# Single variable linear regression: Calculating baseline prediction, SSE, SST, R2 & RMSE:

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Introduction:

This post is focused on basic concepts in linear regression and I will share how to calculate baseline prediction, SSE, SST, R2 and RMSE for a single variable linear regression.

Dataset:

The following figure shows three data points and the best-fit regression line: y = 3x + 2.

The x-coordinate, or “x”, is our independent variable and the y-coordinate, or “y”, is our dependent variable.

Baseline Prediction:

Baseline prediction is just the average of values of dependent variables. So in this case:

(2 + 2 + 8) / 3 = 4

It won’t take into account the independent variables and just predict the same outcome. We’ll see in a minute why baseline prediction is important.

Here’s what the baseline model would look like:

SSE:

SSE stands for Sum of Squared errors.

Error is the difference between actual and predicted values.

So SSE in this case:

= (2 – 2)^2 + (2 – 5)^2 + (8 – 5)^2

= 0 + 9 + 9

= 18

SST:

SST stands for Total Sum of Squares.

Step 1 is to take the difference between Actual values and Baseline values of the dependent variables.

Step 2 is to Square them each and add them up.

So in this case:

= (2 – 4)^2 + (2 – 4)^2 + (8 – 4)^2

= 24

R2:

Now R2 is 1 – (SSE/SST)

So in this case:

= 1 – (18/24)

= 0.25

RMSE:

RMSE is Root mean squared error. It can be computed using:

Square Root of (SSE/N) where N is the # of dependent variables.

So in this case, it’s:

SQRT (18/3) = 2.44

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Tree-like structures are common in our world: Company Hierarchy, File System on your computer, Product category map among others so you might run into a task of creating a Hierarchical level in your SQL query — In this blog post, I will show you how you can do that using couple of approaches. These approaches can also be used to map “parent – child” relationships.

Two approaches are:

1. When you know the Tree Depth
2. When you don’t know the Tree Depth

### #1: When you know tree-depth:

When you know the tree-depth (and if it’s not too deep) then you could consider simple CTE’s to come up with the Hierarchical Levels field in your query.

Let’s take an example:

Input:

 EmployeeID FirstName LastName Title ManagerID 1 Ken Sánchez Chief Executive Officer NULL 16 David Bradley Marketing Manager 273 273 Brian Welcker Vice President of Sales 1 274 Stephen Jiang North American Sales Manager 273 285 Syed Abbas Pacific Sales Manager 273

Query: (On SQL Server)

` with lvl1 as (select [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,1 as Level FROM [dbo].[employees] where ManagerID is null ) , lvl2 as ( select [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,2 as Level FROM [dbo].[employees] where ManagerID IN (Select EmployeeID from lvl1) ), lvl3 as ( select [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,3 as Level FROM [dbo].[employees] where ManagerID IN (Select EmployeeID from lvl2) ) select * from lvl1 union select * from lvl2 union select * from lvl3 `

Output:

 EmployeeID FirstName LastName Title ManagerID Level 1 Ken Sánchez Chief Executive Officer NULL 1 273 Brian Welcker Vice President of Sales 1 2 16 David Bradley Marketing Manager 273 3 274 Stephen Jiang North American Sales Manager 273 3 285 Syed Abbas Pacific Sales Manager 273 3

### #2: When you do NOT know tree-depth:

In other words, if the tree is N-level deep then you are out of luck using option #1. In this case, you should consider the RECURSIVE CTE approach. Here’s an example:

Input: (with the idea that this table will grow over time)

 EmployeeID FirstName LastName Title ManagerID 1 Ken Sánchez Chief Executive Officer NULL 16 David Bradley Marketing Manager 273 23 Mary Gibson Marketing Specialist 16 273 Brian Welcker Vice President of Sales 1 274 Stephen Jiang North American Sales Manager 273 275 Michael Blythe Sales Representative 274 276 Linda Mitchell Sales Representative 274 285 Syed Abbas Pacific Sales Manager 273 286 Lynn Tsoflias Sales Representative 285

Query: (On SQL Server that supports Recursive CTE)

` with HierarchyLvl as ( SELECT [EmployeeID] ,[FirstName] ,[LastName] ,[Title] ,[ManagerID] ,1 as Level FROM [dbo].[employees] where ManagerID is null UNION ALL SELECT e.[EmployeeID] ,e.[FirstName] ,e.[LastName] ,e.[Title] ,e.[ManagerID] ,Level + 1 FROM [dbo].[employees] e INNER JOIN HierarchyLvl d on e.ManagerID = d.EmployeeID ) select * from HierarchyLvl `

Output:

 EmployeeID FirstName LastName Title ManagerID Level 1 Ken Sánchez Chief Executive Officer NULL 1 273 Brian Welcker Vice President of Sales 1 2 16 David Bradley Marketing Manager 273 3 274 Stephen Jiang North American Sales Manager 273 3 285 Syed Abbas Pacific Sales Manager 273 3 286 Lynn Tsoflias Sales Representative 285 4 275 Michael Blythe Sales Representative 274 4 276 Linda Mitchell Sales Representative 274 4 23 Mary Gibson Marketing Specialist 16 4

### Conclusion:

Even if I know tree-depth I will go with option #2 as it’s much easier to read and can accommodate future updates to the table. If you are interested in learning more about this and search for “Recursive Query using Common Table Expression” and you should find technical articles that talk about why it does what it does.

Hope this helps!

# Is the R data science course from datacamp worth the money?

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Question (on Quora) Is the R data science course from datacamp worth the money?

It depends on your learning style.

If you like watching videos then coursera/udacity might be better.

If you like reading then a book/e-book might be better.

If you like hands-on then something like Data Camp is a great choice. I think they have monthly plans so it’s much cheaper to try them out. When I subscribed to it, it was like 30\$/Month or so. I found it was worth it. Also, if you want to see if “hands-on” is how you learn best. Try this: swirl: Learn R, in R. — it’s free! Also, Data Camp has a free course on R too so you could try that as well.

Also, if you want to have free unlimited access for 2-days then try this link: https://www.datacamp.com/invite/G8yVkTrwR3Khn

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# Data analytics vs. Data science vs. Business intelligence: what are the key differences/distinctions?

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They are used interchangeably since all of them involve working with data to find actionable insights. But I like to differentiate them based on the type of the question you’re asking:

• What:

What are my sales number for this quarter?

What is the profit for this year to date?

What are my sales number over the past 6 months?

What did the sales look like same quarter last year?

All of these questions are used to report on facts and tools that help you build data models and reports can be classified as “Business Intelligence” tools.

• Why:

Why is my sales number higher for this quarter compared to last quarter?

Why are we seeing increase in sales over the past 6 months?

Why are we seeing decrease in profit over the past 6 months?

Why does the profit this quarter less compared to same quarter last year?

All of these questions try to figure why something happened? A data analyst typically takes a stab at this. He might use existing Business Intelligence platform to pull data and/or also merge other data sets. He/she then applies data analysis techniques on the data to answer the “why” question and help business user get to the actionable insight.

• What’s next:

What will be my sales forecast for next year?

What will be our profit next year for Scenario A, B & C?

Which customers will cancel/churn next quarter?

Which new customers will convert to a high-value customer?

All of these questions try to “predict” what will happen next (based on historical data/patterns). Sometimes, you don’t know the questions in the first place so there’s a lot of pro-active thinking going on and usually a “data scientist” are doing that. Sometimes you start with a high level business problem and form “hypothesis” to drive your analysis. All of these can be classified under “data science”.

Now, as you can see as we progressed from What -> Why -> What’s next, the level of sophistication needed to do the analysis also increased. So you need a combination of people, process and technology platform in an organization to go from having a Business Intelligence maturity all the way to achieving data science capabilities.

Here’s a related blog post that I wrote on this a while back: Business Analytics Continuum: – Insight Extractor – Blog

..And you can check out other stuff I write about here: Insight Extractor – Blog – Paras Doshi’s Blog on Analytics, Data Science & Business Intelligence.

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# Where can I find a data analyst mentor, be it in-person or online?

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Find a mentor, where do I. Hmmmmmm….

There are few options. 1) Paid online courses with Mentoring 2) Free Options

#1, Paid online courses with mentoring.

I am a mentor for an ed-tech startup Springboard – Learn Data Science & UX Design online — it’s similar to what you are asking for. If you see value in that, you should check it out.

#2. Free options:

a. Quora: You could ask questions here and A2A — Build a network and someone may offer to mentor you offline

b. Mooc: You could join courses on MOOC’s like coursera and udacity — they have good forum support so you could use it for getting your questions answered

c. Cold email: There are lot of analytics/data-science professionals active in the community (linkedin groups, blogs, etc) and if you cold email them, you might find one!

d. local meetups: go to local meetups, meet people and find your mentor.

Stepping back, having a mentor helps and accelerates your progress – but not having one, shouldn’t stop you from achieving what you want.

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