text: ### Lean Six Sigma Green Belt
  
  Course
  Time
  9 hours 53 minutes
  Difficulty
  Intermediate
  CEU/CPE
  10
  
  ### Video Transcription
  
  00:00
  Hi, guys. Welcome back. I'm Katherine MacGyver, and this is lean six Sigma Green Belt, and we're going to pick up where we left off on regression analysis.
  00:09
  So what we're gonna do today is we're gonna have an introduction to scatter plots were gonna understand how to create them and understand how to read them.
  00:18
  The first step in regression analysis is creating a scatter plot. This will give you a two fold one. It will allow you to read the relationship between the data visually
  00:33
  and to You'll be able to then do your regression analysis,
  00:38
  which will formulaic Lee give you that relationship. So
  00:44
  a scatter plot
  00:46
  is an X Y graph. You have two measurements. You always have your X and your Y variables. And from that you will be able to create a single point on a Cartesian system. Remember that from middle school
  01:06
  that will give you a measurement of that data element.
  01:10
  So
  01:11
  as you read scatter plots, a upward slant to the right is a positive relationship. A downward slant to the right, so we're gonna read from left to right is a negative relationship.
  01:29
  We will notice from the okay, Cuba data.
  01:32
  Um, that you are more likely to receive messages if you are measured as more attractive. Eso there is our relationship. Your dependent variable is going to be your Why access in the OK, Cupid's case. It is the number of messages received
  01:53
  01:55
  is your X axis. And in case of OK, cupid is going to be measured. Attractiveness. So as you are measured as more attractive, you are going to receive more messages.
  02:10
  If this were a negative relationship as you are measured as more attractive, you will receive less messages.
  02:20
  Um, this is important for us to know both directions of the relationship because remember, our independent variables are the variables we can change. Our dependent variables are the ones that were looking to change or our process outputs.
  02:34
  So independent variable. How maney work orders dependent variable. How long does it take us to complete those work orders? So when we're creating a scatter plot in Excel, remember that
  02:46
  we like excel because almost everybody has it in excel. You're going to want to capture your two data sets as columns eso the the two data sets that we're looking at here is the year our presidents were born and the year they were elected.
  03:05
  So two columns that you're going to select both of them.
  03:07
  You're going to click insert graph and you're going to insert a scatter plot. It is going to give you a graph like we are looking at right now. And what I can tell you by looking at this is that there is a pretty good relationship from the year that the president was born and the year that they were elected, we could
  03:28
  determine that if you were,
  03:30
  you were probably not gonna be elected before you were born. So we see no negative relationships. And honestly, if we look at it, the data is pretty tightly clustered. Which tells us that there is pretty. There's a lot of consistency between those relationship, So this is a really nice scatter plot to review
  03:49
  because it's very clear we're looking at a positive
  03:52
  or a strongly positive result between our presidents, year of birth and the year they were elected.
  04:00
  So
  04:01
  today we did an overview of our scatter plots. You know that your dependent is your y variable. Your independent is your ex variable and two created an excel. It's Azizi is click, click, Click. So you're going to go ahead and capture your data, Create your scatter plot, and you're gonna take a visual sniff test and see.
  04:21
  Yeah, presidents are probably not going to be elected to office before they're born, So a positive relationship makes sense.
  04:30
  Our next module, we're gonna go into more in depth. We're gonna actually do our correlation analysis, so I will see you guys there.
  
  ### Lean Six Sigma Green Belt
  
  This Six Sigma Green Belt course teaches students how and where to apply the Six Sigma process improvement methodologies. Upon completing the course, students will have the skills and knowledge to pass the Six Sigma Green Belt certification exam.
  
  ### Instructed By
  
  Kathryn McIver
  Lead Instructor at Evidence-Based Management Association
  Instructor
explanation: The provided text is a transcript from a Lean Six Sigma Green Belt course, specifically a segment on regression analysis and scatter plots. The content explains how to create and interpret scatter plots, including the relationship between variables and how to use Excel for this purpose. The text does not contain a specific question or a question-and-answer pair.
distilabel_metadata.content: Evaluate the user given text extract and determine if it contains a math question and optionally and answer to this question.
  
  Output rules:
  
  1. Briefly describe the document and mention if it contains a question or a question and answer.
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distilabel_metadata.role: system
distilabel_metadata.content: ### Lean Six Sigma Green Belt
  
  Course
  Time
  9 hours 53 minutes
  Difficulty
  Intermediate
  CEU/CPE
  10
  
  ### Video Transcription
  
  00:00
  Hi, guys. Welcome back. I'm Katherine MacGyver, and this is lean six Sigma Green Belt, and we're going to pick up where we left off on regression analysis.
  00:09
  So what we're gonna do today is we're gonna have an introduction to scatter plots were gonna understand how to create them and understand how to read them.
  00:18
  The first step in regression analysis is creating a scatter plot. This will give you a two fold one. It will allow you to read the relationship between the data visually
  00:33
  and to You'll be able to then do your regression analysis,
  00:38
  which will formulaic Lee give you that relationship. So
  00:44
  a scatter plot
  00:46
  is an X Y graph. You have two measurements. You always have your X and your Y variables. And from that you will be able to create a single point on a Cartesian system. Remember that from middle school
  01:06
  that will give you a measurement of that data element.
  01:10
  So
  01:11
  as you read scatter plots, a upward slant to the right is a positive relationship. A downward slant to the right, so we're gonna read from left to right is a negative relationship.
  01:29
  We will notice from the okay, Cuba data.
  01:32
  Um, that you are more likely to receive messages if you are measured as more attractive. Eso there is our relationship. Your dependent variable is going to be your Why access in the OK, Cupid's case. It is the number of messages received
  01:53
  01:55
  is your X axis. And in case of OK, cupid is going to be measured. Attractiveness. So as you are measured as more attractive, you are going to receive more messages.
  02:10
  If this were a negative relationship as you are measured as more attractive, you will receive less messages.
  02:20
  Um, this is important for us to know both directions of the relationship because remember, our independent variables are the variables we can change. Our dependent variables are the ones that were looking to change or our process outputs.
  02:34
  So independent variable. How maney work orders dependent variable. How long does it take us to complete those work orders? So when we're creating a scatter plot in Excel, remember that
  02:46
  we like excel because almost everybody has it in excel. You're going to want to capture your two data sets as columns eso the the two data sets that we're looking at here is the year our presidents were born and the year they were elected.
  03:05
  So two columns that you're going to select both of them.
  03:07
  You're going to click insert graph and you're going to insert a scatter plot. It is going to give you a graph like we are looking at right now. And what I can tell you by looking at this is that there is a pretty good relationship from the year that the president was born and the year that they were elected, we could
  03:28
  determine that if you were,
  03:30
  you were probably not gonna be elected before you were born. So we see no negative relationships. And honestly, if we look at it, the data is pretty tightly clustered. Which tells us that there is pretty. There's a lot of consistency between those relationship, So this is a really nice scatter plot to review
  03:49
  because it's very clear we're looking at a positive
  03:52
  or a strongly positive result between our presidents, year of birth and the year they were elected.
  04:00
  So
  04:01
  today we did an overview of our scatter plots. You know that your dependent is your y variable. Your independent is your ex variable and two created an excel. It's Azizi is click, click, Click. So you're going to go ahead and capture your data, Create your scatter plot, and you're gonna take a visual sniff test and see.
  04:21
  Yeah, presidents are probably not going to be elected to office before they're born, So a positive relationship makes sense.
  04:30
  Our next module, we're gonna go into more in depth. We're gonna actually do our correlation analysis, so I will see you guys there.
  
  ### Lean Six Sigma Green Belt
  
  This Six Sigma Green Belt course teaches students how and where to apply the Six Sigma process improvement methodologies. Upon completing the course, students will have the skills and knowledge to pass the Six Sigma Green Belt certification exam.
  
  ### Instructed By
  
  Kathryn McIver
  Lead Instructor at Evidence-Based Management Association
  Instructor
distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The provided text is a transcript from a Lean Six Sigma Green Belt course, specifically a segment on regression analysis and scatter plots. The content explains how to create and interpret scatter plots, including the relationship between variables and how to use Excel for this purpose. The text does not contain a specific question or a question-and-answer pair.
  
  Label: no-question