We are using the average bonuses paid to workers on Wall Street from 2000 to 2016 to fit different auto-regressive models. The data is provided below: Year Bonus($000) 2000 100.5 2001 74.1 2002 60.9 2003 99.9 2004 113.5 2005 149.8 2006 191.4 2007 177.8 2008 100.9 2009 140.6 2010 139.0 2011 111.4 2012 142.9 2013 169.8 2014 160.3 2015 136.8 2016 138.2 a. fit a third-order autoregressive model to the bonuses paid and test for the significance of the third-order autoregressive parameter (slope) (Use a = 0.05.) b. if necessary, fit a second-order autoregressive model to the bonuses paid and test for the significance of the second-order autoregressive parameter (slope). (Use a = 0.05.) c. if necessary, fit a first-order autoregressive model to the bonuses paid and test for the significance of the first-order autoregressive parameter (slope). (Use a = 0.05.) d. if appropriate, forecast the bonuses paid in 2017 and 2018.
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
- We are using the average bonuses paid to workers on Wall Street from 2000 to 2016 to fit different auto-regressive models. The data is provided below:
|
Year |
Bonus($000) |
|
2000 |
100.5 |
|
2001 |
74.1 |
|
2002 |
60.9 |
|
2003 |
99.9 |
|
2004 |
113.5 |
|
2005 |
149.8 |
|
2006 |
191.4 |
|
2007 |
177.8 |
|
2008 |
100.9 |
|
2009 |
140.6 |
|
2010 |
139.0 |
|
2011 |
111.4 |
|
2012 |
142.9 |
|
2013 |
169.8 |
|
2014 |
160.3 |
|
2015 |
136.8 |
|
2016 |
138.2 |
a. fit a third-order autoregressive model to the bonuses paid and test for the significance of the third-order autoregressive parameter (slope) (Use a = 0.05.)
b. if necessary, fit a second-order autoregressive model to the bonuses paid and test for the significance of the second-order autoregressive parameter (slope). (Use a = 0.05.)
c. if necessary, fit a first-order autoregressive model to the bonuses paid and test for the significance of the first-order autoregressive parameter (slope). (Use a = 0.05.)
d. if appropriate, forecast the bonuses paid in 2017 and 2018.
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