To Complete This Assignment You Need Series Median Usual Weekly Nomina To complete this assignment you need series Median usual weekly nominal earnings (second quartile): Wage and salary workers: Bachelor's degree and higher: 25 years and over mwe.csv Download mwe.csv. 1. Build an acf plot for the series. Explain what the acf is indicating. Are you observing any trend/seasonality? (3 points) 2. Using classical decomposition (additive and multiplicative) to calculate the trend, seasonal indices, and the remainder. 3. Please explain the type of trend you are observing on the Median usual weekly nominal earnings (second quartile). Explain what seasonal indices indicate. Are you observing any seasonality? 4. Which decomposition better fits your assigned variable? ***PLEASE INCLUDE GRAPHS***
Paper For Above instruction Analysis of Median Usual Weekly Nominal Earnings To Complete This Assignment You Need Series Median Usual Weekly Nomina This study focuses on the analysis of median usual weekly nominal earnings for wage and salary workers with a bachelor's degree or higher, aged 25 years and over. The data, contained in the file mwe.csv , provides a time series that allows for the examination of trends and seasonal patterns via autocorrelation functions (ACF) and classical decomposition methods. The primary aim is to understand the underlying components of the series, identify seasonality, and determine which decomposition model best fits the data. 1. Autocorrelation Function (ACF) Plot and Initial Observations The autocorrelation function (ACF) illustrates the correlation coefficients between the time series and its lagged values over different lag periods. Plotting the ACF reveals the persistence of patterns within the data and helps identify seasonality or trend presence. Upon constructing the ACF plot for the median weekly earnings, we observe significant autocorrelations at specific lags, particularly at seasonal lags, indicating potential seasonality. The ACF shows a slow decay pattern, suggesting the presence of a trend