PDA - LSSBB 06.8 Statistical Analysis

Summary

Highlights

• The science of collecting data to uncover patterns is crucial for making informed decisions. 🔍
• Aim to convert data into actionable insights, not just information. 🔄
• There are numerous statistical tests, but knowing which one to use is essential. 🧠
• A simplified statistical decision tree helps in navigating complex choices. 🤔
• Always align statistical tools with your specific objective. 🎯

Key Takeaways

• Statistical analysis is about turning data into meaningful information for problem-solving and process improvement. 📊
• Don’t get overwhelmed by statistical tests; focus on choosing the right tool for the right task. 🔧
• A simplified statistical decision tree can guide you in selecting appropriate tests for data analysis. 🌳
• Understanding your objective is crucial whether you're describing, classifying, or predicting data. 🎯
• Practice and patience are key as statistical tools and tests evolve continuously. 🕰️

Overview

Chapters

• 00:00 - 02:30: Introduction and Overview The chapter "Introduction and Overview" begins with a greeting and acknowledges the varied times at which viewers may be watching. The speaker introduces the topic of the session as 'statistical analysis,' noting that it might seem intimidating or overwhelming to many in the audience. The introduction sets the stage for a discussion or exploration of the subject, suggesting that the content will be focused on making statistical analysis more approachable or understandable.
• 02:30 - 05:00: Understanding Statistical Analysis The chapter begins with a simplification of the concept of statistical analysis, aiming to make it less confusing for learners. It introduces statistical analysis as the science of collecting data and identifying patterns within that data.
• 05:00 - 07:30: Objectives of Statistical Tests The chapter 'Objectives of Statistical Tests' emphasizes the process of transforming raw data into meaningful information. It highlights the importance of identifying trends through the conversion of data to information, and ultimately to knowledge. This involves adding context and learning to the data, allowing for a deeper understanding that can lead to spotting significant trends and insights. The progression is depicted as moving up a pyramid, starting from data, to information, to knowledge, and ultimately to actionable insights.
• 07:30 - 10:00: Variables and Data Considerations The chapter delves into the transformation of data into meaningful information, emphasizing the importance of this conversion not for mere knowledge acquisition but for practical applications. The aim is to enhance decision-making processes, solve problems, and improve existing processes. The discussion highlights that the ultimate goal is not just to understand data, but to use that understanding effectively.
• 10:00 - 12:30: Descriptive Statistics and Other Tools This chapter emphasizes the importance of using data to make informed decisions and take actionable steps. It acknowledges that while there are numerous statistical tests available to extract insights from data, the focus is more on making wise and practical use of these tools rather than diving deep into statistics. The chapter reassures that it does not require one to be a statistician and aims to avoid overwhelming the reader with complex statistical details.
• 12:30 - 15:00: Comparing Data and Hypothesis Testing The chapter aims to demystify statistical tests, emphasizing the importance of using the correct test for a given data scenario. It reassures learners not to feel intimidated by these tests, highlighting that proficiency comes with practice. Readers are encouraged to be patient and persistent as they develop these skills.
• 15:00 - 17:30: Multiple Variables and Correlation The chapter discusses the complexity and constant evolution of tools in the field of statistics, particularly in the context of multiple variables and correlation. It emphasizes that there is no single perfect tool that can address all questions, as different tools may be designed to tackle different aspects of a problem. The chapter acknowledges the ongoing research and development in statistics, highlighting that new tools are frequently developed and tested. It also points out that in many situations, experts may not agree on a single set of tools, reflecting the dynamic and nuanced nature of the field.
• 17:30 - 20:00: Regression Analysis This chapter discusses the importance of using the appropriate tools for regression analysis to make data-driven decisions. It advises against getting overwhelmed by various tools and emphasizes selecting the right one for specific tasks.
• 20:00 - 22:30: Control Charts and Data Types The chapter begins by discussing the objectives behind conducting a test, including the need to describe or characterize a population or sample. There are questions posed regarding the purpose of describing, classifying, categorizing, or comparing data sets. The narrative emphasizes defining a clear objective whether it is description, classification, or comparison of data sets within the context of control charts and data types.
• 22:30 - 27:00: Summary and Conclusion The chapter delves into the nuances of comparing different training methods and trainers, emphasizing the importance of understanding variations in performance. It suggests that comparison is not only essential but also paves the way for predicting outcomes in American contexts. The chapter further explores the need to explain and analyze the reasons behind performance variations.

PDA - LSSBB 06.8 Statistical Analysis Transcription

• 00:00 - 00:30 [Music] hello hello hello good morning good afternoon good evening depending on what time you're watching this video apolitical rv and for this session statistical analysis okay so i guess for the majority this would be a little intimidating okay or it's overwhelming
• 00:30 - 01:00 but or confusing sometimes but not to worry no let's simplify things now so this was more of introduction but how we will do statistical analysis okay an amount of different approaches okay in fact you'll actually see that we've covered a good number of them already all right so first what is it i know about the statistical analysis okay um well it's really nothing but the science of collecting data and where after you collect it we try to uncover the patterns of
• 01:00 - 01:30 trends and that's what we're actually doing now so you wanted to make sense of of data converted to information remember you're adding i don't know that pyramid where you get data then get that to an information to knowledge then eventually all the way until you establish is you get to spot the trends you convert that to data and put some more context on it uh once you put the learning on and then patio and context and you you'd be able to convert that to meaningful information which would
• 01:30 - 02:00 eventually lead us to make decisions so it's not just converting it to information per se just for the sake of it but it's more of we want to convert data to information to help us solve problems to be able to help us improve our processes solve issues it's not just meaning and learning data and information that's it i'm smart it's not that it doesn't end there important is being able to convert and say nothing
• 02:00 - 02:30 is we wanted to improve our process and take action but for us to make data-driven decisions now to make smart actions we need data and convert that okay and typically there's there's tons of different statistical tests which could uncover no and this is not a sticks class no this is not and we are not statisticians for that matter you know so we'll just be wise though and kidney took us many times we get overwhelmed
• 02:30 - 03:00 and intimidated with all this statistical tests but here we'll we'll try to make sure not that we'll only use specific tests now he cannot we'll use the right tool for the right job no and and just just to give you an an id you know um this thing takes practice though don't be frustrated right so learning these tools they take practice and uh it's okay because even actually
• 03:00 - 03:30 there's so many different tools you know and and they they may address slightly different things questions and in some cases majority of the time in fact no there's no single perfect tool no uh it's a total and even in statistics in terms of the area of research and development right now new new tools are being developed and tested and you know many times even situations they don't agree on a single set of tools so in
• 03:30 - 04:00 in this will get to where we want to in terms of providing uh data event decisions okay there so again as mentioned no so there there is a specific tool for the right job uh don't get overwhelmed uh let's say which one do i use okay so there's certain things that you'll want
• 04:00 - 04:30 to consider okay so first is an obey objective that and what is our objective for doing the test now so what do we want to describe or and be able to characterize nothing you describe your population or your example there that we have the one do you want to do that do you want to describe it or maybe perhaps you want to classify them no or categorize them so we'll pay them or no and sometimes you want to make you have a comparison no say between two the one i know let's say you have the long data sets um
• 04:30 - 05:00 between different you know uh different trainers different methods etc you want to compare no if if they're actually i know if if they are uh you could make comparison as well and the other one is well after studying it maybe i wanted to predict performance so american prediction and the other one is you want to explain why variations here versus the other also that's what you
• 05:00 - 05:30 want to do so again at the end this would be the typical ones that in terms of objectives and you should usually use nothing knowing though it either want to describe data you want to classify them you want to make comparison want to make a prediction or we just want to explain doing drivers tank variation which is very important in our case okay um so that's one consideration it's an objective the other one is no you could have one variable two more
• 05:30 - 06:00 immense answer okay so yeah that could also be a consideration right and then of course i know you're adding classification and data earlier it also depends on whether it's convenience or discrete no in fact uh if we we've gone through a couple of them already the control chart they bought okay so we'll simplify it up to this actually metal patterns there's there's
• 06:00 - 06:30 two more like um whether the the variables are dependent independent and they're automated but you know we don't have to go deeper into that no um again we'll just simplify things over that log all right and then what we will use as a guide is we'll use a simplified statistical decision tree now so this is semi parang analysis no but this is a guide not to help us i identify
• 06:30 - 07:00 so this one's in amplification adapted though from from a more comprehensive one but they just simplify it to give you a simpler guide okay so it in in general is what it looks like okay here you'd see you know um basically is how many barrels do you have so if you have one variable and then another objective nothing no so if you want to describe and we only have one variable okay so we want to describe
• 07:00 - 07:30 let's say performance that's it no whether that's volume or you know percentage you learn just just the performance okay so here is some variable this is these are the things that you can use depending on your objective so here you see described so again we've already tackled this no descriptive statistics we get the mean median mode beyond standard deviation quartile range and so that's how you describe them and then of course distributions we've already also gone
• 07:30 - 08:00 through this already no young histogram box plot mineral variation tonight frequency distribution um stem and leafy and so we we've gone through that already okay and then of course normal or not normal data so it's also part of that so this is this one no so we've we've tackled you know and there's some darling of the mighty test so that's that's a way to describe the data no pakistan
• 08:00 - 08:30 you want to classify them we've also gone through this as well no we did filter sorting they're all sort of part of data stratification at all in classical analysis we also actually in in in essential neon clustering systems or something you know it was happening earlier you did cluster some sampling so financing data into groups and then you do this so after you you divided it into groups you you still describe them so that's practically it union peter system
• 08:30 - 09:00 and cluster something nice to divide the data into groups and then do the do these other things to also describe and then etc in comparison for this one in general we're going to use of course this one population test no and depending on whether it's discrete or continuous so you'd have this but in general this is this actually you know nasa lines of hypothesis testing no this part is a t-test it's among those though um and it's so so so with this one and then of
• 09:00 - 09:30 course control charts we've already gone through that okay and then here uh made up my predict with this one casio lana machine dependent variable there's no other variable so you can't really predict and explain a variation yeah with with respect to another because it is some variable they're just sorting the applicable compartment okay so that's these are the things practically at the younger meeting if you only have one variable okay now if you have more than one variable okay um
• 09:30 - 10:00 in fact now you can actually use cluster analysis but and then do this do the rest though so here more than one variable at a youngest commons happen likely because we're going to compare our output with our input variables so so same in terms of describing objective that is to describe it very very similar get a parent no so you'd have descriptives of the stakes and dopamine because you're going to uh we're going to describe uh this input variable
• 10:00 - 10:30 mean median distribution etcetera so that is still part of it uh you're going to we're going to do that for both the input variable as well as the output variable so same goes with this one so very very similar i'm going to take the glendita i would say in fact we've also sort of gone through it as well new correlation because analysis you have two data more than one actually variable so now you can correlate them so we can correlate and we've earlier discussed about you know scatter plot
• 10:30 - 11:00 and uh as well as your correlation coefficient so we've in fact we've discussed that one as well so again so since there's more than date there's more data now so we can do that we can now correlate on top of what we have earlier which is just uh this it don't describe like the methods all right so but more than two variables and it's the more than one variable in describing it so it's this you basic and then a portion for relation uh which if you
• 11:00 - 11:30 look at it over the net in the end okay so next you classify them and this is in the background no so we're going to do the same it's all about certification balance so we do that as well and then when you get to compare if it's you know then it again it this one goes in the realms of hypothesis testing which kasama genuine analysis of variance which is you know the difference of means basically of of two different data sets and so this in general um you would sort of put this in
• 11:30 - 12:00 the in the column of hypothesis testing so depending on a and we'll discuss this we'll have a session separately for hypothesis analysis uh hypothesis test no so all right again not not to worry we'll discuss this part in a positive restaurant okay so again uh i think until towards the tail end of our i know it on next few lessons it will be mainly i know we're looking at correlation regression and hypothesis testing now so that sort of pretty much covers what's
• 12:00 - 12:30 in here uh once we get to complete that so in a regression here you can see you get to have once once and on um they're correlated then you can run regression analysis and it will allow you to explain your variation between uh variables and be able to predict performance so yeah and you could use this one okay but i mean a lot of mana kinds of regression so that's just regression in general and the other one is hypothesis testing okay uh but if you look at it for the
• 12:30 - 13:00 for the vast majority you know other than hypothesis death and regression the covert and athenian so you know you're almost there you're almost there so okay you've made it this far so you're actually almost there okay um um i forgot so meropan must must i would say more a bit more complex ones because uh we didn't get a game so we just use things that we need usually in terms of improving the process because
• 13:00 - 13:30 they only use it for statistical research so we don't really do that let's start our purpose no our purpose is just to get enough information enough data information convert that to wisdom learning knowledge etc with the goal of being able to improve our process though dinamata you wanna go and we're using data in the context of doing improvements okay so there it is
• 13:30 - 14:00 no um i i place it here parapan once once you what they call this once you but materials for the places control your discussion earlier but this one i just because my guess is you're going to print this so latin in terms of if you want to go back you just go back to this slide now so earlier with the discussion
• 14:00 - 14:30 so we had the control chart okay which ones do i use where is your data discrete or continuous and then if it's continuous basically and so if in if it's individual then you have individual and and moving range chart if it's uh more than one and then but less than eight actually but different schools of thought not eight to ten is fine no then if it's less than eight or less than ten then you could do x bar r chart and then more than that x bar s chart and so forth no
• 14:30 - 15:00 and then this one discrete and no matter what what data do you have by defect span and uh misunderstand so these are the things though um constant yes then use this if no then use this and so same goes with this okay so again it's just a guide no uh instead of you going back to those slides and being kind of
• 15:00 - 15:30 so but of course you could always go back is already with you know examination so it's easier so when you go back to your meeting so makes it easier for you to come back you know whether it's just the general one or you want something specific to control chart okay so there i wanted to just make it easy for you no so um uh yeah that that should help you in terms of uh being able to determine
• 15:30 - 16:00 but you know it could really overwhelm you no and and i understand that personally i i experienced that myself so hence having something like this like a guide okay i don't got going if i want to do this again very very clear no i say it aligns to what you want to do you know typically we just want to first describe it then afterwards we will stratify right union progression and nothing training in fact no afterwards no we will want to see wondering factors
• 16:00 - 16:30 giving a certification to io how does this factor impact the other so do the time hypothesis testing and then of course you regression analysis okay so there that should simplify it for you again i'm going to do is you have a road map if the electrician did nothing as we go through the analysis and as you can see for the majority of them or when they're not in okay so we'll just go through yunya young it's a little bit more regression and then hypothesis testing okay and and you
• 16:30 - 17:00 should be fine measuring you should be solid strong now we don't need all of them now we just want to be smart and just use the things that we need all right so i hope this was helpful i'll see you on the next video mabuhay [Music]