Question

Difference between time series analysis and regression analysis

Answer 1

Answer:

There are a number of approaches to time series analysis, but the two best known are the regression method and the Box-Jenkins (1976) or ARIMA (AutoRegressive Integrated Moving Average) method. This document introduces the regression method. I consider the regression method far superior to ARIMA for three major reasons

Step-by-step explanation:

Answer 2

Before the holiday break Dr. Redmond and I discussed what I should do while we wait and see what kinds of responses we get. He suggested that I do a content entry about the statistical methods that we may use when we begin analyzing the data. That’s why the theme of this entry is statistics!
Most of the times when I talk to people about statistics the response is that they are glad they never have to take that class again. I think this is unfortunately a result of having to slog through hours of calculations in order to understand the basics of statistical analysis. It’s a shame really, because I think this leads to people missing out on what statistics is: finding meaning in a chaos of data. I had an advanced statistics teacher describe the process as combining individual letters into a book. By themselves the letters don’t mean anything, but organizing them could produce something very meaningful. Dr. Redmond asked me to focus on regression and time-series analysis as these will be what we will use for our data analysis, so that’s what I’ll be discussing.
To begin with, regression analysis is “finding the best-fitting straight line for a set of data” (Gravetter & Wallnau, 2011). When you see a chart with lots of data points spread all over and a line running through them, this is a linear regression analysis (and the line is called a regression line). This line represents the linear equation that has the least amount of error (distance) between the line and the actual data, or the line that is the least far away from the data and is therefore most representative.  What the line is then showing you is the relationship between two variables of interest (Lewis, 2007). UBIQUITOUS STATISTICAL CAUTION: correlation is not causation, but the tighter that the data points are around the regression line, the stronger the relationship between the variables (Dizikes, 2010). Once we have this line we can make predictions about future outcomes if we only have data for one of the variables. If we take this even further and assess the data over time, then we can make predictions about what will happen in the future. This is the point of a time series regression analysis.

Image courtesy of MITnews
While a linear regression analysis is good for simple relationships like height and age or time studying and GPA, if we want to look at relationships over time in order to identify trends, we use a time series regression analysis. There are three types of time series analyses (trend, seasonal, and irregular), but for our study we will be looking at the trend, or long term direction of the relationship (Australian Bureau of Statistics, 2008). This is what we see on graphics of projected overweight rates. Basically, a time series analysis uses time as one of the variables in order to see if there is change over time. For our purposes, this is very important as we need to assess how SDO scores are changing for African-Americans over the years as Barack Obama was both elected and then re-elected.