\(\text{Using the database: Washington_Health_Workforce_Survey_Data_20250607 provided by Washington State }\)
\(\text{at https://data.wa.gov/Health/Washington-Health-Workforce-Survey-Data/cvrw-ujje/about_data to evaluate }\)
\(\text{the medical work force of the state.}\)

\(\text{The data will highlight the top 10 trending medical career certifications.}\)

\(\text{Using the following linear regression equation:}\)

                   \(y = ax^2 + bx + c\)

Tracking the top 10 highest growing medical career field between 2014 and 2024:

Linear regression plot of top 10 medical fields growth by year over the the same 2014-2024 period.

Call:
lm(formula = top_10$n ~ poly(top_10$InitialCredentialYear, 2))

Residuals:
    Min      1Q  Median      3Q     Max 
-454.79 -258.47 -158.47   71.01 2727.82 

Coefficients:
                                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)                              608.20      51.37  11.841  < 2e-16 ***
poly(top_10$InitialCredentialYear, 2)1 -2287.42     536.27  -4.265 4.35e-05 ***
poly(top_10$InitialCredentialYear, 2)2 -1917.48     536.27  -3.576 0.000528 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 536.3 on 106 degrees of freedom
Multiple R-squared:  0.2262,    Adjusted R-squared:  0.2116 
F-statistic: 15.49 on 2 and 106 DF,  p-value: 1.255e-06

thisX = top_10$InitialCredentialYear
thisY = top_10$CredentialType
thisZ = top_10$n
plot_ly(x=thisX, y=~thisY, z=thisZ,
    type="scatter3d", mode="markers",
    color=thisY)%>%
 hide_colorbar()

\(\text{We were able import and use the database provided to find the follow:}\)

        \(\text{Top 10 growing medical credientals in WA state}\)

        \(\text{Use linear regression to find growth trend over 2014 -2024}\)

        \(\text{And provide multiple charts to visualize provide data}\)