Statistics 1-Unit 2

Which of the following examples is a time series problem?

• Estimating the number of hotel rooms booked in the next 6 months.

• Estimating the number of calls for the next week.

• Estimating the total sales in the next 3 years of an insurance company.

• All of them.

An overall upward or downward pattern in an annual time series, In which component of the times series would be contained?

• Trend

• Cyclical

• Irregular

• Seasonal

Tags

CCSS.6.SP.B.5C

CCSS.HSS.ID.A.3

This time series plot contains ....

• Cyclical variations

• Seasonal variations

• Cyclical and seasonal variations

• None of them

Tags

CCSS.6.SP.B.5C

CCSS.HSS.ID.A.3

Seasonal differences are expressed in percentage.

• True

• False

The three first seasonal indexes of the quarterly expenses in clothes and shoes are: I1=83%, I2=103%, I3=60%. Then, I4=?

• 54%

• 154%

• 82%

• 246%

The three last seasonal differences of quarterly time series are: d2=0.25, d3=0.75, d4=-0.5. Then, d1=?

• 0.5

• 0

• -0.5

• 1

If the seasonal difference relating to a time series is -1.2, then:

• This is impossible because seasonal differences are always positive.

• The variable decreases by 20 percent due to the seasonal component.

• The variable decreases by 2 units due to the seasonal component.

• The variable decreases by 1.2 units due to the seasonal component.

The trend line of a quarterly time series is: T(t)=2(t-2015)+1. Then, the trend value in the third quarter of 2020 is:

• 11.25

• 11

• 11.5

• 10.75

The estimated trend component for the 2nd quarter of a time series is: 2.25. SD are: d1=0.25, d2=0.75, d3=d4=-0.5. Then,

• The forecast for the second quarter is 3

• The forecast for the second quarter is 2.25

• The forecast for the second quarter is 1.75

• The forecast for the second quarter is 2.5

Assuming a multiplicative model, the seasonal component of a time series is removed by:

• Dividing each time series data by the corresponding seasonal difference

• Subtracting the corresponding seasonal difference to each time series data

• Dividing each time series data by the corresponding seasonal index.

• Subtracting the corresponding seasonal index to each time series data