2 page summary based on 16 regression analysis (GDP data is provided)

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5
Journal Article
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Author Note
Abstract:
This paper intends to apply multiple regression analysis, using the US economy data. To do this analysis, the paper selects real GDP as dependent variable while CPI, share prices and inflation as independent variables. After conducting multiple regression analyses, the value of R-square, coefficients and standard error can be described. It can be stated that the regression equation become more accurate after applying this recursive technique. 
Introduction:
This paper intends to apply multiple regression analysis, using the US economy data. Through using this data set, one can understand the impact of consumer price index (CPI), share price and inflation on real GDP within the year 1990 to 2016 (Toosi, Fernando and Dekkers 2018). In following section, the report will discuss method and outcomes for further discussion. 
Method:
The report conducts multiple regression analysis to obtain the desired outcome. To do this analysis, the paper selects real GDP as dependent variable while CPI, share prices and inflation as independent variables. The paper runs 16 regression based on recursive least squares (RLS) method. Hence, the first regression analysis has been done by taking the value from 1990 to 2001 (Cohen, West and Aiken 2014). After that, an extra year is added with the previous data set in order to modify the previous estimation. From these regression analyses, the report obtains the value of coefficients, Se and R-square.
Result:
Table 1: Multiple Regression Analysis from 1990-2001
Table 2: Multiple Regression Analysis from 1990-2002
Table 3: Multiple Regression Analysis from 1990-2003
Table 4: Multiple Regression Analysis from 1990-2004
Table 5: Multiple Regression Analysis from 1990-2005
Table 6: Multiple Regression Analysis from 1990-2006
Table 7: Multiple Regression Analysis from 1990-2007
Table 8: Multiple Regression Analysis from 1990-2008
Table 9: Multiple Regression Analysis from 1990-2009
Table 10: Multiple Regression Analysis from 1990-2010
Table 11: Multiple Regression Analysis from 1990-2011
Table 12: Table 11: Multiple Regression Analysis from 1990-2012
Table 13: Multiple Regression Analysis from 1990-2013
Table 14: Multiple Regression Analysis from 1990-2014
Table 15: Multiple Regression Analysis from 1990-2015
Table 16:

2 page summary based on 16 regression analysis (GDP data is provided)

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