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jbstatistics
Registrace 27. 11. 2011
Jeremy Balka's statistics channel, containing some introductory statistics videos.
(I am an associate professor in the Department of Mathematics and Statistics at the University of Guelph. I have a PhD in statistics, and have taught introductory statistics courses on many occasions.)
I try to be concise, and use real data in the vast majority of cases. Some of these videos move at a quick pace, especially my earlier ones, and they are not designed to be lecture replacements. They may serve to clarify some concepts after lecture. If you want to get ahead, watch the relevant videos before lecture to get an introduction to the topic.
The videos are pitched at the level of an applied introductory statistics course to non-statistics majors.
I'll be updating this channel with new videos until I've filled out a full introductory statistics course (and I may possibly expand after that).
A complete list of videos, organized by topic, can be found at www.jbstatistics.com.
(I am an associate professor in the Department of Mathematics and Statistics at the University of Guelph. I have a PhD in statistics, and have taught introductory statistics courses on many occasions.)
I try to be concise, and use real data in the vast majority of cases. Some of these videos move at a quick pace, especially my earlier ones, and they are not designed to be lecture replacements. They may serve to clarify some concepts after lecture. If you want to get ahead, watch the relevant videos before lecture to get an introduction to the topic.
The videos are pitched at the level of an applied introductory statistics course to non-statistics majors.
I'll be updating this channel with new videos until I've filled out a full introductory statistics course (and I may possibly expand after that).
A complete list of videos, organized by topic, can be found at www.jbstatistics.com.
You sir, are no normal distribution
Not even close. Lots of this sort of stuff in textbooks as well. WT...H?
(And just in case anybody thinks that I think I'm in that far right tail --- I'm definitely the midwit here.)
For the normal distribution, the ratio of area between 0 and 1 SD from the mean to that of the area between 1 and 2 SD from the mean is about 2.51. With rounding, the figure states it as 34/14, which is about 2.43. The ratio of the actual areas seen in this plot is about 3.75 (50% larger than it should be). Not even close.
(And just in case anybody thinks that I think I'm in that far right tail --- I'm definitely the midwit here.)
For the normal distribution, the ratio of area between 0 and 1 SD from the mean to that of the area between 1 and 2 SD from the mean is about 2.51. With rounding, the figure states it as 34/14, which is about 2.43. The ratio of the actual areas seen in this plot is about 3.75 (50% larger than it should be). Not even close.
zhlédnutí: 3 444
Video
1000 normal QQ plots in 30 minutes (n = 25, sampling from N(10,5^2))
zhlédnutí 1,2KPřed 5 měsíci
Title, essentially. Properly interpreting normal QQ plots takes some experience, and part of that experience is developing a feel for what natural variability looks like on a normal QQ plot (when sampling from a normal distribution). Each of these 1000 plots is based on a sample of size 25 from a normal distribution with a mean of 10 and standard deviation of 5. Created with R’s qqnorm and qqli...
What I envision a virtual work meeting at D2L (Brightspace) to be like (jk, obv)
zhlédnutí 8KPřed 2 lety
Todd meets with his supervisor at D2L (Brightspace) to discuss priorities going forward. (I'll get back to stats videos soon. This one's mainly for fun, but inspired by a decade of frustration. In case it's not obvious, I play both parts.) D2L (Brightspace) quizzes do not allow an instructor to change the answer to a numeric response (arithmetic) question and regrade quiz attempts. There's a re...
Basic Probability: The Multiplication Rule
zhlédnutí 34KPřed 3 lety
An introduction to the multiplication rule. (I assume the viewer has an understanding of conditional probability and independence, but I do a very brief review of those concepts.) I introduce the multiplication rule for two events, work through a simple example, then discuss the multiplication rule for 3 events and how it generalizes to any number of events. I end with an example using the mult...
Linear Transformations (in a Descriptive Statistics Setting)
zhlédnutí 12KPřed 3 lety
I discuss linear transformations, in the context of descriptive statistics. I discuss what a linear transformation is, give an example, discuss the effect of the linear transformation on various summary statistics, and work through a numerical example. The temperature data is from here: climate.weather.gc.ca/climate_data/daily_data_e.html?StationID=51459&timeframe=2&StartYear=1840&EndYear=2021&...
An Introduction to Boxplots
zhlédnutí 8KPřed 3 lety
An introductory to boxplots. (I do not carry out any calculations, this video is about interpreting boxplots.) I discuss the basics, an applied example, give a few illustrations of histograms and boxplots under symmetry and skewness, then briefly discuss how large samples often lead to a large number of outliers in a boxplot. Source of the jumping fish example: Brunt et al. (2016). Amphibious f...
On average, what proportion of sample means would a randomly selected 95% CI for mu capture?
zhlédnutí 11KPřed 5 lety
This one's inspired by a common confidence interval misinterpretation. (This is a bit of a different video for me, and if you're just looking for help with specific topics in a statistics course, you may not find it helpful. But there's some good stuff in here.) Here I address what might seem at first like bit of a strange or uninformative question: In repeated sampling from a normally distribu...
Deriving the mean and variance of the least squares slope estimator in simple linear regression
zhlédnutí 98KPřed 5 lety
I derive the mean and variance of the sampling distribution of the slope estimator (beta_1 hat) in simple linear regression (in the fixed X case). I discuss the typical model assumptions, and discuss where we use them as I carry out the derivations. The derivations are carried out using summation notation (no matrices). At the end, I briefly discuss the normality assumption, and how that leads ...
The Law of Total Probability
zhlédnutí 160KPřed 5 lety
I discuss the Law of Total Probability. I begin with some motivating plots, then move on to a statement of the law, then work through two examples.
P(A) = P(A and B) + P(A and Bc)
zhlédnutí 22KPřed 5 lety
A quick video to illustrate that P(A) = P(A and B) P(A and Bc), and work through a simple conditional probability example that makes use of this identity. I know from experience that some of my students have trouble seeing this (especially when it comes up in a formulaic approach to a problem), and I wanted to have a video that I could point them to. My Law of Total Probability video is here: c...
Deriving the least squares estimators of the slope and intercept (simple linear regression)
zhlédnutí 226KPřed 5 lety
I derive the least squares estimators of the slope and intercept in simple linear regression (Using summation notation, and no matrices.) I assume that the viewer has already been introduced to the linear regression model, but I do provide a brief review in the first few minutes. I assume that you have a basic knowledge of differential calculus, including the power rule and the chain rule. If y...
Proof that if events A and B are independent, so are Ac and B (and A and Bc)
zhlédnutí 63KPřed 5 lety
Here I prove that if events A and B are independent, so are A complement and B. (And A and B complement, of course, since which event we call A and which we call B is arbitrary.) Looking for a proof that if A and B are independent, so are their complements? That's here: czcams.com/video/bnDpZNlVZ3k/video.html
Proof that if two events are independent, so are their complements.
zhlédnutí 48KPřed 5 lety
Just getting warmed up. Here I prove that if events A and B are independent, so are Ac and Bc. I make use of De Morgan's Laws, without offering a formal proof of that part (but I do provide a brief Venn diagram justification of the needed bit). More probability and statistics videos will follow.
Independent Events (Basics of Probability: Independence of Two Events)
zhlédnutí 248KPřed 6 lety
An introduction to the concept of independent events, pitched at a level appropriate for the probability section of a typical introductory statistics course. I give the definition of independence, work through some simple examples, and attempt to illustrate the meaning of independence in various ways. (Note: I use the phrase "not independent" rather than "dependent" almost exclusively. There is...
Conditional Probability Example Problems
zhlédnutí 231KPřed 6 lety
Conditional probability example problems, pitched at a level appropriate for a typical introductory statistics course. I assume that viewers have already been introduced to the concepts of conditional probability and independence, but I do review the concepts along the way. I work through some problems with the conditional probability formula explicitly, and some using the reduced sample space ...
Basics of Probability: Unions, Intersections, and Complements
zhlédnutí 256KPřed 6 lety
Basics of Probability: Unions, Intersections, and Complements
Don't watch this! (A t test example where nearly everything I say is wrong)
zhlédnutí 7KPřed 6 lety
Don't watch this! (A t test example where nearly everything I say is wrong)
De Morgan's Laws (in a probability context)
zhlédnutí 113KPřed 6 lety
De Morgan's Laws (in a probability context)
An Introduction to Conditional Probability
zhlédnutí 298KPřed 6 lety
An Introduction to Conditional Probability
Are mutually exclusive events independent?
zhlédnutí 114KPřed 6 lety
Are mutually exclusive events independent?
What Does Independence Look Like on a Venn Diagram?
zhlédnutí 87KPřed 9 lety
What Does Independence Look Like on a Venn Diagram?
The Expected Value and Variance of Discrete Random Variables
zhlédnutí 354KPřed 10 lety
The Expected Value and Variance of Discrete Random Variables
An Introduction to Discrete Random Variables and Discrete Probability Distributions
zhlédnutí 352KPřed 10 lety
An Introduction to Discrete Random Variables and Discrete Probability Distributions
Inference for the Ratio of Variances: How Robust are These Procedures?
zhlédnutí 7KPřed 10 lety
Inference for the Ratio of Variances: How Robust are These Procedures?
Inference for a Variance: How Robust are These Procedures?
zhlédnutí 7KPřed 10 lety
Inference for a Variance: How Robust are These Procedures?
The Sampling Distribution of the Ratio of Sample Variances
zhlédnutí 11KPřed 10 lety
The Sampling Distribution of the Ratio of Sample Variances
Inference for Two Variances: An Example of a Confidence Interval and a Hypothesis Test
zhlédnutí 13KPřed 10 lety
Inference for Two Variances: An Example of a Confidence Interval and a Hypothesis Test
The Sampling Distribution of the Sample Variance
zhlédnutí 62KPřed 10 lety
The Sampling Distribution of the Sample Variance
Deriving a Confidence Interval for the Ratio of Two Variances
zhlédnutí 27KPřed 10 lety
Deriving a Confidence Interval for the Ratio of Two Variances
An Introduction to Inference for the Ratio of Two Variances
zhlédnutí 29KPřed 10 lety
An Introduction to Inference for the Ratio of Two Variances
robotic zane boy thanks
Thank you for this great video🤍.
This is sooo helpful. Thank you so much!
Please make vdo on mean and variance of this disttibution
Thankyou so much for making this video. I just bing-watching some of your videos about probability and all of them are great <333
Thanks! I'm glad to be of help!
This was so helpful! Thank you so much!
You're welcome!
thank you so much
sabar bai kuta tp bonkta hay ap chtay rhay
Thank you
4:11
You made it effortlessly easy to understand 😀 Thank you!!!
Excellent.
Excellent! Thanks for this lecture.
Excellent video. I got question at 5:20, shouldn't we use t-table instead of z-table?
No. We are carrying out a probability calculation on the mean of a sample from a normally distributed population, where sigma and mu happen to be known. In this situation, X bar ~ N(mu, sigma^2/n) and (X bar - mu)/(sigma/sqrt(n))~N(0,1). Probabilities are found from the normal distribution.
best video cleared all the concepts thank you so much
Please do a video on Gamma distribution!
12 years later and you can't imagine how much of a life saver you are. thank you soo much
Vrey nice sir thank you very much understood very clearly
I'm glad to be of help!
cramming rn and this is ultra helpful! thanks dude :)
doing gods work thank you still useful 11 years later
Hi! I loved this video and it was very concise! I was just wondering for the last example at around 9:05 , why didnt we divide the significance level by 2? Isn't alpha basically the same thing as the confidence level, so if we do a two tailed test, wouldn't alpha be split amongst the two tails so it would be 0.025? Im just a bit confused, thanks!
These 7 minutes saved my life thanks
Isn't the mean r(1-p)/p not r/p.
Great, I finally get it, thanks!
you're awesome dude
La probabilité peut-elle être négative ?
Comment est-ce possible?
Y a-t-il des événements tels que la probabilité est négative ?
@@AryssaRiyasat Pas selon les définitions standards de la probabilité.
Thank you for this video....easy to understand 🙏
I'm glad to be of help!
perfect
This video is absolutely precious. Couldn't be clearer.
and beta0???
I wish I could touch the like button under this video 1000 times!
If you were doing this for a sample size calculation, would the final sample size be 16 or 32? In other words, if you wanted to find the sample size needed for achieving 7.9% power in the example, would you enroll 16 or 32 participants?
bless wow thank yo
Shouldn't be the probability for H0 and Ha add up to 1? Then, Your H0 needs to be µ >= 50, or Ha µ != 50!
I have an exam coming on this 5th july, thank you boss
really interesting video😇
This video changed my life...thanks brother
Excelent video! Great explanation.
good
So is this p hacking? And a two tailed test would have been more appropriate?
While I personally lean towards two-tailed alternatives in the vast majority of situations, I think the use of a one-tailed procedure is reasonable in this situation. Before collecting the data, there was a strong belief (based on previous studies and information) that puerarin would have a tendency to reduce alcohol consumption. So it wasn't just a "ooooh, I think my new drug is better" sort of argument. (Or even worse, using the data to inform the choice of alternative.) Abusing the use of a one-tailed test can be a form of p-hacking, sure, but I don't think that happened in this study.
What an absolute God
Thank you so much
Thank you very much
Really cleared up confusion.
Thank you very much.Now i understood the central limit theorm.It is the basis.
The best statistics teacher
does this means that formula foe the variance is same for discrete and continuous variable?
Yes, in the sense that for any random variable X, Var(X) = E[(X - mu)^2].
Excellent!