Statistics 101: Multiple Linear Regression, Data Preparation

[@2002budokan]

Crystal clear explanation. I think I've finally found a non-boring statistics channel, with a wise instructor.

[@lubersonjoseph5700]

he is the best.

[@juheidiheithaguilar9898]

I agree

[@dahliajohamamcy3679]

Me too.....

[@MellyGats]

Yes, not boring, but also without cheesy, annoying theatrics!!

[@sulaimanoreshile8988]

I am a Nigerian academic and a PhD student in Southeast Asia, I am marveled and fascinated by your unique pedagogical nuances as well as the serenity and calmness in your delivery. This is a rare quality of a TEACHER . You are gifted! I envy your students! You are the BEST! thumbs up

[@megancarlson7290]

Whoa. Been struggling for days with the data for my MSc. in Global Health thesis. Your videos have broken the principles down to their basics, and built them up to re-form the whole picture. I can't thank you enough.

[@MaddyLovesBase]

I'm currently enrolled in an MA in humanities, examining emergency and disaster management, essentially I'm the only person in my entire cohort doing a quantitative thesis (survey based examining if disaster preparedness is linked to various factors such as demographics and health and well being) and thus my program focuses on qualitative work. I've been teaching myself statistic. Thank you so much for these videos, they are literally a life saver.

[@malikabra1449]

Hello!! I am preparing my thesis too and my work also is survey-based and have qualitative data. Could you plz answer my question? (maybe you have an idea since your work is related). My data about residential satisfaction is collected on a Likert scale. I calculated the overall satisfaction so this is my dependent variable and the calculation is based on the mean value for selected attributes. Now I want to know if it's possible to run a multiple regression using the same attributes and consider them as independent variables to know which one accounts more for the variability of the dependent variable (the satisfaction level).

[@MaddyLovesBase]

@@malikabra1449 haha sorry that was five years ago... once I defended I pretty much forgot everything

[@malikabra1449]

@@MaddyLovesBase hahaha yeah sorry about that!! I was excited to find someone in the same situation. Thanks

[@therealjakub]

sooo did you use SPSS? or what tests did you conduct in your thesis?

[@MadhuKumarKilli]

Your tutorials give me a sense of confidence that despite being from a commerce background i too can gain the expertise in Data Science stream with these ultra clear statistics concepts.

[@BrandonFoltz]

Finally live. Enjoy!

[@ejaycatamora7297]

Thank you!

[@juliansneve4835]

I'm currently pursuing a professional certification in Data Science and while beginning work on my inaugural capstone project I've been, for a lack of better words, blindly cleaning my data. Watching this video opened my eyes to what my end goal of data wrangling should be. Thank you, Mr. Foltz, for the clarification and very likely saving me a ton of time.

[@CreekCat1]

Oh my gosh, thank you. Been trying to figure out what to do with my data from my thesis work on wetlands. No help from my adjunct adviser and I've had one stats class...in 1998. This particular video with the steps for the prep work is finally helping me visualize what I think might work. You are an excellent teacher. Would be so cool if you use some ecology, wildlife, or environmental data in your samples.

[@ericjohnpajo5536]

My thesis proposal proposes a research that uses Multiple Regression. Thanks a lot!

[@sharong9869]

Brandon, a very clear, simple, effective, really easy to understand teacher for difficult subjects.

[@jcdentonunatco]

Thanks for making these! Its amazing that I can learn more in a day of watching these than I can in half a semester from my prof!

[@superhari1972]

Great video, very simple and explained with suitable example. You are a good teacher too..

[@robertwaller7699]

THANK YOU. I type this in capS...THANK YOU. In a r statistics class, and in one weekend you helped me understand and also write the code to mirror your analysis. I understand SST, SSR, SSE, R, r squared, etc. Keep making the videos. For the first time, in all the gofundme's in the world, I contributed to yours. Can't thank you enough for making these videos. Note for those coming across this, the exact process Brandon is going through, my professor in my Phd program wrote the exact same correlation process. Each step is the same. However, Brandon is explaining it to the degree in which I understand it now after just a weekend. THANK YOU, THANK YOU, THANK YOU!!!!

[@MIBCN2008]

Very clear explanation. Wish my teachers at uni were like this. Thanks so much for your videos.

[@josephinew3634]

I found your vidoe coz I'm doing an assignment involved with multiple variable regression. My lecturer and tutor couldn't explain it clearly, and didn't give sufficient instruction about how to do it. But your video is awesome. It saves me from struggling with my assignment, and what surprises me is that your example is highly similar with the scenario in my assignment. Thanks a lot.

[@DeepLakkad]

Thank you Brandon for providing such an amazing community service. I am sure that this is helping thousands of people around the world who want to pursue their career in analytics or data science domain. I would really appreciate if you can add videos of SVM, Random Forest, Decision Trees, Naive Bayes, etc. And, one of the problems we tackle is undersampling and oversampling while dealing with the non-uniform distribution of target variable. I am sure it will help a lot of people especially me who want to clearly understand the concepts behind it in a very in-depth manner.

[@jaredsmith3996]

Thank you so much! These videos have greatly enhanced my grasp and understanding of regression. Having no prior stats experience before my current MA program, these tutorials have become inexorably linked to my econometrics textbook readings and class preparation.

[@xuhongzhang4821]

Thank you very much for these well-made tutorials! A small suggestion though: I think the sea salt vs table salt example is not appropriate to explain multi-collinearity. Multi-collinearity refers to the case where one independent variable can be predicted from another. The existence of table salt doesn't seem to be predictable from the existence of sea salt. In fact, their existences can be made independent to each other, but of course their influences on the output (taste of the dish) are not independent.

[@djaurit]

Thanks Brandon! Can tell you have the heart of a teacher and a great mind! Appreciate the way you present the material and the journey through it. Also appreciate the use of a consistent example. Best wishes.

[@girljorge]

Thank you Mr Foltz for posting these videos. They really helped me in my statistics class this fall. I went from being discouraged to earning a B in the class. Thank you! You truly have the gift of teaching.

[@aisamarnie]

Brandon, I'm so grateful that people like you exist, you help so many students around the world, thank you!

[@ineschaby6122]

This guy is saving my life - I was so lost before I found your videos! Thanks !

[@esthermetieh6320]

I'm watching this video 7 whole years after it was posted. Statistical analysis for my MSc data brought me here and I'm super glad I found your channel. I no longer have palpitations when thinking about multiple regression. Thank you very much!

[@BrandonFoltz]

Glad it helped Esther :) Deep breath. You can do this.

[@esthermetieh6320]

@@BrandonFoltz Thank you for the encouraging words!🤗🤗 Really needed it!

[@tamammohamad922]

I invite teachers of the world to adopt your way of teaching! I cannot thank you enough!

[@MRradicalmj]

These lectures are excellent. They explain the subject FAR better than my text book does. Thank you so much for posting.

[@ClaudioA-tq2ex]

Finally a tutorial with a real world example that is clearly explained, great work Brandon

[@gudusangtani]

Best explanation i have heard so far on regression. Very few people have such a teaching talent

[@netmadefamous]

You took so much time and hard work just to teach us .. I have no words to thank you ,on the occasion of Teachers Day in India i would like to thank you for all the hard work you have done for us..

[@abhinavbhatnagar5651]

I m a big fan of yours. Here is one compliment that's suits you " You don't understand enough, if you can't explain it simply " . Your explanation helps to understand the depths of complexities. Or rather I should say you explain complex problems so simply which helps me getting better and dive more into data analysis. Keep doing good work. Thanks for everything you do.

[@MAbdullah47]

Thank you so much about the materials you provide , it's really helpful you make a regression analysis as a peace of cake

[@cristinapraderamos6548]

OMG, I wish I new this channel before! Im doing my MSc with lots of stats and you're saving my life hehehe amazing lectures, thank you for sharing your knowledge!

[@eswan7638]

This is a truly excellent series. I was trying to understand the ARMA models for prediction and I could not understand the terms. Now I have began seeing what the terms used there imply.

[@desertstormmarine]

Brandon, great stuff! You have saved me from total mental rejection of this subject! You should be my professor!! I am at a University in San Antonio, Texas and have struggled up to this point (Simple Linear Regression) and you have turned it all around!! Thank you sooo much!!

[@Thecollector33]

Thank you for the videos. Learned a lot between simple linear regression videos to this one. Even the nitty gritty math of the coefficients makes sense!

[@abdallamohamed2374]

Looking forward to enjoying part3. Thanks so much for this thoughtful & well-organized work

[@greggapowell67]

Brandon. Excellent!... You've made quick and easy work of going back and renewing my understanding of Multivariate regression. THANKS!

[@abhijitchhaganpatil]

This is really Helpful. Your videos have always cleared my concepts in a very less time. As a professional in analytics industry I find your work very useful whenever I need to refresh my concepts. Thanks *****. Can you also do a couple of videos on logistic regression?

[@MsSakony]

Abhijit Patil j

[@vickylin715]

I just discovered your channel. I wished I had done so earlier! I like that you explain at a reasonable pace and give a brief summary at the end of each step. You are a very skilled teacher!

[@sonyarutherfurd1065]

Your teaching is amazing. Thank you so much. There is nothing else out there that compares

[@rakotondrazakatojoherilant6403]

5 years ago ‘ s video and still outstanding 👍🏾 well done

[@ericl.gurley6830]

Thanks for your work here! Clear, visual, and real examples. Again, thanks!

[@rtoro90]

Great video! You just did in 20 minutes what my teacher couldn´t do in a week. I will be waiting for part 3. Thanks.

[@BrandonFoltz]

Rafael Toro Thanks so much! And Parts 3A and 3B are now live just FYI.

[@mkwarlock]

You are the best. I have never seen an explanation as good as the one given in this video series. Amazing, I love your work, and I'm very thankful!

[@gabrielraoust4068]

Amazing videos. Very pedagogical. Your Voice is also perfect for this. Thank you!

[@jeffw2672]

Nice video! You lectured very systematically and clearly and you were very careful for each term/jargon that could confuse us. You are really thoughtful and well organized. Thank you god that you made this video. Really appreciated!!!

[@ZeynepAmbarkutuk]

These videos are perfect! The explanations are great and content is very well-organized. Thank you so much!!!!

[@mulugetarussom2526]

I found the Stat 101 presentations as "life saver" as Madeline stated. Thanks so much Foltz!

[@Maris2012]

Forecasting is the most difficult subject for me! I'm so glad I found your channel! I have a great professor but I like your teaching style! thanks so much!

[@gvott1]

You are the best professor in the whole world!!!!!!

[@EzioOmer]

This teacher is awesome. Thank you mr brandon.

[@saraouanes]

Thank you for your effort. You are very good at Teaching. You make Statistics look easy. I have learned a lot from your videos.

[@denara5004]

Just want to say thanks for this! MSc research brain fog has lifted thanks to your clear explanation. I was able to find my way again

[@evilmorty-tv1xs]

Just stated with machine learning and found your videos. Really helpful content. Thanks Dude !!!

[@Bert1234100]

Thank you Brandon for these very clear and comprehensive videos. If I may make a suggestion, it would be to add videos on how to get the results but from SPSS. We are using SPSS at my University and all students are allowed a SPSS license as a student. Another suggestion is to make videos using an open source software, such as GRETL, which is totally free and unlimited in terms of variables to work with. People that do not have access to statistical packages around the world can use GRETL and still learn by applying the concepts shown in your videos. Thanks!

[@sergemiguelkl6207]

Very helpful, clear explanations, really appreciated the way through you introduce topics, always come back and recall why this.

[@Oneminutewin]

Thank you for your help. Make more regression videos please! You're videos are the easiest to understand on youtube!

[@prasadiamaratunge1368]

This is truly helpful. Thank you and May God bless you

[@ChumePeterNhanombe]

Thank you for the videos. you made my master studies much simpler

[@JackIsNotInTheBox]

The repetition in the concept is very helpful when you were explaining it.

[@beatrixfriedeberg6339]

could not agree more with Megan. This is excellent. thank you

[@MegaSpicyT]

You are a life saver! Well presented and clearly explained. Thank you!

[@anilnampally2660]

fantastic ...complicated topics explained in a simple way

[@veegenius6750]

Brandon! Your da' man! Thank for sharing the knowledge and taking baby steps with us! My journey with you started with - like, then as I watched more on stats it went to subscribe then...the level went off the scale!!!! I'm beaming all the way to understanding this quantitative method which can be used as a research method! - Now, I feel empowered by your teaching! Appreciated and will pass on the links. :-) you have provided. Kate

[@sbarter]

rly helping me understand the concepts of machine learning. Thanks

[@shijirn3108]

I am so happy to find you, crystal as clear explanation thanks

[@pradeepsharma30]

Fantastic!!...complex topics explained in simple manner....very helpful!

[@kayodeadechinan5928]

Such a wise instructor! OMG

[@ramamoorthyselvamuthukumar5822]

Wow, a clear shot explanation, Thank you so much....!!!

[@yidenekachewworku7744]

IT IS WONDERFUL PRESENTATION, THE TERM REGRESSION AND ITS APPLICATION BECAME EASY THANKS FOR YOUR TUTOR

[@monazaizan947]

I really enjoy your slides. I appreciated your efforts.

[@juheidiheithaguilar9898]

So clear. Tnx for doing these presentations.

[@zalida100]

Excellent video. Having the link to the playlists is very very useful, making it easier to review videos in sequence etc. You score extra for doing that :) - Thanks v much.

[@ErikFKL]

Thanks Brandon, i am new to statistics and your videos help me a lot!

[@mbizglobaljoe]

Thank you so much for your informative lecture. It was really big help for me.

[@dexterpante]

Always excited to watch brandon's tutorial!

[@BrandonFoltz]

Dexter Pante Thank you Dexter! Always glad to hear that kind of enthusiasm!

[@sathyanarayanankulasekaran1674]

You have made my life really easy...awesome explanation :)

[@boburjonmamatov5079]

Thank you so much!! You explain very clearly!

[@mildredaviles4829]

Thank you for making it simple to understand.

[@VinodRS01]

♥️ simply awesome ,👌 best explaination ever i got

[@michaelpappas3857]

Another outstanding video! Thank you Brandon!

[@Stitch_spring]

SO beautiful! can you do a tutorial on neural networks and the math equations behind it? Also trees? :-) Im not sure if you already have a video but I will check your channel once more.

[@anubhutisingh5778]

only if I had a teacher as cool as you! thankyou for all the help

[@ivanlozano305]

well explained! we need more content like this for statistics

[@hasansaeed837]

Brandon Foltz Thanks for these fruitful lectures

[@rnd322]

amazing! I've learned a lot in 40 minutes more than weeks of my stat class

[@perreshelton2205]

+rnd322 I have too...so happy to have found these videos. I've literally watched all of them from Simple Regression up through Multiple Regression.

[@khalidtasand3858]

Thank you very much, I hope long life for you sir.

[@abdiwarsame6743]

This is excellent explanation!! thank you very much posting it!

[@Nico-jc7zr]

Your series is so well made. Really good work.

[@shawsr7]

Thank you. I learned something today.

[@onbrowserapp7186]

This is fantastic!!!! Thank you very much!

[@sumitbose7873]

Hello Brandon.. firstly, your vedios are really awesome & good for learning. I have one question in this vedio i.e. you have mentioned here that .05 is the threshold for P value. From where we got this threshold value .05 for P and if some variables P value is close to .001, how should we determine the relation based on P values? Could you pls. explain ..

[@govamurali2309]

This is amazing, please do advanced topics such as supervised learning,svm etc

[@mohamedelogail1992]

Those are really awesome, useful, and highly illustrated videos, Can you please make a series about how to calculate the "Gradient Descent" and when it is useful to be used?

[@katerinaboididou8601]

Thanks so much for your helpful videos!

[@maryamahmadij.3128]

omg..................ure amazing..............................thank you !!!!greetings from Korea!!!

[@sachchhca]

Very easy to understand. Thank you so much!

[@angtoddjohnson]

You are gifted at this!

[@inkyukang2358]

fantastic free video, thumbs up x 100!

[@jcAugur]

One note from the one grad econometric class I took a few years ago. You sometimes do want to include a non-statistically significant Xi in your model if there is a big beta and a good theoretical reason to do so. But this is likely at a more advanced level.

[@lalualiyyatirangga6466]

Thank you for clear explanation. What is the limit for number to distinguish strong and weak correlation in case of multicollinearity?