İSTATİSTİK I

A population contains all members of a specified group Example: The population may be "ALL people living in the Turkey"

 A sample data set contains a part, or a subset, of a population. The size of a sample is always less than the size of the population from which it is taken. Example: The sample may be "SOME people living in the Turkey"

Nominal scale is a scale of measurement that is used for identification purposes. A nominal scale describes a variable with categories that do not have a natural order or ranking.

Ordinal Scale involves the ranking or ordering of the attributes depending on the variable being scaled.

Interval scale of data measurement is a scale in which the levels are ordered and each numerically equal distances on the scale have equal interval difference, but no absolute zero.

Ratio scale has all the properties of an interval variable, and also has a clear definition of zero. When the variable equals zero, there is none of that variable.

The interval scale of data measurement is a scale in which the levels are ordered and each numerically equal distances on the scale have equal interval difference.

Observational study – data are observed and collected on each subject and no manipulation of the subjects’ environment occurs.

Experimental study– Manipulate the subjects’ environment, then measure the response variable.

Dealing with unstructured and structured data, Data Science is a field that comprises everything that related to data cleansing, preparation, and analysis. Data science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data.

Statisticians are the most sought-after professionals these days.We are living in the information age, and the Statistician is the king of handling, analysing, interpreting and facilitating decisions from data. The principal reason why Statistics is a key necessity to humankind today is the massive amounts of available information.

The main criteria for selecting a sample will be that the sample is representative of the population and that there’s no or very little subjectivity in the choice of the sampling units.

The easiest form of data is called categorical, or qualitative. Categorical variables and data can be either nominal or ordinal. Exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on. Other examples of nominal categorical variables are gender, region of residence, field of study, type of transport, type of housing, etc.

Categorical variables and data can be either nominal or ordinal. Examples of nominal categorical variables are gender, region of residence, field of study, type of transport, type of housing, etc. There i­s no orderi­ng i­n the categori­es of these variables. . Exam grade is an ordinal categorical variable, since its categories are ordered: A is better than a B, B is better than a C, and so on. Other examples of ordinal categorical variables are ­income group (I­f i­ncomes have been categorized), an attitude question in a survey where possible responses are strongly agree/agree/disagree/strongly disagree (these categories have an order).

The other mai­n type of data i­s called continuous, or quantitative, for example data on variables “blood pressure”, “age” and “­income”. These are observations of variables on continuous scales, usually rounded i­n some convenient way. For example, although age ­is a continuous time variable, and we are getting older all the time by seconds, minutes and hours, someone’s age i­s almost always rounded to the number of years completed. There i­s a subtle difference between interval-scale and ratio-scale continuous data, which­ is worth mentioning here. Age i­s an ­interval-scale variable: to compare two children of ages 10 and 12, we would compute the interval d­ifference, ­.e. 2 years.

As a general rule, most data on monetary values and those coming from physical measurements (e.g., l­ira, gold price, centimeters, and kilograms) are ratio-scale variables.

What are the “big data” sets today and where do they come from? These are mostly found in the following areas:

As a general rule, most data on monetary values and those coming from physical measurements (e.g., l­ira, gold price, centimeters, and kilograms) are ratio-scale variables.

The first role of Statistics is to reduce this mass of complex data to a simpler form in order to facilitate understanding and learning from the data. This is often done by making graphical representations of the data. Statistics can make a lot of numerical data easily understandable. The world today abounds i­n textual data. Words, SMSs, tweets, social media posts, verbal responses in questionnaires, these can all be treated as data. Climate data, measurements from tens of thousands of weather stations across the world, is an example of big data.

The other main type of data is called continuous, or quantitative, for example data on variables “blood pressure”, “age” and “income”. These are observations of variables on continuous scales, usually rounded in some convenient way. For example, although age is a continuous time variable, and we are getting older all the time by seconds, minutes and hours, someone’s age is almost always rounded to the number of years completed. There is a subtle difference between interval-scale and ratio-scale continuous data, which is worth mentioning here. Other measures of time are ­interval-scale variables, for example hours of sleep (on Sundays I sleep an hour longer - I would not say I sleep 14% longer).