Introduction
In statistics and finance, the name of forecasting is the most dominating one, which has its own recognition and importance lies in a broad nutshell. It is important to analyze and forecast a thing with the help of effective tools of forecasting and organizations always consider the initiation of different standard tools and methods that stride under the ambit of statistical management in total.
Among different statistical tool, which would be used for different purposes in an organization, the name of regression tool is one of them, which has its own significance and importance. There are certain amount of topics, which specifically comes under the ambit of regression analysis, which predominantly is linear regression analysis and others and all of these tools, are essential for a company in a broad nutshell. Regression tool is also used to assess the level of finding the relationship or forecasted a thing accordingly and effectively at the same time.
Linear Regression Forecast is one of the most effective kinds of forecasting tool, which has been taken into account for different purposes in total. The main theme of this paper is to analyze different things from the viewpoint of statistical management. There is a data set has been given along with the assignment which should be used to complete this particular assignment effectively. There are certain tools which should be applied on the same assignment to make it done accordingly. There are total 4 questions that needed to be answered in this particular section accordingly.
- Linear Trend Forecasting
There is data has been given with this particular question, and it is required to analyze the same from the attached statistical based tool, which is Linear Trend Forecasting.
- Plotting
The above mentioned table and graph are indeed seems quite reasonable with each other, and it would certainly bring effectiveness to the company as a whole. Seasonality could been easily with the above mentioned graph.
- It is required to analyze the entire SASS period from the year 1992 to 2007, with the help of different tools of statistics. The same tool of linear based regression has been taken into account for the same work and mentioned below result is found form the same.
There is a marginal growth rate which has been envisaged in this particular answer. The growth rate has been analyzed with the same formula of proportion increase or decrease. The forecasted value is 0.0916 and a significant change has been found while forecasting the value of the same.
Question-2
- Only the missing value is required to analyze and mentioned
- The regression model which has been used in this particular analysis is,
Y = A + Bx
It is the most effective and widely used method to assess the future belongings of a particular thing and it is used in forecasting as well.
- The slope coefficient in this particular analysis lies with the value of 10.668, which is showing that the figures of both of the variables lie under this jurisdiction in total.
- In this particular scenario, if a hypothesis would have been applied, then null hypothesis should have been rejected because of the values mentioned in the summary statistics and in consequences, alternative hypothesis should have been selected accordingly as it would certainly bring effective economic change for the company.
- The test has been conducted to answer that is there any sort of relationship is found among the race of a basket ball player and its impact over the annual salary determination of a player. From the answer and summary statistics, it is found that there is indeed a positive relationship found among both of these variables and it is hypothetically proved that the racism played an important role in annual salary determination.
- Relying on a single formula or method is not a wise decision for a research or hypothesis, In my view, the scenario should be given a new chance of regression or used any other equation or formula for the equation instead of the current ANOVA based table in total.
Q-3
- Decomposition of the Series into Seasonal, Trend and Cyclic
Seasonal
Trend
Trend
- Forecasting
- Plotting
It is seen clearly that the sales of the company is increasing year on year (YOY).
- Holt Model
= 1551,470 * (0.52) + 1,750,260 * (0.11) + 1,591,650 * (0.44)
8, 06,764 + 1, 92,528 + 700,326
Holt Model = 1699,618 Sales
