Statistics: Correlation and Regression Analysis in SPSS
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- Опубликовано: 21 авг 2024
- This video shows how to use SPSS to conduct a Correlation and Regression Analysis. A simple null hypothesis is tested as well. The regression equation is explained despite the result of the hypothesis conclusion.
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Hi
frankly speaking and allow me to say that your video is the most valuable, clear and simple video on YouToub.
> simple and clear language
> focus on the important areas (why, when, how..)
> slow enough to understand and absorb
> good example
KEEP IT ON YOU ARE GOOD AT IT MAN (THUMB UP)
We have learned for a whole semester about doing applied linguistics research and reading papers, yet our so-called professors never clearly impart us with the necessary SPSS techniques and reasonable analysis. They might themselves get baffled in understanding the data. It shames me to say that I am already an English postgraduate from an "eminent" university in China. So I am about to learn SPSS by myself. Thank you!
This is by far the most enlightening and helpful tutorial I have viewed on the topic. I will seek more of your tutorials. Thank you so much!
This is the best regression tutorial I've EVER watched. Everything was very clear and well explained. THANK YOU!!!!!!
Man, This is the most clarifying tutorial I've come across. Thank you. Thanks a lot. You're doing a fantastic work. Keep it up. Best wishes to you. :)
Shubham Lahan... Thank you very much. It's encouraging to hear that. Good luck to you!
Same, this is extremely helpful.
I second.
Excellent video, with CLEAR explanations, especially for SPSS novices and statistics allergics! Thank you for sharing!
Finally I understood something. Its great that you have used the ppt parts to actually tell us what all those stand for teaching the basics. My dissertation draft is due tomorrow and this helped me a lot. Thank you so much.
Thank you for this videos Prof. Agron.
I am preparing my business statistics exam and your videos are very helpful.
Si shqipetar nuk ju fshehi dot edhe emocionin qe me krijoj kur pash emrin tuaj, faleminderit shume!
You're very welcome Nardi! Hope your business statistics is going well...
Correlation and regression analysis were explicitly clarified to a logical conclusion in this video. Thanks a lot
It really gives the clear concepts on improving the skills of statistical inference
Thank you!
The best video I've seen on correlation and regression. Thanks.
This is a clear tutorial. You have explained it well, especially when you spoke of both the results, and you further went onwards to speak about PREDICTION. Thanks,
I wish I had the data access so that I could practice at the same time. The font is small also .
I will make another video for correlation and regression, hopefully more qualitative than this one.
What a crystal clear explanation sir, we are grateful to view this session .Let your service continues and light lamp for many who are in need
Thank you. Appreciate it
This was a very helpful video. Thank you for posting it
Great video and great explanation.It makes Regression Analysis sound easy.Thank you!
Huge thumbs up... you deserve more than 5 stars for this great tutorial. Very helpful... many thanks! Jimmy Aldido
A very comprehensive presentation on correlation and regression. Keep it up Agron.
Thank you kindly!
CRISP AND CLEAR!!!
If p is smaller or equal to 0.05 you must reject the null hypothesis, if your alpha level (reference probability) is 5%. So, yes, reject it even if p=.05.
Thank you so much!!! Your video help me a lots when i am suffering. Million thanks have to express for you. Appreciated
Thanks Mr. Kaci !! This was very useful to me :)
Best wishes from Albania!
wonderful video. explains everything anyone needs needs to know for correlation and regression. thank you very much.
Thanks for the very CLEAR explanation! It does help!!
Great! Video and explanation.....Thank you so much for such a clear and concise tutorial.
The p-value represents the chances that your Null Hyp is true. The smaller the p-value the less likely it is that your null will be true; therefore you reject it if the chances are less than 1 in 20, or 5 in 100 (hence 5%, or alpha 0.05) that it is true. P-value larger than 5% (this reference probability is chosen in advance - depending on the field or research, or on the seriousness of your work and test) show a greater likelihood that the null would be true, that's why you do not reject it.
Well done Mr. Agron! I'm proud of albanians like you.
Faleminderit Shpend!
Perfectly explained! Thank you so much, you made it very easy to comprehend.
Great to hear!
your explanation is clear and precise...thanks a lot
It was very helpful Sir. Your effort is appreciated ! Thank You
Thank you so much for this tutorial. You made is so easy to understand.
Simply the best. Respect to you Sir.
you have just made my concepts clear.. awesome tutorial...
Your video is very simple and answered some of the basic questions I had. With regard to the value of constant, I was told that it refers to other factors that are affecting the independent variable other than what we have considered (in this video, GPA). Please let me know if I am right or pl explain
Thank you for sharing these information and simplify it, I am dealing with multiple regression to assess the impact of the provenance of students (Home/International) and the course studied (MBA/MSc) on the employability of graduates in a University in the UK, as there are many independent variables, I am confused whether the correlation coefficient represents the correlation between all the variables (dependent and independent)!?
Thank you for the very simplified and clear explanation
Thank you very much Sir, your explanation is very clear!
Thank you for uploading this. It made things very clear!
Thank you Mr. Argon but usually we look to the Beta and not B which is 0.271 (slope)...please clarify
Thank you. Really helpful in understanding. Can you include more videos on how to write these results out in a manuscript? Thank you
Your comment is very much appreciated! I'm glad you have liked it.
I wish everything was explained like this!
Thank you! Now I know what to do with my weak correlation!!!
That about 14 minutes vs my entire education.Bow to you.
I tried several times to understand the correlation and regression but failed. This video helps me to understand
Hi Sir,
May i firstly thank you for such a clear and concise lecture on correlation and regression and secondly for some advice. I am currently comparing 4 indep variables to 1 depen to test correlation and which has highest sig. However all four have resulted in .000 sig. How do i determine which is now more statistically significant between the four? Your guidance would be much appreciated.
This is an interesting session. very simple and adding value for beginners. Awesome.
Crystal clear. Thank you!
thank you. very useful for my thesis.
Awesome video. I am currently writing a research paper using these measures. How would I differentiate between correlation and regression? Just that correlation is based on observing if there is a relationship and regression deals with being able to predict the dependent based on the independent?
That is basically it! However, mathematically, these concept are just about the same thing. Statistically, although you may see a high correlation between two variables, it does not necessarily mean that there is causation. Correlation, basically, tells you whether there is an association (existence), if this association is positive or negative (direction) and the strength of this association.
If you've accepted your null hypothesis, then why would you then use your line formula as a predictor for age and GPA? I thought we just determined that age doesn't explain GPA with statistical significance?
Hello Nicholas! Firstly, thank you for sharing my video! Much appreciated! Secondly, we never accept the null hypothesis, since we cannot create a new fake notion about the initial idea; we just fail to reject it, in which case we do not have enough evidence to accept the alternative hypothesis, which was our main research idea. Thirdly, the reason I explained the regression equation is for academic purposes only; just to show my students (in the video) how to properly set up and solve such an equation, the conclusion of the hypothesis notwithstanding. Thanks!
Agron Kaci i have 4 question in questionnaire that represent dependent variable and 3 question in the questionnaire represent independent variable 1,how can i combine all this question to represent depended variable and independent variable 1 because i want to use dependent variable with Independence variable in correlation analysis?
anna melissa
Anna, yes, you can combine variables into one variable and then use the combined variable for statistical variables. However, my video does not demonstrate SPSS' commands. A simple search for how to combine variables will get you to where you want to be.
why when i try doing my reliability test,i get cronbach alpha number section but i don't get any number from cronbach alpha if item deleted section? it just show a small letter (a) saying it was negative value?and say my coding is wrong?i check triple time already and nothing is wrong with my coding and key in data? :( the weird thing is i able to do the rest of analysis with that data :(
Excellent explanation thank you so much!
very clear and straightforward thank you!
Yoespañolasmegutariasconocertehablar
You're welcome!
Good and simple explanation.
Thank you! This helps a lot! Do you have a video on confidence intervals?
amazing tutorial which i have seen so far
You're welcome!
Its really good. Thanks a lot. Waiting for more videos from you.
excellent presentation. well done
Saw and really liked this video. But I have a confusion regarding my analysis, as my topic is 'Impact of brand extensions on parent brand image'. How can I find correlation and regression analysis when you have more than 2 variables. Would appreciate your response towards this.
You need to do a multiple regression, where the independent variable field would have all your predictors, which seem to be "brand extensions".
thank you for the amazing tutorial :) it is really simple and easy to digest :) you are amazing statistics teacher really thank you !
Great lecture
it is very helpful! Thank you so much!!
I believe you should use a p-value of 1/100= 1%. Your confidence will be higher that you have the correct result ;)
Amazing explanation!
Thank you very much.
Thank you so much! Very helpful!
Thank you very much, it was very helpful
Very helpful, thanks
Thank you so much !
It is so clear. Thank you so much.
Excellent.
Thank you very much ..do you have a video on multiple regression
i love this tutorial
great video! :) Helped a lot :)
In your example you show firstly there is no correlation between the two variables, but, then, using the same variables, your regression equation shows a relationship between the two, hence the ability to predict. Is that possible with the same data? Or am I missing the point (that's fairly likely!)?
That's what I am trying to figure out...
Please note that there is a correlation between the two variables. It is just that such correlation is 'weak' (i.e. 0.271) which is less than 0.3. The regression analysis further supports this notion as it indicates that the strength of relationship given by the two variables used is only 7% meaning that there are other factors (amounting to 93%) that determine the relationship between the two variables.
Murtaza Mansoor Excellent, thank you.
Johan! Apologies for replying so late. Your question is excellent. The correlation coefficient, r= 0.271, is very small, therefore showing a weak correlation between the variables. The regression definitely shows the same thing, since mathematically, they crunch the same numbers the same way. Therefore, it logically follows that the relationship is insignificant. When the relationship is insignificant at the alpha level, we must retain the null hypothesis, therefore we find that we do not have enough evidence to accept the hypothesis that the relationship is significantly strong. Now, why did I bother and explained the regression equation when the model is insignificant? For pedagogical purposes! I did not want to create another video with other variables or datasets where I could find a significant association for regression purposes. That's all...
Agron Kaci Thanks Agron!
Thank you!
Are correlation and regression analysis applicable as nonparametric tests? does it go with convenience type of sampling?
Agron Kaci thanks for the tutorial, however I have a question , if I have to find the relationship between age and salary and the given salary is current annual salary and starting annual salary which test to use for it and which salary to choose as variable with age.
Dear sir,
Can you explain how we can analyze liker t scale questionnaire and
use the result seeking mean,median,and standard divination including relation ships.thanks a lot sir
I need more lecture to understand spss internationally
Thank you sooo much Sir !!!!!
best for learners........
Thank you! Appreciated!
My friend would you please tell me when we use correlation and when using Regression? as far as I know correlation is to examine the relation between IVs & DV but then what the regression is for? please simplify the answer as am not from statistic background.
Thanks for showing the easiest way to understand about variables correlations. However my independent variable show -.143. So does the variable is not correlate with my dependent variable? does it show negative relation between two variables? after that i did multiple linear regression and again the number shown for my variable 1 is positive and my 2nd variable is negative. What does it mean? thanks for the help.
Thank you very much.
Thank you very much
Faleminderit 🇦🇱
Thank you
Thank you, I Need it.
thanks you ..i need it
Thank you so much
thank you so much!!!
Great...Stats made easy :-)
thanks .it was helpful
Hi, I have the same analysis. However, my age dataset is in range (ex: 0-7, 7-9, etc.). What should I do to analyse this? And should I choose R pearson correlation analysis? Thanks!
Anissa Yuniashaesa
Variable Age as you have it, makes an ordinal variable. Please make sure that you meet all the assumptions, one of which is the variable should be at least on an interval level of measurement.
if p value is exactly 0.05, should we reject or accept the null hypothesis? thanx in a dvance
thanks
Say we have a project in finance that is tacking the effect of
omni-channels business strategies on competitiveness of commercial
banks. The channels are e-transaction, e-communication, e-banking, and
e-finance, and these make up the independent variables of the study. In
development of the questionnaire, each variable has 6 items/statements
on a 5-point likert scale. Moreover, the dependent variable on
competitiveness has six statements that should be rated on a likert
scale. Now my question is, how do i carry out correlation analysis or
regression analysis going by the fact that there exist so many
statements under one variable?
I would think that you may need analysis of association, hence chi-square (cross tabulation), because your data is ordinal, not ratio.
That is well understood. How do you go about variables of the study (independent) in a questionnaire having particular statements, say five each and requiring responses on a 5-point likert scale, and you need to carry out regression analysis? In SPSS, all these statements/items of individual variables will be there once you click on regression then linear. Help here.
nice explanation
Is the value of alpha 0.05 by default? For all calculations and any set of data while using linear regression?
Kavan Puranik,
In social sciences, probability level is usually 5%. This value is to guard against making Type I Error, where you fear that you will reject a true null hypothesis. A true Ho would be when data shows no relationship or difference. For example, when you want all your products to be the same and they are, but you by mistake reject that and decide that some of your products are different. Because you think they are different (when they are not) then they are not fit for sale, and you have to throw all of them in trash and make others. This error cost your business. However, 5% error is generally tolerable.
If you want to guard more sensitive things in like, such as health, then you may want to use 1% probability level, or 0.01 significance level. This can be when testing new medicine, for example. You should not tolerate a lot of error in medicine, because that may affect health and life.
Hope this helped.