The p-value is computed by examining the summary of linear relation formed between the target and features or between the dependent and independent variables. Statistics and probability are the base of data science. One should know the fundamentals and concepts so as to solve the data science problems.
It gives you the information about the data, how it is distributed, information about the independent and dependent variable, etc. In this blog, I have tried to give you the basic idea about statistics and probability. Yes, there is much more to be explored when we talk about Statistics and probability in Data Science.
We have discussed the important, central limit theorem, statistical analysis, measure of central tendency, basic terminologies in statistics, and skewness.
Also, I have given you the idea of a Hypothesis done in probability and how we can accept it and reject it on the basis of a p-value. Be a part of our Instagram community. Very Good Information about Data Science and importance. Present I am in Learning stage so i got lot of confidence. Thanks for the Article. Introduction There is often a question raised; Why do we need to learn statistics and probability?
What is Central Limit Theorem? Different terms used in Statistics? Let us understand the same - Population - The place or a source from where the data has to be fetched or collected. What is Statistical Analysis? There is mainly two types of Statistical Analysis- Quantitative Analysis: The type of analysis is defined as the science of fetching and interpreting the data with graphs and numbers to search for underlying hidden trends.
Measures of Central Tendency It is defined as the single value that aims to explore a set of data by recognizing the central position within the set of data. Mode - It is the most occurring value in the dataset. By this, we refer to what are the known facts when we approach a problem. Inherent in both probability and statistics is a population , consisting of every individual we are interested in studying, and a sample, consisting of the individuals that are selected from the population.
We can see the difference between probability and statistics by thinking about a drawer of socks. Perhaps we have a drawer with socks. Depending upon our knowledge of the socks, we could have either a statistics problem or a probability problem.
If we know that there are 30 red socks, 20 blue socks, and 50 black socks, then we can use probability to answer questions about the makeup of a random sample of these socks. Questions of this type would be:. If instead, we have no knowledge about the types of socks in the drawer, then we enter into the realm of statistics.
Statistics help us to infer properties about the population on the basis of a random sample. Questions that are statistical in nature would be:. Of course, probability and statistics do have much in common. This is because statistics are built upon the foundation of probability. Although we typically do not have complete information about a population, we can use theorems and results from probability to arrive at statistical results.
We can express this rule as. Third, if two events A and B are mutually exclusive, then the probability of the union of these events is the sum of the probabilities of each event individually.
That is,. If the events are not mutually exclusive that is, they share some elements in common , then the probability of their union is sum of the individual probabilities minus the probability of all elements in common this is just the intersection of A and B.
This formula is actually a more general expression of the preceding formula. Fourth and finally, the probability of an event E is equal to unity minus the probability of the event's complement, E C. This statement simply combines the facts that E and E C are mutually exclusive but span the entire sample space S and that the probability of S is unity.
Thus, using the rules above,. Although these rules and concepts may seem somewhat esoteric, they are indeed helpful in discussing probability as it relates to statistics. The following practice problems will help you apply these ideas to practical problems and situations.
Practice Problem : For a random experiment involving the roll of a sided die, what is the probability that the outcome will be between 1 and 10 inclusive? Because the event for which the outcome of a roll is between 1 and 10 inclusive spans the sample space, the probability must simply be unity.
Practice Problem : Given a standard deck of 52 playing cards, what is the probability that a card pulled from the deck is either an ace or a spade? Solution : This problem forces you to apply several different aspects of statistics.
The problem defines two events, which we will call A and P. Event A is the selection of an ace, and event P is the selection of a spade. Although you may realize already that A and P are not mutually exclusive events, let's write out the two sets to illustrate.
The notation used below is the value of the card A for ace, for example followed by the suit of the card S for spades, for example. Note that one element outcome is shared between the two sets. Let's now write the probability formula for the union of A and P , which is the probability that the card selected is either a spade or an ace. Now, we must calculate these probabilities. Let's use these numbers to calculate the probability that a random drawing of a card yields either an ace or a spade:.
Of course, a simpler approach would simply be to find the relative frequency of aces and spades there are 16 such cards in a deck --again, this is just 0. The solution above, however, illustrates the use of the concepts presented in this article.
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