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2005-0.76 trillion 2006-0.78 trillion 2007-0.79 trillion 2008-0.85 trillion 2009-0.89 trillion 2010-0.94 trillion 2011- 1.03 trillion 2012-1.13 trillion 2013-1.2 trillion 2014-11.3 trillion 2015-1.38 trillion 2016- 1.46trillion
2005-0.76 trillion 2006-0.78 trillion 2007-0.79 trillion 2008-0.85 trillion 2009-0.89 trillion 2010-0.94 trillion 2011- 1.03 trillion 2012-1.13 trillion 2013-1.2 trillion 2014-11.3 trillion 2015-1.38 trillion 2016- 1.46trillion 2017-1.57 trillion 2018- 1.67 trillion 2019- 1.69 trillion 2020- 1.87 trillion The datas of spending Money on digital pay Normality tests for your data. Point estimations and confidence intervals. Define 2-3 hypotheses and test for them. These may be one sample or two sample hypothesis tests. (Isthe average height of basketball players > 2 meters? Are shorter football players' average incomes higher compared to taller players? How about the standard deviation of incomes? etc...) Goodness of fit tests and other checks for detecting the distribution. Design a linear regression model for your problem. Input, output analysis. Remember some data could be linear when transformed such as after taking the logarithm. ANOVA Application of nonparametric tests That's all have to be in r studio with code. 2005-0.76 trillion 2006-0.78 trillion 2007-0.79 trillion 2008-0.85 trillion 2009-0.89 trillion 2010-0.94 trillion 2011- 1.03 trillion 2012-1.13 trillion 2013-1.2 trillion 2014-11.3 trillion 2015-1.38 trillion 2016- 1.46trillion 2017-1.57 trillion 2018- 1.67 trillion 2019- 1.69 trillion 2020- 1.87 trillion The datas of spending Money on digital pay Normality tests for your data. Point estimations and confidence intervals. Define 2-3 hypotheses and test for them. These may be one sample or two sample hypothesis tests. (Isthe average height of basketball players > 2 meters? Are shorter football players' average incomes higher compared to taller players? How about the standard deviation of incomes? etc...) Goodness of fit tests and other checks for detecting the distribution. Design a linear regression model for your problem. Input, output analysis. Remember some data could be linear when transformed such as after taking the logarithm. ANOVA Application of nonparametric tests That's all have to be in r studio with code
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