and Y ranges between 1200 and 2000. It is not wise to use the resulting regression line equation to predict Y when X is equal to −10 because

1. A) a negative number cannot be used.
2. B) the predicted value for Y might turn out to be negative.
3. C) the Y-intercept might be above zero.
4. D) the proposed X value is well beyond the range of observed data.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

9) The following regression equation was estimated: Y = −2.0 + 4.6X. This indicates that

1. A) there has been an error since “b” cannot be a negative number.
2. B) there is a negative relationship between the two variables.
3. C) Y equals 44 when X is 10.
4. D) the correlation coefficient for Y and X will be negative.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

10) Which of the following is not a reason to employ simple linear regression to generate sales forecasts for a retail outlet store?

1. A) Causal relationships can be examined.
2. B) Trend can be handled using a time index.
3. C) Data seasonality can be handled by deseasonalizing the data.
4. D) The conditional mean of sales can be estimated.
5. E) None of the options are correct.

Difficulty: 2 Medium

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

11) Sample regression model forecast errors are called

1. A) disturbances.
2. B) residuals.
3. C) least-squares predictions.
4. D) outliers.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

12) The regression slope term (β) in the simple bivariate regression model is

1. A) correctly interpreted as dY/dX.
2. B) usually known to the investigator.
3. C) the change in the conditional mean of Y given a unit change in X.
4. D) undetermined using the OLS method.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

13) Regression model disturbances (forecast errors)

1. A) are assumed to follow a normal probability distribution.
2. B) are assumed to be independent over time.
3. C) are assumed to average to zero.
4. D) can be estimated by OLS residuals.
5. E) All of the options are correct.

Difficulty: 2 Medium

Topic:  The Bivariate Regression Model

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

14) Serial correlation (or autocorrelation) causes estimates of the

1. A) slope parameter β to be understated on average.
2. B) slope parameter β to be overstated on average.
3. C) estimates of the standard errors to be understated on average.
4. D) estimates of the standard errors to be overstated on average.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

15) Visual inspection of the data will help the forecaster identify

1. A) trend.
2. B) seasonality.
3. C) linearity.
4. D) nonlinearity.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Visual Inspection of Data

Learning Objective:  4-01 Explain why it is important to look at data in a graph rather than only in a table.

16) Which of the following is a tool used in model selection?

1. A) Seasonality
2. B) Cyclicity
3. C) Growth
4. D) Plotting the data
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  A Process for Regression Forecasting

Learning Objective:  4-02 Describe the type of data patterns for which a linear regression trend forecast would be appropriate.

17) Which of the following is not a recommended step in preparing a forecast using the simple linear regression model?

1. A) Visually inspect the data.
2. B) Forecast the independent variable.
3. C) Specify a regression model.
4. D) Select a holdout period for model evaluation.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  A Process for Regression Forecasting

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

18) Fit and accuracy

1. A) are the same things.
2. B) do not depend on the fitted regression model.
3. C) do not depend on the estimated standard error.
4. D) reflect in sample versus out-of-sample model forecast errors.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  A Process for Regression Forecasting

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

19) Which of the following is not a method for estimating data with trend?

1. A) Holt’s smoothing
2. B) Winter’s smoothing
3. C) Regression linear trend model
4. D) Using time as the independent variable
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  A Process for Regression Forecasting

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

20) The most common mathematical trend equation for a time series is called the least squares trend because it is the line which minimizes the sum of the

1. A) squares of deviations from the sample mean.
2. B) deviations from the mean.
3. C) squared vertical deviations from the trend line.
4. D) deviations from the mean of the X variable.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Bivariate Regression Model

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

21) Consider the following model: Sales = α + β(TIME)2 + ε. The trend is modeled here as a(n)

1. A) linear trend.
2. B) exponential trend.
4. D) logarithmic trend.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Bivariate Regression Model

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

22) Seasonal indices of sales for the Black Lab Ski Resort are for 1.20 for January and .80 for December. If December sales for 1998 were \$5,000, a reasonable estimate of sales for January 1999 is

1. A) \$4,800.
2. B) \$6,000.
3. C) \$7,500.
4. D) \$10,000.
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Using a Causal Regression Model to Forecast

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

23) The expected trend value of September sales for a firm is \$900. Assuming a September seasonal index of .91, what would be the seasonally adjusted forecast for September?

1. A) \$989
2. B) \$950
3. C) \$900
4. D) \$819
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Using a Causal Regression Model to Forecast

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

24) Which of the following is not correct about causal regression analysis of the form Y = f(X)?

1. A) Selection of the appropriate causal variable Y is important.
2. B) Selection of the appropriate causal variable X is important.
3. C) Use of past experience to identify X is common.
4. D) Use of economic theory to identify X is common.
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Using a Causal Regression Model to Forecast

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

If the quarter four seasonal index is 1.07264 and DPI is 19,119.6, our forecast for quarter-four sales is

1. A) \$550,200.51.
2. B) \$619,352.99.
3. C) \$620,531.65.
4. D) \$668,116.89.
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Using a Causal Regression Model to Forecast

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

 Month Seasonal Index January 1.20 February .90 March 1.00 April 1.08 May 1.02 June 1.10 July 1.05 August .90 September .85 October 1.00 November 1.10 December .80

Total annual sales for BDC in 2001 are forecasted at \$120 million. Based on the seasonal indexes above, sales in the first three months of 2001 should be

1. A) 10 million dollars.
2. B) 1.2 million dollars.
3. C) 30 million dollars.
4. D) 31 million dollars.
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Using a Causal Regression Model to Forecast

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

 Month Seasonal Index January 1.20 February .90 March 1.00 April 1.08 May 1.02 June 1.10 July 1.05 August .90 September .85 October 1.00 November 1.10 December .80

If December 2000 sales for BDC are 20 million, what is a reasonable estimate for sales in January of 2001?

1. A) 16 million dollars
2. B) 19.2 million dollars
3. C) 20.84 million dollars
4. D) 30 million dollars
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Using a Causal Regression Model to Forecast

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

 Month Seasonal Index January 1.20 February .90 March 1.00 April 1.08 May 1.02 June 1.10 July 1.05 August .90 September .85 October 1.00 November 1.10 December .80

If BDC sales in November of 2000 were 12 million dollars, November sales after adjustment for seasonal variation are

1. A) 10.91 million dollars.
2. B) 13.2 million dollars.
3. C) 13.1 million dollars.
4. D) Not enough information is present to answer the question.
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Using a Causal Regression Model to Forecast

Learning Objective:  4-03 Explain how a seasonal data set can be forecast with a linear regression trend.

29) Which of the following statements is true?

1. A) Autocorrelation arises when there is a perfect linear association among the independent variables in the sample.
2. B) Autocorrelation and its presence have no effect on the Gauss-Markov theorem.
3. C) Autocorrelation causes the sum of squares decomposition to become unreliable.
4. D) Autocorrelation causes the ordinary least squares estimate of the error variance to become biased.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

30) Which of the following is incorrect?

1. A) R2is a measure of the degree of variability in the dependent variable about its sample mean explained by the regression line.
2. B) R2measures the “goodness-of-fit” of a regression model.
3. C) The null hypothesis that R2= 0 can be tested using an F-test.
4. D) The best model selection criteria and variable selection criteria for a forecaster to use is the maximization of R Squared.
5. E) None of the options are correct.

Difficulty: 2 Medium

Topic:  Statistical Evaluation of Regression Models

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

31) The autocorrelation parameter is used to measure

1. A) disturbances or independent random variates.
2. B) correlation between residuals.
3. C) the slope of the regression line.
4. D) error or difference between a data point and the regression line.
5. E) difference between present and past residuals.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

32) Which of the following is not used to solve the problem of autocorrelation?

1. A) Autoregressive models
2. B) Improving the model specification
3. C) Moving average smoothing
4. D) First differencing the data
5. E) Regression using percentage changes

Difficulty: 2 Medium

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

33) If the residuals in a regression equation are positively autocorrelated, which of the following is not a problem when the least squares procedure is used?

1. A) The standard error of the regression slope coefficient underestimates the true variability of the estimated regression.
2. B) Confidence intervals are no longer strictly applicable.
3. C) The t and F distributions are no longer strictly applicable.
4. D) The regression coefficients are no longer strictly applicable.
5. E) The standard error of the regression seriously understates the variability of the error terms.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

34) When autocorrelation is present, which of the following is not a problem?

1. A) The F-statistic may be unreliable.
2. B) The t-statistics for each coefficient will be overstated.
3. C) The R-squared statistic may be unreliable.
4. D) The estimated standard errors will be larger than the true standard errors.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

35) When severe autocorrelation is indicated after a regression model has been estimated, which underlying regression assumption is violated?

1. A) The population of Y values is normally distributed about the population regression line.
2. B) The dispersion of population data points around the population regression line remains constant everywhere along the line.
3. C) The error terms are independent of each other.
4. D) A linear relationship exists between X and Y in the population.
5. E) Heteroscedasticity

Difficulty: 2 Medium

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

36) One method for solving the autocorrelation problem is to take advantage of the correlation between adjacent observations. This method is called

1. A) Regression on percentage changes in the data.
2. B) Multiple regression.
3. C) Durbin-Watson model.
4. D) Cochrane-Orcutt regression method.
5. E) Exponential model.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

37) Which of the following is not an indicator of regression fit?

1. A) Does the estimated sign of the slope coefficient make economic sense?
2. B) Is R-squared greater than one?
3. C) Is the model underspecified?
4. D) Are X and Y significantly related?
5. E) All of the options are correct.

Difficulty: 1 Easy

Topic:  Statistical Evaluation of Regression Models

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

38) Testing the null hypothesis that the slope coefficient is zero uses what sampling distribution for small sample sizes?

1. A) Normal.
2. B) Chi-square.
3. C) t distribution with n-1 degrees of freedom.
4. D) Standard Normal.
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Statistical Evaluation of Regression Models

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

39) Which diagnostic test allows the researcher to claim that her model explains x-percent of the variation in the dependent variable?

1. A) Durbin-Watson test
2. B) Coefficient of Determination
3. C) t-test on slope coefficient
4. D) Sum of squared residuals
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Statistical Evaluation of Regression Models

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

40) Which of the following would indicate a perfect model fit?

1. A) R2= 1
2. B) R2= 0
3. C) Durbin-Watson = 2
4. D) t-test for slope > 2
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Statistical Evaluation of Regression Models

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

41) Consider the following time trend regression model for explaining the behavior of disposable personal income (DPI): DPI = 17,000 + 41(TIME). If the regression standard error were 150, what is an approximate 95% prediction interval for quarter 3 DPI?

1. A) 16,366 to 17,105
2. B) 16,823 to 17,423
3. C) 16,932 to 18,108
4. D) 17,102 to 18,345
5. E) None of the options are correct.

Difficulty: 3 Hard

Topic:  Statistical Evaluation of Regression Models

Learning Objective:  4-04 Discuss the four steps that should be used to evaluate a linear regression model.

42) Serial correlation violates which classical assumption?

1. A) The error terms have a zero mean.
2. B) The error terms follow a normal distribution.
3. C) The error terms are independent of each other.
4. D) The error terms have the same variance.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.

43) Which of the following does not become unreliable when serial correlation is present?

1. A) R-squared
2. B) t-tests
3. C) OLS slope estimates
4. D) Error sum of squares
5. E) None of the options are correct.

Difficulty: 1 Easy

Topic:  Serial Correlation

Learning Objective:  4-06 Explain the difference between the most common kind of correlation (the Pearson product moment correlation) and serial correlation.