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Stats4Everyone
Registrace 10. 10. 2013
Why the constant is squared when finding the Variance of aX. Proof that Var(aX)=a^2Var(X)
Why the constant is squared when finding the Variance of aX. Proof that Var(aX)=a^2Var(X)
zhlédnutí: 666
Video
Solve rational inequality by factoring and using test points
zhlédnutí 87Před 9 měsíci
Solve rational inequality by factoring and using test points
Using Z (Standard Normal) Table to find the Percentiles for X and a Sample Mean, X-bar (using CLT)
zhlédnutí 101Před 10 měsíci
Using Z (Standard Normal) Table to find the Percentiles for X and a Sample Mean, X-bar (using CLT)
Why is X non-random in Linear Regression? What does it mean for X to not be random?
zhlédnutí 868Před rokem
Why is X non-random in Linear Regression? What does it mean for X to not be random?
Proof that the Sum (xi - xbar)xi = Sum (xi - xbar) * (xi - xbar) = Sxx
zhlédnutí 2,9KPřed rokem
Proof that the Sum (xi - xbar)xi = Sum (xi - xbar) * (xi - xbar) = Sxx
Deriving the Binomial canonical link function, logit, for Generalized Linear Model (GLM)
zhlédnutí 2,1KPřed rokem
Deriving the Binomial canonical link function, logit, for Generalized Linear Model (GLM)
GLM Link Function, and Canonical Links
zhlédnutí 3,4KPřed rokem
GLM Exponential Family, Finding theta for Normal distribution: czcams.com/video/wqIcv3Wehug/video.html GLM Exponential Family, Finding theta for Poisson distribution: czcams.com/video/OfvWpRxISU0/video.html
GLM Exponential Family, Example with Poisson distribution finding functions a, b, and c
zhlédnutí 2,2KPřed rokem
GLM Exponential Family, Example with Poisson distribution finding functions a, b, and c
GLM Exponential Family, Prove that Var(Y) = b''(theta)*a(phi)
zhlédnutí 1,2KPřed rokem
GLM Exponential Family, Prove that Var(Y) = b''(theta)*a(phi)
GLM Exponential Family, Prove that the mean is the first derivative of b, E(Y) = mu = b'(theta)
zhlédnutí 1,8KPřed rokem
GLM Exponential Family, Prove that the mean is the first derivative of b, E(Y) = mu = b'(theta)
GLM Exponential Family, Example with Normal distribution finding functions a, b, and c
zhlédnutí 5KPřed rokem
GLM Exponential Family, Example with Normal distribution finding functions a, b, and c
Simple linear regression, Variance of a residual, Yi and Yhat independence. Plus or Minus sign?
zhlédnutí 2,1KPřed rokem
Simple linear regression, Variance of a residual, Yi and Yhat independence. Plus or Minus sign?
Deriving Confidence Interval vs Deriving Prediction Interval
zhlédnutí 990Před rokem
For more details regarding deriving confidence interval for a mean response, please see this video, czcams.com/video/D0hEr2zngw0/video.html For more details regarding deriving prediction interval, please see this video, czcams.com/video/tJ8cv-fFPHs/video.html
Proof for the Probability of a Union of Three Events
zhlédnutí 2,8KPřed rokem
This is a long proof, though it is worth it to see how we arrived at the solution for the probability of a union of three events. The same ideas here are used as those used to find the probability of a union of two events: czcams.com/video/m67AKAWM3gA/video.html
Using Venn Diagrams, show that if A is a subset in B, then B complement is a subset of A complement
zhlédnutí 2,8KPřed rokem
Using Venn Diagrams, show that if A is a subset in B, then B complement is a subset of A complement
Why is the Variance of the Sample Mean equal to Sigma^2/n ? How to find the Variance of X-bar
zhlédnutí 8KPřed rokem
Why is the Variance of the Sample Mean equal to Sigma^2/n ? How to find the Variance of X-bar
Where did 1/(1-exp(x*beta)) come from in Logistic Regression?
zhlédnutí 961Před rokem
Where did 1/(1-exp(x*beta)) come from in Logistic Regression?
How to Derive the Maximum Likelihood Estimators for Logistic Regression
zhlédnutí 6KPřed rokem
How to Derive the Maximum Likelihood Estimators for Logistic Regression
Prove Sum yi(xi - xbar) = Sum (yi - ybar)(xi - xbar)
zhlédnutí 7KPřed rokem
Prove Sum yi(xi - xbar) = Sum (yi - ybar)(xi - xbar)
How to Derive the Score Vector for the Maximum Likelihood Estimators of a Logistic Regression
zhlédnutí 1,8KPřed rokem
How to Derive the Score Vector for the Maximum Likelihood Estimators of a Logistic Regression
Set Theory Example: A Complement Intersection B = A' ∩ B = A^c ∩ B
zhlédnutí 1,3KPřed rokem
Set Theory Example: A Complement Intersection B = A' ∩ B = A^c ∩ B
Set Theory Example: Intersection of three Events: A Intersection B Intersection C = A ∩ B ∩ C
zhlédnutí 910Před rokem
Set Theory Example: Intersection of three Events: A Intersection B Intersection C = A ∩ B ∩ C
Set Theory Example: Union of three Events: A Union B Union C = A U B U C
zhlédnutí 1,3KPřed rokem
Set Theory Example: Union of three Events: A Union B Union C = A U B U C
Set Theory Examples: A Intersection (B Union C) = A(BUC) = A∩(BUC)
zhlédnutí 1,3KPřed rokem
Set Theory Examples: A Intersection (B Union C) = A(BUC) = A∩(BUC)
Set Notation Examples: Sample Spaces is the real number line: Find Complement, Union, Intersection
zhlédnutí 709Před rokem
Set Notation Examples: Sample Spaces is the real number line: Find Complement, Union, Intersection
Introduction to Set Notation: Complement, Union, Intersection, Sample Space, Event, Element
zhlédnutí 472Před rokem
Introduction to Set Notation: Complement, Union, Intersection, Sample Space, Event, Element
Interpreting R squared building up from interpreting variance (and standard deviation)
zhlédnutí 254Před 2 lety
Interpreting R squared building up from interpreting variance (and standard deviation)
Calculating Normal Probabilities in Excel for a single observation and for the sample mean
zhlédnutí 807Před 2 lety
Calculating Normal Probabilities in Excel for a single observation and for the sample mean
Thanks so much for your kindness.❤❤❤
this is actually so so helpful thank uuuu sooo much
Thank you! Thank you! I've been searching for more than 20 minutes on Google to get the exact value ( at 5:25) and not the rounded 1.96! Wow! Thanks again!
Thanks!!
Does Descriptive Statistics work with multiple columns and rows?
U made the concept easier. Thx a lot!!
🎉
Hello! Could you explain why the var of Y is the same as the variance of e? You said it is because X is not random, but what does it imply? Thanks!
Super helpful, thank you.
You are perfecttt. Thank you very much. Wishing you a long and healthy life x
expectation and average are different
Clear and straight forward. Thank you. However, what if the total population is limited, how do we factor that into the sample size calculation?
4:26 why is it the same??
2:36 wait why is e multiplied by e transpose the same as e^2?
5:24 wow…I didn’t even know beta could occur in exponents….this is all very new, could you recommend some stats textbooks that are effective in covering higher order concepts
THEN HOW WILL WE CALCULATE E(XY) ??, DO WE USE DEPENDENT LIMTIS THERE AS WELL, ??
Hey, thank you for this video.
Thank you! So helpful 🙏
Indeed a lifesaver !!!
Thank you!!
Great video!
amazing!!!!!
thank you very much
thanks so much!!!! I FINALLY UNDERSTANDT
thanks
Can you post a video showing why y0 and y0 hat are independent? BTW, I love your statistics videos. They are very helpful!
Thank you, at last these error iid's are explained properly.
Amazing content on this channel! Very crisp explanations. A playlist on machine learning topics such as Logistic Regression, Gradient Descent Optimization, PCA, SVM, Decision Trees, Multi-layer Perceptrons, feed-forward neural networks, etc would be amazing!
This taught me in the first two minutes what my graduate-level professor was unable to teach over two days. Thanks!
Thank you! Great presentation!
I am very glad this came up on Google. Thank you for making this and for giving it a title that came up in my haphazard googling.
Mam, please upload lectures of BAYESIAN STATISTICS
Wow❤🎉
Im doing a variation of this problem where Pk is the probability that no two people share the same birthday, and I am supposed to show that it is maximized when all birthdays are equally likely. Which means that for any birthday i, Pi = 1/n where n is the number of possible birthdays. Anyone know how I should start going about this?
Because generally the problem has you assume that each birthday is equally likely in order to find the probability, but here I am simply supposed to prove that the probability is maximized under that assumption
Thanks you are very helpful!
YA SEN NE BÜYÜK Bİ ADAMSIN BE KARDŞEİM
Ya sen dünyanın en iyi matematikçisin aq ya adamsın adam
you just saved my ass on this problem set thank you 🥲
Thanks for the amazing video! Very intuitive and well-explained. How would derive the same for multiple non-linear regression. For eg: Exponential curve-fit: y = e^(ax), where a is the parameter
how did you express Var(x bar) in terms of expected value of (x bar square) and (expected value of x bar) square . Where can I read more theory about it.
cool examples! Now I understand the concept of pdf
very helpful, thank you so much!
Great explanation! Thank you.
Thank you soo much, just what I needed.
Wow thank you so much for your explanation Im really so glad that you use different colors for deriving something out of the main problem ❤ It helps us to understand better 💓Again Thank you so much😄
How do you display the selection summary output that actually shows you the CP term? I keep getting a table that shows the AIC, SBIC, and SBC. Do you know why this discrepancy in our output exists? I am using the same package and function as you are.
KK Wagh Institute Of Engineering Education And Research, Nashik
Thank you
very helpful! Thank you!
yooooo that's really good thank you!