DATA ANALYSIS [2 marks]Why is it appropriate to deseasonalise this data?Calculate the seasonal indices for each month. (2 decimal places)Deseasonalise the data using monthly seasonal indices. (whole number)Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec2014201520162017Determine the equation of the line of best fit (note use Jan 2014 as 1, Feb 2014 as 2, etc.).Using your line of best fit and the seasonal index, predict the rainfall for September 2024.Why is it appropriate to smooth this data?Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

What is the purpose of the seasonal adjustment technique in time series analysis?Review LaterTo remove trend from the time seriesTo remove seasonality from the time seriesTo remove noise from the time seriesTo smooth out the time series data

It has been established in the lecture that December is the seasonal trough for this time series data. That is, December index values are smaller than other months of the year, with the trend having been accounted for. Which TWO of the following statements are FALSE?a.The analysis is false, as the seasonal effect from the month of December is largest amongst all twelve months, estimated at 103.81b. At the 5% significance level, only the seasonal components from May to October (six months inclusive) are statistically larger than that of December.c.At the 5% significance level, only the seasonal components from June to October (five months inclusive) are statistically larger than that of December.d.The seasonal effect from the month of December is included as part of the intercept in this regression model.

Assume you first collected a quarterly sales data (in millions of dollars) over a four-year period from the first quarter 2016 to the fourth quarter 2019 then computed (the normalised) seasonal index for each quarter using ratio-to-moving-average method, and finally fitted a linear trend model based on the deseasonalised sales data and a time period (X), coded as 0, 1, ......, n. If the estimated coefficients of the intercept and X (time period) are 153.93 and 6.01, respectively, what is the seasonally unadjusted forecast value of sales (in millions of dollars) for the first quarter of 2020? Round your final answer to two decimal places. Assume the (normalised) seasonal indices for the quarterly sales data are 1.04, 1.02, 0.96 and 0.98 for quarters 1, 2, 3 and 4, respectively.

Which of the following is NOT a valid time series decomposition? trend x seasonal + irregular trend x seasonal x irregular trend + irregular seasonal + calendar effects

What is the purpose of using a trend line in analytics?To capture seasonal variationsTo capture underlying patterns in dataTo reduce the impact of outliersTo ensure normal distribution of residuals