Observations:

Level of position and experience as well as state are all cateogircal explanatory variables. A response variable needs to be incorporated in a form of frequency in order to build models for association.

Do levels of position/experience differ per state? There are not enough states/respondents to test if there is a difference between state and level of position/ experience.. each respondent indicated they are from a unique state, thus, there’s not repetition that would allow to test ANOVA or T-test..

My recommendation would be to incporate the number of tax programs that had a positive result post financial crisis per state in the form of frequency that would allow us to test further.

library(readxl)
## Warning: package 'readxl' was built under R version 3.4.4
SurveyTax<- read_excel("C:/Users/Enrique/OneDrive/Documents/HU/ANLY545_AnalyticalMethods02/Project/TableSurvey.xlsx")
head(SurveyTax)
## # A tibble: 6 x 4
##   Respondent `Position level` `Experience level` State       
##        <dbl>            <dbl>              <dbl> <chr>       
## 1       1.00             2.00               1.00 Delaware    
## 2       2.00             1.00               3.00 Iowa        
## 3       3.00             2.00               4.00 Maryland    
## 4       4.00             1.00               3.00 Utah        
## 5       5.00             4.00               3.00 New Jersey  
## 6       6.00             2.00               4.00 Pennsylvania

Convert position and experience level into factors

SurveyTax$`Position level`= as.factor(SurveyTax$`Position level`)
SurveyTax$`Experience level`= as.factor(SurveyTax$`Experience level`)
str(SurveyTax)
## Classes 'tbl_df', 'tbl' and 'data.frame':    29 obs. of  4 variables:
##  $ Respondent      : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ Position level  : Factor w/ 4 levels "1","2","3","4": 2 1 2 1 4 2 1 3 2 2 ...
##  $ Experience level: Factor w/ 4 levels "1","2","3","4": 1 3 4 3 3 4 4 2 3 4 ...
##  $ State           : chr  "Delaware" "Iowa" "Maryland" "Utah" ...

Visualizations of respondent’s position and experience levels:

plot(SurveyTax$`Position level`, main="Respondent Position level", xlab="Lowest to highest",
     ylab= "Respondents",col="lightblue")

plot(SurveyTax$`Experience level`, main="Respondent Experience level", xlab="Lowest to highest",
     ylab= "Respondents",col="lightblue")

Proportion of respondents position and experience levels

Position_level = table(SurveyTax$`Position level`)
Experience_level = table(SurveyTax$`Experience level`)
prop.table(Position_level)
## 
##          1          2          3          4 
## 0.24137931 0.58620690 0.13793103 0.03448276
prop.table(Experience_level)
## 
##         1         2         3         4 
## 0.1379310 0.2068966 0.3103448 0.3448276

Assuming 3 and above are management, it looks like ~805 of repondents are within the analyst level role. However, 65% of respondents said they have been working in the economic development field for quite a while, Pending on confirming how these levels translate into years of experience.

pie(prop.table(Position_level), col = c(5,6,7,8), main="Proportion of Respondent's Position level")

pie(prop.table(Experience_level), col = c(5,6,7,8), main="Proportion of Respondent's Experience level")