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

You conduct a Pearson Product-Moment correlation test to see if two variables are related to each other. Even though the test is significant, you suspect that there actually isn't a real relationship between X and Y, and that they only seem to be related because of their relationship to another variable Z. This is called:     bi-directionality     the third-variable problem     reverse-causality     a causal effect

The results from a research study indicate that adolescents who watch more violent content on television also tend to engage in more violent behavior than their peers. The correlation between amount of TV violent content and amount of violent behavior is an example of Question 25 options: a positive correlation a negative correlation a correlation near zero a correlation near one

If we find that there is a linear correlation between the concentration of carbon dioxide in our atmosphere and the global​ temperature, does that indicate that changes in the concentration of carbon dioxide cause changes in the global​ temperature? Choose the correct answer below.     A. No. The presence of a linear correlation between two variables does not imply that one of the variables is the cause of the other variable.   B. Yes. The presence of a linear correlation between two variables implies that one of the variables is the cause of the other variable.

A Pearson's correlation produces a significant r = -0.32. We can infer that it is a _____ relationship.

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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. A research veterinarian at a major university has developed a new vaccine to protect horses from West Nile virus. An important question is: How predictable is the buildup of antibodies in the horse's blood after the vaccination is given? A large random sample of horses were given the vaccination. The average antibody buildup factor (as determined from blood samples) was measured each week after the vaccination for 8 weeks. Results are shown in the following time series. Original Time Series Week Buildup Factor 2 3 4…