Youtube Full Course: Test of Hypothesis

-

Hello,

 Full video course is now available on Youtube

 Visit my dedicated channel here >> Test of Hypothesis


All the best with your learning journey :)

Comments

Post a Comment

Popular posts from this blog

Normal Probability Distribution

-
Image
Also called Gaussian distribution. OK, many things in this world tends, and should do, to be normally distributed. Any distribution is a representation of how the information or data is distributed. We mainly look for its central tendency ( mean ) and variability ( variance ). That's why the normal distribution is usually written as: N ~ (Mu, Sigma^2) For example: the weight of most adult (who still youth) people will normally be centered around some values. Yes, you right there is a diversity: some are slim and some are obese. We may expect the average weight for people (example: ages 20 to 30) to be between 70 to 74 kg. OK, let's consider it as 72 (this is the mean value). Let x represents the weight of a random person. Thus, Expected Value [x] = mean [x] = Mu = 72 kg If we have a sample, we can compute the variance (sigma^2) to indicate variability. But we may here think as following: Variance = Sigma^2 = Expected Value [(x-Mu)^2] Sta...
Read more

Statistic Distributions CDF - Android App Privacy Policy

-
  Privacy Policy This privacy policy applies to the Statistic Distributions CDF app (hereby referred to as "Application") for mobile devices that was created by Misbah Aiad (hereby referred to as "Service Provider") as a Free service. This service is intended for use "AS IS". Information Collection and Use The Application does NOT collect any type of information, neither online (account based) nor offline (user device). The Application does not connect to the internet at all, nor stores anything entered by the user on their device. Third Party Access Only aggregated, anonymized data is periodically transmitted to external services to aid the Service Provider in improving the Application and their service. The Service Provider may share your information with third parties in the ways that are described in this privacy statement. Please note that the Application utilizes third-party services that have their own Privacy Policy about handling data. Below are t...
Read more

Understanding the distribution of sample mean (x_bar)

-
Image
Cool, say now we have a huge population with characteristics ( Mu, Sigma^2 ). When doing a study by sampling, we take a random sample ( size n items ) and then perform the study on the sample and conclude results back for the population. From Central Limit Theorem, we know that the sample mean will always follow a normal distribution  apart from what the population distribution is, such that: x_bar ~ N (Mu, Sigma^2/n) or say: Expected (x_bar) = Mu Variance (x_bar) = Sigma^2/n Well, let's see a simple illustrating example: Suppose we have a population with mean Mu=100 . Now, we have taken a sample, and computed the sample mean, x_bar. We mostly will have x_bar near 100 but not exactly 100. OK, let take another 9 separate samples... suppose these results: First sample --> x_bar = 99.8 Second sample --> x_bar = 100.1 .. .. .. 10th sample --> x_bar = 100.3 What we see that the sample mean is usually...
Read more