Polynomial regression as equation for a paper I need to write the following model in equation form for a paper:

Is the following equation describing the model correctly?

Edit:
I forgot to mention that this is a machine learning model. The purpose of the equation is simply to state it in the methodology in a paper not to calculate anything.
Here is the code used:
df.shuffled <- fullsample5[sample(nrow(fullsample5)),]

#define number of folds to use for k-fold cross-validation
K <- 100

#define degree of polynomials to fit
degree <- 5

#create k equal-sized folds
folds <- cut(seq(1,nrow(df.shuffled)),breaks=K,labels=FALSE)

#create object to hold MSE's of models
mse = matrix(data=NA,nrow=K,ncol=degree)

#Perform K-fold cross validation
for(i in 1:K){
  
  #define training and testing data
  testIndexes <- which(folds==i,arr.ind=TRUE)
  testData <- df.shuffled[testIndexes, ]
  trainData <- df.shuffled[-testIndexes, ]
  
  #use k-fold cv to evaluate models
  for (j in 1:degree){
    fit.train = lm(ROI ~ poly(Freq,j), data=trainData)
    fit.test = predict(fit.train, newdata=testData)
    mse[i,j] = mean((fit.test-testData$Freq)^2) 
  }
}


Because you used raw and not orthogonal polynomials, the fitted model could be written as follows (with four significant digits):
$$
\widehat{ROI}_i=-39.15-0.5117\times Freq_i + 0.004323\times Freq^{2}_{i}
$$
