Analyzingthe Number of Persons who performed House Keeping
Data
Forthis analysis, I used data from the 2010 General Service Survey(GSS). The respondents were asked several questions that have beenused in thorough analysis. These questions included whether they wereworking full time, whether they were working parttime, whether theywere temporarily not working, retired, unemployed, keeping the house,schooling, and so forth. The first dependent variable is keeping thehouse, which is an intervalratio measurement (Wilcox,2005). Thenumber reported in this survey is the actual number of people whowere able to keep their houses given different scenarios like whenthey are working full time, working parttime, retired, unemployed,or temporarily not working.
Theindependent variables for the analysis are the number of peopleworking fulltime, the number of people working parttime, the numberof people who are temporarily not working and those who did notanswer the survey. The number of people who are working full time isan intervalratio measurement, and it represents the actual number ofrespondents that reported in the survey to be working full time(Wilcox,2005). Thoseworking parttime is also an intervalratio interval as it representsthe actual persons who stated in the questionnaire that they wereworking parttime. Those that were temporarily not working are anintervalratio independent variable as it represents the actualnumber of people that actually stated that they were temporarily outof employment. This implies that they had been fired, but they werecurrently searching for employment. Lastly, the variable thatmeasured those who did not answer is a nominal variable as the datahas been coded in categories. By this I mean that from the surveythose who did not answer ranged from 0 to 4 meaning that code zeroare those who were working under contract, code 1 are those who werenot under the legal age of employment, code 3 are those who werefreelancing while code 4 are those were earning money throughinvesting. However, in this statistical analysis, I will be zero,that is those working under contract and four, that is those who wereinvestors.
Methods
Inthis research paper, I am conducting two different statisticalanalyses to examine the importance of the four independent variablesthat I stated above. These variables include the number of peopleworking fulltime, those who were working parttime at the time thesurvey was conducted, those who were temporarily out of employment,and those who did not answer the questionnaire presented to them onthe dependent variable (housekeeping). First, I examine the averagenumber of people who performed housekeeping and were working undercontract and the average number of people who performed housekeepingand were investors using a two samples hypothesis test (Wilcox,2005). WhenI use this hypothesis test, I will be able to determine whether therewill be a difference between the two means. Lastly, I will computethe measures of central tendency for housekeeping and all the otherindependent variables. This will allow me to determine how theintervalratio variables are interrelated.
Results
Toperform my analysis, I must first examine the descriptive analysis ofall the variables that have been used in this research paper.
Table1: Descriptive Statistics
Variables 
N 
Min 
Max 
Mean 
Median 
Mode 
SD 
Working full time 
29437 
619 
2322 
981.233 
418.717 

Working parttime 
6115 
107 
440 
203.833 
81.8253 

Temporary not working 
1253 
19 
90 
41.7667 
15.7122 

Unemployed 
1977 
25 
148 
65.9 
33.5861 

Retired 
8102 
144 
715 
270.067 
132.058 

School 
1841 
31 
140 
61.3667 
25.062 

Keeping house 
9650 
199 
496 
321.667 
78.1009 

Other 
1208 
9 
155 
40.2667 
30.2688 

No answer 
16 
0 
4 
0 
Themode is the measure of central tendency of the nominal variable abovethat is ‘no answer’. The mean, on the other hand, is the measureof central tendency for the intervalratio variables while the medianis the measure of central tendency for the ordinal data, which we dono have in this case. As shown in the table above, on average, thep eople reported as to working full time are 981.233, workingparttime are 203.833, temporarily not working are 41.7667,unemployed are 65.9, retired are 270.067, attending school are61.3667, house keeping are 321.667, and others are 40.2667. The modeof the nominal variable that is ‘no answer’ is code 0, whichimplies those who were working under contract.
Table2: TwoSample Hypothesis Test of the number of people performinghousekeeping by no answer
No answer 

  
  
Under Contract 
Investors 
  
  
  

Number of People keeping house 
15 
14.5 

N 
22 
1 

N= 23, t= 16.446, p=1 
Thetable above shows the mean frequency of the number of people keepinghouses for the investors and those who were under contract. The 22people under contract keep houses for a mean of 15 times while theone person who was an investor kept houses for an average of 14.5times per year.
Althoughthe two variables are different, I conducted a two samples hypothesistest to determine whether the mean difference of the variables isstatistically significant. The test statistics stated that the t–testwas 16.446 while the pvalue of one indicated the 5% critical region.Since the pvalue of one is larger than the critical value of 0.05, Iam therefore able to conclude that the difference between the meansof the two variables is not statistically significant (Wilcox,2005). I,therefore, fail to reject the null hypothesis that there is nodifference between the two means. I am therefore able to concludethat the average number of people that perform housekeeping who areunder contract is more than that of the investors.
Table3: Correlation Matrix of Number of People who performed Housekeepingas compared o other independent variables
  
Working full time 
Working parttime 
Temporary not working 
Unemployed 
Retired 
School 
Keeping house 
Other 
Working full time 
1 

Working parttime 
0.96393 
1 

Temporary not working 
0.85033 
0.79207504 
1 

Unemployed 
0.62681 
0.720392278 
0.56289125 
1 

Retired 
0.9308 
0.957356456 
0.77868217 
0.7399 
1 

School 
0.85222 
0.883074944 
0.77196701 
0.7322 
0.89783 
1 

Keeping house 
0.7322 
0.000643902 
0.23029901 
0.04071 
0.0113 
0.04418 
1 

Other 
0.86517 
0.881928059 
0.75063469 
0.81396 
0.93098 
0.84171 
0.07176 
1 
Fromthe table above, I calculated the correlation of all the variables inthe analysis. The number of people performing house keeping has weakpositive correlation with the number of people working parttime(r=0.000644). Similarly, it has a weak positive correlation with thenumber of people schooling (r= 0.0044). Other people also have a weakpositive correlation with the number of people attending school (r=0.0718).
Theoverall statistics presented in table 2 and table three states thatpeople who perform housekeeping will work more on a fulltime basisand less on a parttime basis. Fewer of them will also not betemporarily employed fewer of them will be unemployed while retired.Lastly, most people who perform housekeeping will not be attendingschool.
References
Wilcox,R. (2005). Introductionto robust estimation and hypothesis testing.Amsterdam: Elsevier/Academic Press. Retrieved on 24 July 2016.