Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by Morningstar. An Internet advertising agency is studying the number of "hits" on a certain web site during an advertising campaign. It is hoped that as the campaign progresses, the number of hits on the web site will also increase in a predictable way from one day to the next. For 10 days of the campaign, the number of hits × 105 is shown. Original Time Series Day 1 2 3 4 5 6 7 8 9 10 Hits × 105 1.3 3.5 4.4 7.2 6.8 8.3 9.0 11.3 13.1 14.7  Test ρ > 0 at the 1% level of significance. (Use 2 decimal places.) t

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Serial correlation, also known as autocorrelation, describes the extent to which the result in one period of a time series is related to the result in the next period. A time series with high serial correlation is said to be very predictable from one period to the next. If the serial correlation is low (or near zero), the time series is considered to be much less predictable. For more information about serial correlation, see the book Ibbotson SBBI published by Morningstar.

An Internet advertising agency is studying the number of "hits" on a certain web site during an advertising campaign. It is hoped that as the campaign progresses, the number of hits on the web site will also increase in a predictable way from one day to the next. For 10 days of the campaign, the number of

hits × 105

is shown.

Original Time Series
Day 1 2 3 4 5 6 7 8 9 10
Hits × 105 1.3 3.5 4.4 7.2 6.8 8.3 9.0 11.3 13.1 14.7
 
Test
ρ > 0
at the 1% level of significance. (Use 2 decimal places.)
t  
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