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...
Being confident make one's self more reassured. Briefly, explanations below are for two sided confidence levels/intervals in order to simplify the idea. Saying " two sided " gives initial impression that there is something like two limits, yeah they are: upper and lower limits where the confidence interval lies in between. Example: Let's look at the population of a specific mobile phone model. Suppose we are now interested in the ' weight ' property. We found that weight property follows a normal distribution with mean value of 120 grams and a standard deviation of 1.4 grams. Weight ~ Normal (Mu, Sigma) = Normal (120, 1.4) This understanding means that majority of mobiles tested will weigh very closely to 120 grams. Yes, there should be fluctuations above and below the mean value but surely that still relatively close to mean value. Suppose a question: do you expect weights like: 121, 119.5, 122.1, 118.9? Answer: Yes , I surely expect such ...
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...
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