The probability of event A given that event B has occurred is denoted as p(A|B). The condition here is to pick from box A which clearly changes the probability of the event (picking a blue ball). These were some of the statistics concepts for data science that you need to work on. Time interval bias: It is caused intentionally by specifying a certain time range to favor a particular outcome. What if I ask you to pick a ball from box A? The probability of picking a blue ball clearly decreases. Confirmation bias: It occurs when the person performing the statistical analysis has some predefined assumption. The Data Science Program has the advantage of combining including many traditionally. The probability of getting a blue ball is 6 / 10 = 0,6. Working with data requires the mastery of a variety of skills and concepts. Suppose that we have 6 blue balls and 4 yellows placed in two boxes as seen below. The number of desired outcomes is 2 (1 and 2) the number of total outcomes is 6.Ĭonditional probability is the likelihood of an event A to occur given that another event that has a relation with event A has already occurred. For example, when you roll a die, the probability of getting a number less than three is 2 / 6. The probability of event A is denoted as p(A) and calculated as the number of the desired outcome divided by the number of all outcomes. It is derived from calculations that include: Mean: It is the central value which is commonly known as arithmetic average. Probability simply means the likelihood of an event to occur and always takes a value between 0 and 1 (0 and 1 inclusive). It is used to describe the basic features of data that provide a summary of the given data set which can either represent the entire population or a sample of the population. The scope of the lab is to introduce you to basic Data Science concepts and techniques, by going through the steps of a Data Science project example, from the formation of the Business Objective to Model Building and evaluation. And, if we know the mean and standard deviation of a normal distribution, we can compute pretty much everything about it. This lab is an introduction to Data Science concepts, for people who are familiar with using the basic PI tools. Why is it so important to have a normal distribution? Normal distribution is described in terms of mean and standard deviation which can easily be calculated. CLT states that as we take more samples from the population, sampling distribution will get close to a normal distribution. So, we take samples of 20-year-old people across the country and calculate the average height of the people in samples. It is almost impossible and, of course not practical, to collect this data. According to the CLT, as we take more samples from a distribution, the sample averages will tend towards a normal distribution regardless of the population distribution.Ĭonsider a case that we need to learn the distribution of the heights of all 20-year-old people in a country. In many fields including natural and social sciences, when the distribution of a random variable is unknown, normal distribution is used.Ĭentral limit theorem (CLT) justifies why normal distribution can be used in such cases.
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