Thesis

Does the money per student have a direct effect on the outcome of the student’s science proficiency scores is the question I am trying to answer for my project. I believe that we will see there being a positive trend of the counties with hgiher money per students numbers that they will tend to have higher proficiency scores.

## # A tibble: 55 × 7
##    county    school school_name    population_group subgroup science_proficiency
##    <chr>     <chr>  <chr>          <chr>            <chr>                  <dbl>
##  1 Barbour   999    Barbour Count… Total Population Total                   26.0
##  2 Berkeley  999    Berkeley Coun… Total Population Total                   28.6
##  3 Boone     999    Boone County … Total Population Total                   19.6
##  4 Braxton   999    Braxton Count… Total Population Total                   22.6
##  5 Brooke    999    Brooke County… Total Population Total                   21.1
##  6 Cabell    999    Cabell County… Total Population Total                   30.8
##  7 Calhoun   999    Calhoun Count… Total Population Total                   27.8
##  8 Clay      999    Clay County T… Total Population Total                   23.3
##  9 Doddridge 999    Doddridge Cou… Total Population Total                   31.3
## 10 Fayette   999    Fayette Count… Total Population Total                   17.4
## # ℹ 45 more rows
## # ℹ 1 more variable: proficiency <dbl>
## # A tibble: 55 × 9
##    name    enroll tfedrev tstrev tlocrev totalexp ppcstot county moneyPerStudent
##    <chr>    <dbl>   <dbl>  <dbl>   <dbl>    <dbl>   <dbl> <chr>            <dbl>
##  1 BARBOU…   2144    7559  16584    5872    28021   11885 Barbo…            13.1
##  2 BERKEL…  19722   48407 140127   86699   264253   12704 Berke…            13.4
##  3 BOONE …   3177    8194  26858   14564    48642   14663 Boone             15.3
##  4 BRAXTO…   1747    5479  12748    6404    24417   13153 Braxt…            14.0
##  5 BROOKE…   2582    6791  17114   21352    41908   15642 Brooke            16.2
##  6 CABELL…  11667   42518  88337   66699   183621   14538 Cabell            15.7
##  7 CALHOU…    861    3254   9953    3190    15154   16085 Calho…            17.6
##  8 CLAY C…   1669    6157  17655    2791    25963   13825 Clay              15.6
##  9 DODDRI…   1082    3455   3999   31752    38493   23563 Doddr…            35.6
## 10 FAYETT…   5594   15293  51759   23477    83373   13777 Fayet…            14.9
## # ℹ 45 more rows
## # A tibble: 55 × 2
##    county          unemployed
##    <chr>                <dbl>
##  1 McDowell County       15.1
##  2 Braxton County        14.4
##  3 Logan County          13.3
##  4 Calhoun County        12.2
##  5 Roane County          11.7
##  6 Clay County           11.2
##  7 Mingo County          11.2
##  8 Webster County        11.1
##  9 Monroe County         10.6
## 10 Barbour County        10.1
## # ℹ 45 more rows

Data Review

Summary of Data

##     county             school          school_name        population_group  
##  Length:55          Length:55          Length:55          Length:55         
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##    subgroup         science_proficiency  proficiency   
##  Length:55          Min.   :14.81       Min.   :14.81  
##  Class :character   1st Qu.:21.45       1st Qu.:21.45  
##  Mode  :character   Median :24.36       Median :24.36  
##                     Mean   :25.30       Mean   :25.30  
##                     3rd Qu.:29.39       3rd Qu.:29.39  
##                     Max.   :41.80       Max.   :41.80
##      name               enroll         tfedrev           tstrev      
##  Length:55          Min.   :  800   Min.   :  1511   Min.   :  3895  
##  Class :character   1st Qu.: 1654   1st Qu.:  4991   1st Qu.: 12668  
##  Mode  :character   Median : 3177   Median : 10158   Median : 26858  
##                     Mean   : 4586   Mean   : 13312   Mean   : 34234  
##                     3rd Qu.: 5104   3rd Qu.: 14518   3rd Qu.: 39496  
##                     Max.   :24392   Max.   :109522   Max.   :176062  
##     tlocrev          totalexp         ppcstot         county         
##  Min.   :  1956   Min.   : 13954   Min.   :11885   Length:55         
##  1st Qu.:  8194   1st Qu.: 26486   1st Qu.:13151   Class :character  
##  Median : 14813   Median : 48642   Median :13777   Mode  :character  
##  Mean   : 25032   Mean   : 69482   Mean   :14466                     
##  3rd Qu.: 33333   3rd Qu.: 81172   3rd Qu.:15236                     
##  Max.   :145623   Max.   :416491   Max.   :23563                     
##  moneyPerStudent
##  Min.   :12.78  
##  1st Qu.:13.98  
##  Median :14.95  
##  Mean   :15.88  
##  3rd Qu.:16.27  
##  Max.   :35.58
##     county            unemployed    
##  Length:55          Min.   : 2.600  
##  Class :character   1st Qu.: 5.050  
##  Mode  :character   Median : 6.400  
##                     Mean   : 7.055  
##                     3rd Qu.: 8.500  
##                     Max.   :15.100

For this graph, I did totalexp divided by enrollment to get the money allocated per student and then I decided to graph the science proficiency scores against the money per student to see how it looked. Here does not show a super strong correlation between the two as the place with the highest proficiency is very middle of the pack in regards to the money per student.

Correlations

##                 proficiency moneyPerStudent
## proficiency       1.0000000       0.1705259
## moneyPerStudent   0.1705259       1.0000000

Here I did a correlation matrix to see if there was a good correlation between the proficiency and the money per student. The correlation ended up being .1705259 which is not very good.

Linear Regression Model

The p value of .869 is very high and tells us that there is no significant relationship between money per student and proficiency.

Conclusion

Overall, I have learned that I was wrong and that the money per student of a county does not truly indicate a students proficiency score, as nothing really suggests they have a good correlation. Things I could have done better are maybe dive into other indicators and find something that does lead to a good correlation.

Citations