Merge pull request #2 from hemant087/hemant087-patch-2

basic of statistic for data science

blogs/Statistics For Data Science.md

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+<h1>Why Should I Learn Statistics?</h1>
+
+Variation is an inevitability facet of life!  Every process has variation.High variation means low quality. 
+**Decision makers make better decisions when they use all available information in an effective and
+meaningful way. The primary role of statistics is to provide decision makers with methods for obtaining and
+analyzing information to help make these decisions. Statistics is used to answer long-range planning
+questions, such as when and where to locate facilities to handle future sales.**
+
+
+**Just like weather, if we cannot control something, We should learn how to measure and analyze in
+order to predict it, effectively.**
+
+``The aim of this blog is to provide you with hands-on
+experience to promote the use of statistical thinking and techniques to apply them to make educated decisions
+whenever you encounter variation in business data.``
+
+<h1>What Is Business Statistics?</h1>
+
+The main objective of Business Statistics is to make inferences (predictions, decisions) about certain
+characteristics of a population based on information contained in a random sample.
+
+Business Statistics is the science of "good" decision making in the face of uncertainty and is used in many
+disciplines, such as financial analysis, econometrics, auditing, production and operations, and marketing
+research. It provides knowledge and skills to interpret and use statistical techniques in a variety of business
+applications. 
+- Carefully planned statistical studies remove hindrances to high quality and productivity at every stage of
+production, saving time and money. It is well recognized that quality must be engineered into products as
+early as possible in the design process. One must know how to use carefully planned, cost-effective
+experiments to improve, optimize and make robust products/services and processes data.
+
+Statistical topics are about decision-making with respect to the characteristics of a group of persons or objects
+on the basis of numerical information obtained from a randomly selected sample of the group.
+
+<h1>Types of Statistical Analyses:</h1>
+
+- ``Descriptive Statistics`` is concerned with summary calculations, graphs, charts, and tables.
+
+- ``Inferential Statistics`` is a method used to generalize from a sample to a population. For example, the
+average income of all families in the US (the population) can be estimated from figures obtained from a
+few hundred families (the sample).
+
+
+<h2>Statistical Population:</h2> A statistical population is the collection of all possible observations with a specified
+characteristic of interest.
+**An example :**  all the students in the College is population then ECE, Computer Science ,1st year,2nd Year and etc, are the sample. Note
+that a sample is a subset of the population.
+<h2>Variable:</h2>A variable is an item of interest that can take on many different values.
+
+<h1>Types of Variables or Data:</h1>
+
+- ``Qualitative Variables`` are non-numerical variables that cannot be measured. Examples include gender,
+religious affiliation, place of birth.
+
+- ``Quantitative Variables`` are numerical variables that can be measured. Examples include balance in
+your checking account, number of children in your larger family. Note that quantitative variables are
+either discrete (which can assume only certain values, and there are usually "gaps" between the values,
+such as the number of bedrooms in your house) or continuous (which can assume any value within a
+specific range, such as the air pressure in a tire).
+
+