The purpose of the app is to predict snow-fall and base (the height of snow comprising the base of a ski field) for ski fields in New Zealand. The predictions are based on which ski field(s) you are interested in and during which time of year

You can see the app here: https://mikeshout.shinyapps.io/nz_snow_adviser/

This is for the week-4 assignment in the Data Products class offered by John Hopkins University on Coursera

Snow predictions are based on three variables,

• Elevation
• Latitude
• Month

The user slects the month and filters ski field(s) of interest by choosing difficulty and location (nearby airport). The model then returns,

• Predicted monthly snowfalls with 95% confidence interval
• Predicted base with 95% confidence interval

snowModel <- lm(avg.SnowFall ~ Lat + SumitFT + month, data = skiData)
baseModel <- lm(Base ~ Lat + SumitFT + month, data = skiData)
snowModel

## 
## Call:
## lm(formula = avg.SnowFall ~ Lat + SumitFT + month, data = skiData)
## 
## Coefficients:
## (Intercept)          Lat      SumitFT     monthJul     monthJun  
##  -1.399e+01   -2.739e-01    9.275e-04   -6.793e-01   -3.265e+00  
##    monthOct     monthSep  
##  -3.165e+00    2.927e+00