Problem Three. Download the dataset PulseSurvey$39.$xlsx from the Canvas website. There is a corresponding data dictionary which explains the variables on the Canvas site, called PulseSurvey$39$DataDictionary.xlsx$.$ This data comes from the latest wave $($week $39)$ of the United States Census$$ Household Pulse Survey Data. You can learn more about this data from the Census here. Problem Three. Download the dataset PulseSurvey $39.$xlsx from the Canvas website. There is a corresponding

data dictionary which explains the variables on the Canvas site, called PulseSurvey$39$DataDictionary.xlsx$.$ This

data comes from the latest wave $($week $39)$ of the United States Census' Household Pulse Survey Data. You can

learn more about this data from the Census here.

a$)$ Suppose we are most interested in the variable RECVDVACC which records whether or not an

individual reporting being vaccinated against Covid$-19.$ Engage in pattern discovery by trying to find

some pattern between the other variables in the dataset and self$-$reported vaccination status. Do any of

these other variables help predict whether or not an individual in this data is vaccinated? Feel free to

answer this question in whatever way you wish, using either graphs or simple tabulations or even a

report of what you found. Be careful about values of $"-88"$ and $"-99"$ which are used when respondents

did not complete the survey, as described in the data dictionary. It is said of microdata that it often has

"holes, kinks and corners" and this dataset is no exception.

a$)$ Suppose we are most interested in the variable RECVDVACC which records whether or not an individual reporting being vaccinated against Covid$-19.$ Engage in pattern discovery by trying to find some pattern between the other variables in the dataset and self$-$reported vaccination status. Do any of these other variables help predict whether or not an individual in this data is vaccinated? Feel free to answer this question in whatever way you wish, using either graphs or simple tabulations or even a report of what you found. Be careful about values of $-88$ and $-99$ which are used when respondents did not complete the survey, as described in the data dictionary. It is said of microdata that it often has $$holes$,$ kinks and corners$$ and this dataset is no exception. Here is some data attached....