Quant II Final Exam

What is the component of a time series model that is attributable to multiyear cycles in the time series? A. The cyclical component B. The irregular component C. The seasonal component D. The trend

A. The cyclical component

For a(n) _____ , it is impossible to construct a sampling frame. A. infinite population B. defined population C. finite population D. target population

A. infinite population

The sampling procedure being used to collect completion time data is based on: A. pooled samples. B. matched samples. C. cross samples. D. independent samples.

B. matched samples.

The average of the sum of squared forecast errors is called: A. mean absolute error. B. mean squared error. C. mean absolute percentage error. D. residual error.

B. mean squared error.

When developing an interval estimate for the difference between two sample means, with sample sizes of n1 and n2 : A. n1 must be smaller than n2 . B. n1 and n2 can be of different sizes. C. n1 must be equal to n2 . D. n1 must be larger than n2 .

B. n1 and n2 can be of different sizes.

The mathematical equation relating the independent variable to the expected value of the dependent variable, E(y) = B0 + B1x, is known as the: A. correlation equation. B. regression equation. C. regression model. D. estimated regression equation.

B. regression equation.

The difference between the observed value of the dependent variable and the value predicted using the estimated regression equation is called a(n): A. outlier. B. residual. C. point estimate. D. prediction.

B. residual.

The margin of error in an interval estimate of the population mean is a function of all of the following except the: A. sample size. B. sample mean. C. level of significance. D. variability of the population.

B. sample mean.

When "s" is used to estimate "σ," the margin of error is computed by using the: A. mean of the sample. B. t distribution. C. normal distribution. D. mean of the population.

B. t distribution.

All of the following are true about qualitative forecasting methods except: A. they generally involve the use of expert judgment to develop forecasts. B. they assume that the pattern of the past will continue into the future. C. they are appropriate when past data on the variable being forecast are not applicable. D. they are appropriate when past data on the variable being forecast are not available.

B. they assume that the pattern of the past will continue into the future.

A positive forecast error indicates that the forecasting method _____ the dependent variable. A. overestimated B. underestimated C. accurately estimated D. closely approximated

B. underestimated

Which of the following does not need to be known in order to compute the p-value? A. Knowledge of whether the test is one-tailed or two-tailed B. The value of the test statistic C. The level of significance D. The distribution of the data

C. The level of significance

Time series regression refers to the use of regression analysis when the independent variable is: A. profit. B. sales. C. time. D. years.

C. time.

When we use the estimated regression equation to develop an interval that can be used to predict the mean for a specific unit that meets a particular set of given criteria, that interval is called a(n): A. estimation interval. B. confidence interval. C. population interval. D. prediction interval.

D. prediction interval.

Multicollinearity can cause problems if the absolute value of the sample correlation coefficient exceeds: A. .7 for any two of the independent variables. B. .1 for any two of the independent variables. C. .3 for any two of the independent variables. D. .5 for any two of the independent variables.

A. .7 for any two of the independent variables.

What kind of forecasting method is based on the assumption that the variable we are forecasting has a cause-effect relationship with one or more other variables? A. Casual forecasting method B. Time series method C. Qualitative method D. Cyclical method

A. Casual forecasting method

What forecasting method uses a weighted average of past time series values as the forecast; it is a special case of the weighted moving averages method in which we select only one weight—the weight for the most recent observation? A. Exponential smoothing B.Moving averages C. Weighted moving averages D. Time series decomposition

A. Exponential smoothing

What criterion is used in determining which estimated regression equations are best? A. R2 B. p-values C. The F test statistic D. The t test statistic

A. R2

When carrying out an F test to determine if the addition of extra predictor variables results in a significant reduction in the error sum of squares, what are the degrees of freedom of the numerator and denominator of the F statistic? A. The numerator degrees of freedom equals the number of predictors added to the model. The denominator degrees of freedom is n—p—1. B. The numerator degrees of freedom equals the total number of predictors in the model. The denominator degrees of freedom is n—p—1. C. The numerator degrees of freedom equals the number of predictors added to the model. The denominator degrees of freedom is n—1. D. The numerator degrees of freedom equals the total number of predictors in the model. The denominator degrees of freedom is n.

A. The numerator degrees of freedom equals the number of predictors added to the model. The denominator degrees of freedom is n—p—1.

The time series model that is appropriate in situations where the seasonal fluctuations do not depend upon the level of the time series is: A. an additive model. B. a multiplicative model. C. a linear trend model. D. a cyclical model.

A. an additive model.

The multiple regression equation based on the sample data, which has the form of , is called: A. an estimated multiple regression equation. B. a simple linear regression model. C. a multiple regression equation. D. a multiple regression model.

A. an estimated multiple regression equation.

For a lower tail test, the p-value is the probability of obtaining a value for the test statistic: A. at least as small as that provided by the sample. B. at least as large as that provided by the sample. C. at least as small as that provided by the population. D. at least as large as that provided by the population.

A. at least as small as that provided by the sample.

As the test statistic becomes larger, the p-value: A. becomes smaller. B. becomes larger. C. stays the same, since the sample size has not been changed. D. becomes negative.

A. becomes smaller.

The variable selection procedure that identifies the best regression equation, given a specified number of independent variables, is: A. best-subsets regression. B. stepwise regression. C. forward selection. D. backward elimination.

A. best-subsets regression.

To model a time series with a seasonal pattern, we treat the season as a(n): A. categorical variable. B. confounding variable. C. influential observation. D. quantitative variable.

A. categorical variable.

The fact that the sampling distribution of sample means can be approximated by a normal probability distribution whenever the sample size becomes large is based on the: A. central limit theorem. B. fact that we have tables of areas for the normal distribution. C. assumption that the population has a normal distribution. D. None of these alternatives is correct.

A. central limit theorem.

A variable used to model the effect of categorical independent variables is called a(n): A. dummy variable. B. explanatory variable. C. quantitative variable. D. categorical variable.

A. dummy variable.

For a fixed confidence level and population standard deviation, if we would like to cut our margin of error in half, we should take a sample size that is: A. four times as large as the original sample size. B. three times as large as the original sample size. C. twice as large as the original sample size. D. nine times as large as the original sample size.

A. four times as large as the original sample size.

When using a weighted moving average, if we believe that the recent past is a better predictor of the future than the distant past, we should: A. give larger weights to recent observations. B. give smaller weights to recent observations. C. give equal weights to all observations. D. give alternating large and small weights to consecutive observations.

A. give larger weights to recent observations.

In a multiple regression model, the values of the error term, ε, are assumed to be: A. independent of each other. B. zero. C. always negative. D. dependent on each other.

A. independent of each other.

The probability of making a Type I error when the null hypothesis is true as an equality is called the: A. level of significance. B. possibility of error. C. critical value. D. power.

A. level of significance.

The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is called the: A. multiple coefficient of determination. B. correlation. C. slope of the least squares regression line. D. error term.

A. multiple coefficient of determination.

The study of how a dependent variable y is related to two or more independent variables is called: A. multiple regression analysis. B. least significant difference analysis. C. factorial design analysis. D. linear regression analysis.

A. multiple regression analysis.

A seasonal pattern: A. occurs when a time series plot exhibits a repeating pattern over successive periods. B. often takes more than a year to repeat itself. C. is a multi-year run of observations above and below the trend line. D. reflects a shift in the time series over time.

A. occurs when a time series plot exhibits a repeating pattern over successive periods.

When we conduct significance tests for a multiple regression relationship, the F test will be used as the test for: A. overall significance. B. complete significance. C. pairwise significance. D. individual significance.

A. overall significance.

When studying the relationship between two quantitative variables, whenever we want to predict an individual value of y for a new observation corresponding to a given value of x, we should use a(n): A. prediction interval. B. confidence interval. C. determination interval. D. estimation interval.

A. prediction interval.

The sampling distribution of is the: A. probability distribution of all possible values of the sample proportion. B. mean of the population. C. probability distribution of all possible values of the sample mean. D. mean of the sample.

A. probability distribution of all possible values of the sample proportion.

The standard deviation of a point estimator is called the: A. standard error. B. variance of estimation. C. standard deviation. D. point estimator.

A. standard error.

When determining the best estimated regression equation to model a set of data, the procedure that allows an independent variable to enter the model at one step, be removed at a subsequent step, and then enter the model at a later step is: A. stepwise regression. B. backward elimination. C. best-subsets regression. D. forward selection.

A. stepwise regression.

When determining the best estimated regression equation to model a set of data, the procedure that begins each step by determining whether any of the variables already in the model should be removed is called: A. stepwise regression. B. backward elimination. C. best-subsets regression. D. forward selection.

A. stepwise regression.

In a regression analysis, an outlier will always increase: A. the value of the correlation. B. the slope of the regression line. C. the ability to predict y with precision. D. the y-intercept.

A. the value of the correlation.

Suppose a high correlation existed between variables x 1 and x 2 . If variable x 1 was used as an independent variable, then variable x 2 : A. would not add much more explanatory power to the current model. B. could strongly influence the direction of the current model. C. might contradict the trend found within the current model. D. could possibly reverse the trend of the current model.

A. would not add much more explanatory power to the current model.

In a regression analysis, the error term ε is a random variable with a mean or expected value of A. zero. B. one. C. any positive value. D. any value.

A. zero.

The sample statistic characteristic s is the point estimator of: A. σ. B. π. C. μ. D. ρ.

A. σ.

What kind of model is appropriate if the seasonal fluctuations change over time, growing larger as the dependent variable increases because of a long-term linear trend? A. An additive model B. A multiplicative model C. A linear trend model D. A cyclical model

B. A multiplicative model

Which of the following scenarios follows a matched sample design? A. A farmer tracks his sales of red and green apples to see which is preferred by his customers. B. A teacher uses a pretest and then a posttest with her students to see how much they have improved. C. A dietitian had 50 clients follow a calorie-counting diet and another 50 clients follow a low-carb diet to see which is more effective for weight loss. D. A company looks at the satisfaction of men and women to see which gender is more satisfied with the current work conditions.

B. A teacher uses a pretest and then a posttest with her students to see how much they have improved.

Three of the following forecasting methods are appropriate for a time series with a horizontal pattern. Which one is not appropriate for a time series with a horizontal pattern? A. Exponential smoothing B. Linear trend regression C. Moving averages D. Weighted moving averages

B. Linear trend regression

Which of the following is not present in a time series? A. Seasonality B. Operational variations C. Trend D. Cycles

B. Operational variations

When historical data on the variable being forecast are either not applicable or unavailable, what kind of forecasting method should be used? A. Quantitative methods B. Qualitative methods C. Either quantitative or qualitative methods D. Both quantitative and qualitative methods

B. Qualitative methods

If data for a time series analysis is collected on an annual basis only, which component may be ignored? A. Trend B. Seasonal C. Cyclical D. Irregular

B. Seasonal

How many independent variables does the forward selection process start with? A. p B. Zero C. One D. Two

B. Zero

When working with regression analysis, an outlier is: A. any value that falls more than 1.5(IQR) above Q3 or below Q1 B. any observation that does not fit the trend shown by the remaining data. C. any value that has a small residual. D. any observation that is extreme in the x direction.

B. any observation that does not fit the trend shown by the remaining data.

Correlation in the errors that arises when the error terms at successive points in time are related is called: A. multicollinearity. B. autocorrelation. C. interaction. D. extrapolation.

B. autocorrelation.

As the number of degrees of freedom for a t distribution increases, the difference between the t distribution and the standard normal distribution: A. stays the same. B. becomes smaller. C. becomes larger. D. fluctuates.

B. becomes smaller.

When the level of confidence decreases, the margin of error: A. becomes larger. B. becomes smaller. C. stays the same. D. becomes smaller or larger, depending on the sample size.

B. becomes smaller.

The value of the coefficient of correlation (r): A. can never be equal to the value of the coefficient of determination (r2). B. can be equal to the value of the coefficient of determination (r2). C. is always larger than the value of the coefficient of determination. D. is always smaller than the value of the coefficient of determination.

B. can be equal to the value of the coefficient of determination (r2).

When we use the estimated regression equation to develop an interval that can be used to predict the mean for ALL units that meet a particular set of given criteria, that interval is called a(n): A. estimation interval. B. confidence interval. C. population interval. D. prediction interval.

B. confidence interval.

As the sample size increases, the margin of error: A. stays the same. B. decreases. C. increases. D. increases or decreases depending on the value of the sample mean.

B. decreases.

In regression analysis, the variable that is being predicted is the: A. independent variable. B. dependent variable. C. random variable. D. confounding variable.

B. dependent variable.

A time series from which the effect of season has been removed by dividing each original time series observation by the corresponding seasonal index is called a: A. cyclical pattern. B. deseasonalized time series. C. moving average. D. time series decomposition.

B. deseasonalized time series.

If the value of y in time period t is related to its value in time period t - 1, we say that: A. first-order interaction is present. B. first-order autocorrelation is present. C. second-order autocorrelation is present. D. interaction is present.

B. first-order autocorrelation is present.

The difference between the actual time series value and the forecast is called: A. extrapolation error. B. forecast error. C. mean absolute error. D. residual error.

B. forecast error.

We can reduce the margin of error in an interval estimate of p by doing any of the following except: A. increasing the level of significance. B. increasing the planning value p* to .5. C. increasing the sample size. D. reducing the confidence coefficient.

B. increasing the planning value p* to .5.

The tests of significance in regression analysis are based on assumptions about the error term ɛ. One such assumption is that the values of ɛ are: A. categorical. B. independent. C. limited. D. uniformly distributed.

B. independent.

When using a categorical variable in a multiple regression model that has k levels, how many dummy variables are needed? A. k B. k - 1 C. k - 2 D. n - 1

B. k - 1

The probability that the interval estimation procedure will generate an interval that does not contain µ is known as the: A. confidence level. B. level of significance. C. confidence coefficient. D. margin of error.

B. level of significance.

If the historical data are restricted to past values of the variable to be forecast, the forecasting procedure is called a: A. casual forecasting method. B. time series method. C. qualitative method. D. cyclical method.

B. time series method.

For a two-tailed test, the p-value is the probability of obtaining a value for the test statistic as: A. likely as that provided by the sample. B. unlikely as that provided by the sample. C. likely as that provided by the population. D. unlikely as that provided by the population.

B. unlikely as that provided by the sample.

What is the probability of making a Type I error? A. σ B. 𝛼 C. .01 D. .05

B. 𝛼

Which of the following is not an iterative variable selection procedure? A. Stepwise regression B. Backward elimination C. Best-subsets regression D. Forward selection

C. Best-subsets regression

Which of the following options guarantees that the best model for a given number of variables will be found? A. Stepwise regression B. Backward elimination C. Best-subsets regression D. Forward selection

C. Best-subsets regression

Which of the following statements about the backward elimination procedure is false? A. It does not guarantee that the best regression model will be found. B. It is an iterative procedure. C. It begins with zero independent variables. D. It does not permit an independent variable to be reentered once it has been removed.

C. It begins with zero independent variables.

What type of analysis aims to discover a pattern in the historical data or time series and then extrapolate the pattern into the future? A. Causal analysis B. Cyclical analysis C. Time series analysis D. Qualitative analysis

C. Time series analysis

Which of the following set of circumstances is not needed to carry out quantitative forecasting methods? A. When past information about the variable being forecast is available. B. When the information can be quantified. C. When the historical data has a correlation of 1. D. When it is reasonable to assume that the pattern of the past will continue into the future.

C. When the historical data has a correlation of 1.

In interval estimation, as the sample size becomes larger, the interval estimate: A. remains the same, since the mean is not changing. B. becomes wider. C. becomes narrower. D. gets closer to 1.96.

C. becomes narrower.

If the coefficient of determination is a positive value, then the coefficient of correlation: A. must also be positive. B. must be zero. C. can be either negative or positive. D. must be larger than 1.

C. can be either negative or positive.

In multiple regression analysis, the general linear model: A. must contain more than two dependent variables. B. cannot be used to accommodate curvilinear relationships between the dependent variable and the independent variables. C. can be used to accommodate curvilinear relationships between the independent variables and the dependent variable. D. must contain more than two independent variables.

C. can be used to accommodate curvilinear relationships between the independent variables and the dependent variable.

Whenever the probability of making a Type II error has not been determined and controlled, only two conclusions are possible. We either reject H0 or: A. accept H0. B. reject Ha . C. do not reject H0. D. do not reject Ha .

C. do not reject H0.

A simple random sample of size n from an infinite population is a sample selected such that: A. each element has a .5 probability of being selected. B. the probability of being selected changes. C. each element is selected independently and is selected from the same population. D. each element has a probability of at least .5 of being selected.

C. each element is selected independently and is selected from the same population.

In general, higher confidence levels provide larger confidence intervals. One way to have high confidence and a small margin of error is to: A. be more clear in the way the question is phrased. B. take a different sample. C. increase the sample size. D. have a third party to conduct the survey for you.

C. increase the sample size.

When drawing a sample from a population, the goal is for the sample to: A. include some of the targeted population. B. be more varied than the targeted population. C. match the targeted population. D. be smaller than the targeted population.

C. match the targeted population.

The average of the absolute values of the forecast errors is called: A. extrapolation error. B. forecast error. C. mean absolute error. D. residual error.

C. mean absolute error.

The method that uses the average of the most recent k data values in the time series as the forecast for the next period is called: A. exponential smoothing. B. linear trend regression. C. moving averages. D. weighted moving averages.

C. moving averages

For a fixed sample size, n, in order to have a higher degree of confidence, the margin of error and the width of the interval: A. must remain the same. B. must be smaller. C. must be larger. D. are unaffected by the confidence level.

C. must be larger.

A forecast model of the form Tt= b0+b1t+b2t^2 is called a(n): A. exponential trend equation. B. linear trend equation. C. quadratic trend equation. D. logarithmic trend equation.

C. quadratic trend equation.

Doubling the size of the sample will: A. increase the standard error of the mean. B. double the standard error of the mean. C. reduce the standard error of the mean. D. have no effect on the standard error of the mean.

C. reduce the standard error of the mean.

n regression analysis, the equation in the form y = 𝛽0 + 𝛽1x + ε is called the: A. correlation equation. B. regression equation. C. regression model. D. estimated regression equation.

C. regression model.

Since the multiple regression equation generates a plane or surface, its graph is called a: A. dependent variable graph. B. dependent variable plane. C. response surface. D. response plane.

C. response surface.

The average of all the historical data will always provide the best results as long as the underlying time series is: A. increasing. B. decreasing. C. stationary. D. cyclical.

C. stationary.

A simple random sample of size n from an infinite population of size N is to be selected. Each possible sample should have: A. a probability of 1/n of being selected. B. a probability of N/n of being selected. C. the same probability of being selected. D. a probability of 1/N of being selected.

C. the same probability of being selected.

The distribution of values taken by a statistic in all possible samples of the same size from the same population is the sampling distribution of: A. the population. B. the target population. C. the sample. D. the distribution.

C. the sample.

In multiple regression analysis: A. the coefficient of determination must be larger than 1. B. there can be any number of dependent variables, but only one independent variable. C. there can be several independent variables, but only one dependent variable. D. there must be only one independent variable.

C. there can be several independent variables, but only one dependent variable.

The sample mean is the point estimator of: A. σ. B. π. C. μ. D. ρ.

C. μ.

Which of these best describes a sampling distribution of a statistic? A. It is the probability distribution of the values of a statistic that are contained in all possible samples of the same sample size. B. It is the histogram of sample statistics from all possible samples of the same sample size. C. It is the probability that the sample statistic equals the parameter of interest. D. It is the distribution of all of the statistics calculated from all possible samples of the same sample size.

D. It is the distribution of all of the statistics calculated from all possible samples of the same sample size.

When autocorrelation is present, which of the following assumptions is violated? A. The data is obtained from a randomized sample. B. The variance is constant. C. The error terms are normally distributed. D. The error terms are independent.

D. The error terms are independent.

What forecasting method involves selecting a different weight for the most recent k data values in the time series and then computing a weighted average of the values? A. Multiplicative decomposition B. Time series decomposition C. Moving average method D. Weighted moving average method

D. Weighted moving average method

If two large independent random samples are taken from two populations, the sampling distribution of the difference between the two sample means: A. will have a variance of one. B. will have a mean of one. C. can be approximated by a binomial distribution. D. can be approximated by a normal distribution.

D. can be approximated by a normal distribution.

The central limit theorem is important in Statistics because it: A. guarantees that when it is applied, the samples that are drawn are always randomly selected. B. tells us that if several samples have produced sample averages, which seem to be different than expected, the next sample average will likely be close to its expected value. C. tells us that large samples do not need to be selected. D. enables reasonably accurate probabilities to be determined for events involving the sample average when the sample size is large regardless of the distribution of the variable.

D. enables reasonably accurate probabilities to be determined for events involving the sample average when the sample size is large regardless of the distribution of the variable.

The model developed from sample data that has the form y^ = b0+b1x is known as the: A. correlation equation. B. regression equation. C. regression model. D. estimated regression equation.

D. estimated regression equation.

If a significant relationship exists between x and y and the coefficient of determination shows that the fit is good, the estimated regression equation should be useful for: A. extrapolation. B. determining nonresponse error. C. determining cause and effect. D. estimation and prediction.

D. estimation and prediction.

Looking at the sample correlation coefficients between the response variable and each of the independent variables can give us a quick indication of which independent variables are, by themselves, A. statistically significant dependent variables for the regression model. B. outliers. C. influential observations. D. good predictors.

D. good predictors.

Time series decomposition can be used to separate or decompose a time series into all of the following components except: A. irregular components. B. seasonal components. C. trend. D. horizontal patterns.

D. horizontal patterns.

In general, R2 always _____ as independent variables are added to the regression model. A. stays the same B. decreases C. increases or decreases depending on how the variables relate to the response variable. D. increases

D. increases

Regarding inferences about the difference between two population means, the alternative to the matched sample design, as covered in the textbook, is: A. pooled samples. B. mutually exclusive samples. C. cross samples. D. independent samples.

D. independent samples.

When we conduct significance tests for a multiple regression relationship, the t test can be conducted for each of the independent variables in the model. Each of those tests are called tests for: A. overall significance. B. complete significance. C. pairwise significance. D. individual significance.

D. individual significance.

An observation that has a strong influence or effect on the regression results is called a(n): A. mistake. B. outlier. C. residual. D. influential observation.

D. influential observation.

If a categorical variable has k levels, then: A. k + 1 dummy variables are needed. B. k dummy variables are needed. C. n dummy variables are needed. D. k - 1 dummy variables are needed.

D. k - 1 dummy variables are needed.

The method used to develop the estimated regression equation that minimizes the sum of squared residuals is called the: A. linear regression model. B. least significant difference method. C. multiple regression technique. D. least squares method.

D. least squares method.

For a fixed confidence level and population standard deviation, if we would like to cut our margin of error to 1/3 of the original size, we should take a sample size that is: A. four times as large as the original sample size. B. three times as large as the original sample size. C. twice as large as the original sample size. D. nine times as large as the original sample size.

D. nine times as large as the original sample size.

The tests of significance in regression analysis are based on assumptions about the error term ɛ. One such assumption is that the error term follows ɛ a(n) _____ distribution for all values of x. A. exponential B. binomial C. uniform D. normal

D. normal

In a multiple regression model, the values of the error term, ε, are assumed to be: A. skewed to the right. B. uniformly distributed. C. skewed to the left. D. normally distributed.

D. normally distributed.

The parameters of nonlinear models have exponents: A. other than three. B. other than zero. C. other than two. D. other than one.

D. other than one.

When completing a two-tailed hypothesis test about the difference between two population means, the A. test statistic must be doubled. B. sample sizes must be added. C. samples must be of the same size. D. p-value must be doubled.

D. p-value must be doubled.

All things held constant, which interval will be wider: a confidence interval or a prediction interval? A. The confidence interval and the prediction interval will have the same width. B. confidence interval C. It cannot be determined from the information given. D. prediction interval

D. prediction interval

Which of the following is a point estimator? A. μ B. p C. σ D. s

D. s

Which of the following is not a symbol for a parameter? A. ρ B. σ C. μ D. s

D. s

The distribution of values taken by a statistic in all possible samples of the same size from the same population is called a: A. population parameter. B. distribution of interest. C. sample parameter. D. sampling distribution.

D. sampling distribution.

An F test, based on the F probability distribution, can be used to test for: A. equality of two population proportions. B. equality of the means of two populations. C. significance in regression. D. significance in the relationship between two categorical variables.

D. significance in the relationship

Applications of hypothesis testing that only control for the Type I error are called: A. level of significance. B. research tests. C. assumption of calculations. D. significance tests.

D. significance tests.

As the sample size increases, the: A. population mean increases. B. standard error of the mean increases. C. standard deviation of the population decreases. D. standard error of the mean decreases.

D. standard error of the mean decreases.

When constructing a confidence or a prediction interval to quantify the relationship between two quantitative variables, what distribution do confidence and prediction intervals follow? A. Normal distribution B. Chi-Square distribution C. Uniform distribution D. t distribution

D. t distribution

The tests of significance in regression analysis are based on assumptions about the error term ɛ . One such assumption is that the variance of ɛ, denoted by 𝝈2, is: A. less as x increases. B. greater as x increases. C. unrelated to the value of x. D. the same for all values of x.

D. the same for all values of x.

A time series method that is used to separate or decompose a time series into seasonal, trend, and irregular components is called: A. cyclical pattern. B. deseasonalized time series. C. moving averages. D. time series decomposition.

D. time series decomposition.

In the linear trend equation, Tt = b0 + b1t, b0 represents the: A. time, in years. B. slope of the trend line. C. trend value in period 1. D. y- intercept of the trend line.

D. y- intercept of the trend line.

When studying the relationship between two quantitative variables, an interval estimate of the mean value of y for a given value of x is called a(n): a. prediction interval. b. confidence interval. c. determination interval. d. estimation interval.

b. confidence interval.