A quadratic trend equation yt = 900 + 80t - 5t2 was fitted to a company's sales. This result implies that the sales trend:

A) hit a peak in period 8. B) hit a peak in period 5. C) hit a trough in period 8. D) hit a trough in period 5. A) hit a peak in period 8. B) hit a peak in period 5. C) hit a trough in period 8. D) hit a trough in period 5.

Exam 14: Time Series Analysis

A quadratic trend equation was estimated from monthly sales of trucks in the United States from July 2006 to July 2011. The estimated trend is y_t = 106 + 1.03t + 0.048t², where y_t units are in thousands. From this trend, how many trucks would be sold in July 2012?

(Multiple Choice)

4.7/5

(39)

Question 101

Increasing the smoothing constant α increases the weight assigned to the most recent observation.

(True/False)

4.9/5

(37)

Question 102

The shape of the fitted exponential model y_t = 256e^-.07^t is always declining.

(True/False)

4.7/5

(35)

Question 103

Regression analysis can be used for forecasting monthly time-series data using a trend variable and 11 binary predictors (one for each month except omitting one month).

(True/False)

4.9/5

(40)

Question 104

A trend line has been fitted to a company's annual sales. The trend is given by y_t = 50 + 5t, where t is the time index (t = 1, 2, …, n) and y_t is annual sales (in millions of dollars). The implication of this trend line is:

(Multiple Choice)

4.9/5

(37)

Question 105

Using the first observed data value is a common way of initializing the forecasts in the exponential smoothing model.

(True/False)

4.9/5

(40)

Question 106

Which is a time series?

(Multiple Choice)

4.8/5

(42)

Question 107

If the fitted annual trend for a stock price is y_t = 27e^0.213^t, then:

(Multiple Choice)

4.8/5

(36)

Question 108

A quadratic trend equation y_t = 900 + 80t - 5t² was fitted to a company's sales. This result implies that the sales trend:

(Multiple Choice)

4.8/5

(32)

Question 109

The shape of the fitted quadratic model y_t = 324 - 42t - 1.3t² is declining, then rising.

(True/False)

4.9/5

(42)

Question 110

The fitted annual sales trend is Y_t = 187.3e^-.047^t. On average, sales are:

(Multiple Choice)

4.8/5

(34)

Question 111

If we fit a linear trend to 10 observations on time-series data that are growing exponentially, then it is most likely that:

(Multiple Choice)

4.8/5

(43)

Question 112

Moving averages are most useful for irregular data with no clear trend.

(True/False)

4.8/5

(36)

Question 113

The shape of the fitted exponential model y_t = 256e^-.07^t is rising at first, then declining.

(True/False)

4.8/5

(31)

Question 114

Showing 101 - 114 of 114

A quadratic trend equation was estimated from monthly sales of trucks in the United States from July 2006 to July 2011. The estimated trend is y_t = 106 + 1.03t + 0.048t², where y_t units are in thousands. From this trend, how many trucks would be sold in July 2012?

Increasing the smoothing constant α increases the weight assigned to the most recent observation.

The shape of the fitted exponential model y_t = 256e^-.07^t is always declining.

Regression analysis can be used for forecasting monthly time-series data using a trend variable and 11 binary predictors (one for each month except omitting one month).

A trend line has been fitted to a company's annual sales. The trend is given by y_t = 50 + 5t, where t is the time index (t = 1, 2, …, n) and y_t is annual sales (in millions of dollars). The implication of this trend line is:

Using the first observed data value is a common way of initializing the forecasts in the exponential smoothing model.

Which is a time series?

If the fitted annual trend for a stock price is y_t = 27e^0.213^t, then:

A quadratic trend equation y_t = 900 + 80t - 5t² was fitted to a company's sales. This result implies that the sales trend:

The shape of the fitted quadratic model y_t = 324 - 42t - 1.3t² is declining, then rising.

The fitted annual sales trend is Y_t = 187.3e^-.047^t. On average, sales are:

If we fit a linear trend to 10 observations on time-series data that are growing exponentially, then it is most likely that:

Moving averages are most useful for irregular data with no clear trend.

The shape of the fitted exponential model y_t = 256e^-.07^t is rising at first, then declining.

Overview of Statistics

Data Collection

Describing Data Visually

Descriptive Statistics

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions and Estimation

One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests

Analysis of Variance

Simple Regression

Multiple Regression

Chi-Square Tests

Nonparametric Tests

Quality Management

Filters

Exam 14: Time Series Analysis

A quadratic trend equation was estimated from monthly sales of trucks in the United States from July 2006 to July 2011. The estimated trend is yt = 106 + 1.03t + 0.048t2, where yt units are in thousands. From this trend, how many trucks would be sold in July 2012?

Increasing the smoothing constant α increases the weight assigned to the most recent observation.

The shape of the fitted exponential model yt = 256e-.07t is always declining.

Regression analysis can be used for forecasting monthly time-series data using a trend variable and 11 binary predictors (one for each month except omitting one month).

A trend line has been fitted to a company's annual sales. The trend is given by yt = 50 + 5t, where t is the time index (t = 1, 2, …, n) and yt is annual sales (in millions of dollars). The implication of this trend line is:

Using the first observed data value is a common way of initializing the forecasts in the exponential smoothing model.

Which is a time series?

If the fitted annual trend for a stock price is yt = 27e0.213t, then:

A quadratic trend equation yt = 900 + 80t - 5t2 was fitted to a company's sales. This result implies that the sales trend:

The shape of the fitted quadratic model yt = 324 - 42t - 1.3t2 is declining, then rising.

The fitted annual sales trend is Yt = 187.3e-.047t. On average, sales are:

If we fit a linear trend to 10 observations on time-series data that are growing exponentially, then it is most likely that:

Moving averages are most useful for irregular data with no clear trend.

The shape of the fitted exponential model yt = 256e-.07t is rising at first, then declining.

Overview of Statistics

Data Collection

Describing Data Visually

Descriptive Statistics

Probability

Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions and Estimation

One-Sample Hypothesis Tests

Two-Sample Hypothesis Tests

Analysis of Variance

Simple Regression

Multiple Regression

Chi-Square Tests

Nonparametric Tests

Quality Management

Filters

A quadratic trend equation was estimated from monthly sales of trucks in the United States from July 2006 to July 2011. The estimated trend is y_t = 106 + 1.03t + 0.048t², where y_t units are in thousands. From this trend, how many trucks would be sold in July 2012?

The shape of the fitted exponential model y_t = 256e^-.07^t is always declining.

A trend line has been fitted to a company's annual sales. The trend is given by y_t = 50 + 5t, where t is the time index (t = 1, 2, …, n) and y_t is annual sales (in millions of dollars). The implication of this trend line is:

If the fitted annual trend for a stock price is y_t = 27e^0.213^t, then:

A quadratic trend equation y_t = 900 + 80t - 5t² was fitted to a company's sales. This result implies that the sales trend:

The shape of the fitted quadratic model y_t = 324 - 42t - 1.3t² is declining, then rising.

The fitted annual sales trend is Y_t = 187.3e^-.047^t. On average, sales are:

The shape of the fitted exponential model y_t = 256e^-.07^t is rising at first, then declining.