Load assessment data
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## chisq.test, fisher.test
## # A tibble: 476 × 4
## district population_group subgroup proficiency
## <chr> <chr> <chr> <dbl>
## 1 Barbour Gender Female 20.6
## 2 Barbour Gender Male 22.9
## 3 Barbour Race/Ethnicity Multi-Racial 35.7
## 4 Barbour Race/Ethnicity White 21.7
## 5 Barbour Student Status Economically Disadvantaged 15.5
## 6 Barbour Student Status Foster Care 14.0
## 7 Barbour Student Status Special Education (Students with Disab… 5.59
## 8 Barbour Total Population Total 21.8
## 9 Berkeley Gender Female 24.7
## 10 Berkeley Gender Male 24.9
## # ℹ 466 more rows
Load spending data
## # A tibble: 55 × 10
## School_Name enroll Federal_Revenue State_Revenue Local_Revenue
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BARBOUR CO SCH DIST 2144 7559 16584 5872
## 2 BERKELEY CO SCH DIST 19722 48407 140127 86699
## 3 BOONE CO SCH DIST 3177 8194 26858 14564
## 4 BRAXTON CO SCH DIST 1747 5479 12748 6404
## 5 BROOKE CO SCH DIST 2582 6791 17114 21352
## 6 CABELL CO SCH DIST 11667 42518 88337 66699
## 7 CALHOUN CO SCH DIST 861 3254 9953 3190
## 8 CLAY CO SCH DIST 1669 6157 17655 2791
## 9 DODDRIDGE CO SCH DIST 1082 3455 3999 31752
## 10 FAYETTE CO SCH DIST 5594 15293 51759 23477
## # ℹ 45 more rows
## # ℹ 5 more variables: Total_Expenditures <dbl>, Total_Current_Spending <dbl>,
## # Instruction_Spending <dbl>, Support_Services_Spending <dbl>, county <chr>
Load demographic data
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
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## Rows: 62 Columns: 5
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## Delimiter: ","
## chr (3): County, FIPS, Rank within US (of 3143 counties)
## dbl (2): Value (Percent), People (Unemployed)
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## ℹ Use `spec()` to retrieve the full column specification for this data.
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## # A tibble: 55 × 2
## county percent_unemployed
## <chr> <dbl>
## 1 Mcdowell 15.1
## 2 Braxton 14.4
## 3 Logan 13.3
## 4 Calhoun 12.2
## 5 Roane 11.7
## 6 Clay 11.2
## 7 Mingo 11.2
## 8 Webster 11.1
## 9 Monroe 10.6
## 10 Barbour 10.1
## # ℹ 45 more rows
Joined data
Correlations
Linear Regression Model