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**URGENT** due in a few hours. Please do all of the parts. Directions are in the picture

Since 2014, the United Nations has conducted annual studies that measure the level of happiness among its member countries. Experts in social science and psychology are commissioned to collect relevant data and define measurements related to happiness. Happiness measurements are based on survey questions such as how people feel about their life (i.e. life ladders) levels of positive and negative emotion, freedom to make choices (life choices) and aggregated indicators such as social support, life expectancy, and relative household income. These data are converted into numerical scores for each member country. The accompanying table shows a portion of the United Nation's happiness data.

Country Life Ladder Social Support   Household Income
Albania 4.4623 0.5582 0.3317
Argentina 6.5133 0.8922 0.2726
       
Zimbabwe 3.7570 0.8534 0.5242
 
a. Conduct principal component analysis on all the variables except the Country variable. Should you standardize the data?
  ◯ Yes
  ◯ No
 
b. What percent of the total variability is accounted for by the first principal component? How many principal components must be retained in order to account for at least 80% of the total variance in the data? (Show your final answer as a percentage point with 2 decimal places)
 
   
Percent of the total variability  
Number of principal components  

c-1. Display the weights used to compute the first principal component scores. (Round your final answers to 4 decimal places.) 

  Weight
Life ladder  
Social support  
Positive emotion  
Negative emotion  
Life choices  
Life expectancy  
Household income index  

c-2. Which original variable is given the highest weight to compute the second principal component? 

   was given the most weight to compute the second principal component

d. What is the principal component 1 score for the first record (Albania)? (Round your final answer to 4 decimal places.)

The first record (Albania) had a score of   for the first principal component

 

 

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