Learning Multiple Regression Analysis with an SEO Example【Regression Analysis Series 3】

By | August 21, 2019

Today I’ll show you the procedure for Multiple
Regression Analysis that you can use in practice. [Intro Video] Hi, this is Mike Negami, Lean
Sigma Black Belt. This episode is the third video of the ‘Regression
Analysis Series’ which is very useful in business and this is about Multiple Regression
Analysis. The previous two videos are the basics and
will be the foundation for today’s content, so please watch them from the link above if
you haven’t done so. This will be a short review from the previous
video. Regression Analysis is finding a Regression
Equation to calculate the Objective Variable Y. In the equation, the X’s are called Explanatory
Variables. When there is only one Explanatory Variable,
it’s a Simple Regression Analysis. When there are multiple, it’s a Multiple Regression
Analysis. In practice, we perform a Regression Analysis
for sales forecasting or marketing analysis etc., but it’s rare to have only one Explanatory
Variable, so we usually use Multiple Regression Analysis. Fortunately, its operation in Excel is the
same as the Simple Regression Analysis. However, when preparing source data, Multiple
Regression Analysis has a couple of things you should be aware of. I’ll also explain the Multiple Regression
Analysis using my blog site econoshift.com as an example. These days, website management requires certain
tactics to increase traffic from Google. Those tactics are called SEO. In SEO, it’s essential to set Target Keywords
for each page of a web site, so I always have trouble choosing the best one. This is the SEO tool I made. The numbers in Column R are the ‘Clicks to
Page’ that came from Google to each page of my blog site. If you click on ‘Target Keywords’ in Column
AJ, you can see actual search keywords that were used when they came to the page. There are also various data for the keywords. I would like to know which of these data are
the most important for selecting Target Keywords using a Multiple Regression Analysis. In Multiple Regression Analysis, the most
important and difficult thing is to decide the Objective Variable and the Explanatory
Variables effectively. In our example, our goal is to gain more traffic
from Google, so I chose the ‘Clicks to Page’ as an Objective Variable. The Explanatory Variables are selected Target
Keywords’ ‘Clicks’, ‘Impressions’, ‘CTR’ and ‘Position’. Although I will not explain the details of
SEO today, among those Keywords’ data, I’ll find which one influences the ‘Clicks to Page’
most with a Multiple Regression Analysis. In our case, the data to be set as the Objective
Variable was obvious, but there are cases when there are several choices. If so, it will be easier to decide by considering
what you want to accomplish from the analysis results. There are also times when you have many data
to use as Explanatory Variables. However, if you include too many of them,
the accuracy of the analysis will fall, so generally speaking, have up to seven Explanatory
Variables at most. When you select Explanatory Variables, there
are a couple of things to consider. Continuous data such as length and time are
called Quantitative Data. On the other hand, data such as ‘Male / Female’
and ‘Like / Dislike’ are called Qualitative Data. These data are always included in survey results. You can use those data in Multiple Regression
Analysis too, but they need to be quantified. You can apply numbers like 1 for male, 2 for
female, or 3 for Like, 2 for Neutral, 1 for Dislike and so on. This variable is called a Dummy Variable. Here is another consideration. During Multiple Regression Analysis, if you
use data with high correlation among Explanatory Variables, we know that mathematically, it’s
not possible to obtain a correct Regression Equation. We say that there is Multicollinearity. In a frequently used example, when you do
Multiple Regression Analysis with people’s heights as the Objective Variable and the
lengths of right and left legs as Explanatory Variables, you’ll get a funny result like
this: the left legs’ coefficient is minus, which means that the longer it becomes, the
lower the height becomes. To prevent multicollinearity, first, calculate
all of the Coefficient of Correlations between all Explanatory Variables. If there is a group of data with strong correlation,
exclude one of them. Or, for some reason, if you need to include
both, merge them as one group of data in an appropriate way such as averaging them. Let’s do it on Excel. Here are Explanatory Variable data. Excel has the CORREL Function. By selecting two data ranges you want to compare
in the CORREL Function, it will show the Coefficient of Correlation between the two data groups. I confirmed that there are no high Coefficient
of Correlations this time, so I’ll use all of the data as Explanatory Objectives. Let’s do a Multiple Regression Analysis now. Select ‘Data’, ‘Data Analysis’, then ‘Regression’. In the ‘Input Y Range’, select the range of
the Objective Variable, which is ‘Clicks to Page’. In the ‘Input X Range’, select the entire
‘Explanatory Variables’ data range. For a Multiple Regression Analysis, you should
include each column name and check ‘Labels’ here. As a result, the column names of your Explanatory
Variables will appear in the regression result. I explained the P-Value in the previous video
in detail. Please watch it. Briefly, Explanatory Variables with lower
P-Values have stronger relationships with the Objective Variable. In our case, it turned out that statistically
the ‘Impression’ of search keywords affects the ‘Clicks to Page’ most. It was great to explain this logically. From these coefficients, my Multiple Regression
Equation looks like this: Now I can predict the ‘Clicks to Page’ from my Target Keywords
data. The ‘Adjusted R Square’ is 66% so it is
a reasonable result. The best result in this analysis for me was
to find that ‘Impression’ is the most important factor when deciding my Target Keywords. This is very helpful for my SEO activities. Thank you very much for viewing. Please click the ‘Subscribe’ button. Also click and watch my other related videos. Thanks.