İSTATİSTİK I - Deneme Sınavı - 10

The paragraph is an example of experimental design in sports science. So, drinking coffee and water on pre-trainings appears to be beneficial to athletes. More experiments with more athletes should be undertaken in other conditions and controls.

Statistics mainly work on numerical data. Qualitative inquiry is not an interest of statistics.

Data come in the form of numbers as well as text, which is something we are discovering more and more. We live in an information world and information is the virtual gold of our society. All observations can be reduced to some numerical quantity, and there are even fields of digital philosophy and digital sociology. It may seem that everything may be recorded and stored somewhere. But in reality - unless we somehow centralize and link all the databases in the world, and have free access to them - we can get access to only a small part of whatever data we are interested in.

A reasonable survey would carefully select people as a sample and gathering data from Denizli would not represent Turkey. Statistics working on samples cannot give absolutely correct information. Sample being carefully selected, it can give a statistical estimate of proportions.

Data such as gender, hometown and branch do not represent a computational score. They can only be used to categorize the sample.

Data labeled as successful, average and unsuccessful does not represent a computational score but does represent an order. So, it is an ordinal data.

Temperature in Celsius does not have an absolute zero. Talking about 20 degrees and 40 degrees, you cannot say its twice warmer but you can say it is 20 degrees more/less. So, the measurement is interval.

Because the difference in the counts of words could be stated in percentages, it is a ratio type of measurement.

In order to prove whether tea has an effect on heart diseases, an experiment needs to be conducted where conditions are controlled.

When it comes to Data Science, however, the term does have some different meaning. Data Science is a field that includes Statistics as well as areas such as Computer Science, Database Management and Data Visualization, for example, and has come into being mainly as a result of the spectacular growth in the amount of available data in this new information world that we live in. The need has been recognized for someone who not only has statistical skills, but also advanced programming skills and knowledge about handling huge data sets, the so-called “Big Data” of today.

What are the “big data” sets today and where do they come from? These are mostly found in the following areas:
• Commerce, especially online electronic commerce
• Finance, for example share prices on stock markets, all managed electronically
• Insurance, all the premiums, incidents, actuarial transactions in an insurance company
• Biomedicine, especially in genetics, where information is literally exploding as gene-sequencing reveals and codes the total genetic profile of a person
• Transport, for example in the airline industry, all the flights, all the passengers
• Climate data, measurements from tens of thousands of weather stations across the world

These terms are the basic concepts in statistics which are population, sample and data. 

A population data set contains all members of a specified group (the entire list of possible data values). In this question, 56 million people show the entire population that can vote in the country. 

A sample 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. Here, instead of asking 56 million people in the process of public opinion poll, 2000 people selected from the entire population. We call this groups as sample.

Data are individual pieces of factual information recorded and used for the purpose of analysis. These 2000 people were asked which party they would vote for and their responses were recorded as data.

This is a ratio measure. It is ratio scale because it has a true value of 0 (no downloads at all). If your answer is incorrect, please review “Measurement Scales” section in the first chapter.

The ordinal scales are categorical variables and typically measures of non-numeric concepts. This type of variable classifies according to rank.

Only II and III are ratio variables.

I- IQ (intelligence scale): This is an interval variable. It has values of equal intervals that mean something but there is not an absolute zero. In other words, zero IQ does not mean you do not have any cognitive abilities. 

II- Weights of newborn babies: This variable is continuous and ratio. It is continuous because weights (kg, g, mg or etc.) can be broken down into very small amounts.Ratio scales have a clear definition of zero.

III-Income earned in a month: It is also ratio because it has an absolute zero and the ratios are meaningful.

IV-Types of blood as A, B, AB and O. This variable is categorical and nominal: The type of blood tells us something meaningful (dominant or recessive genes etc.) but has no meaningful order.

V-Level of Agreement: yes, maybe, no. This is an ordinal variable and the order of categories cannot quantify–how much better it is.  For example, is the difference between “Yes” and “Maybe” the same as the difference between “Maybe” and “No” ? We can’t say.

This is an ordinal (categorical) scale because ordinal scales provide an information about the order of choices, such as in a people opinion survey.

This is an observational study. Observational research is non-experimental because nothing (variables, participants etc.) is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting.

The pie graph shows a nominal variable.A nominal scale describes a variable with categories that do not have a natural order or ranking. Here, the frequency of the categories of the nominal variable is shown in percentages. Numbers do not indicate that the variable is a numerical variable, it just refers to the frequency of the categories.

Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered. Big data often comes from multiple sources and arrives in multiple formats. Other concepts are old terms commonly used in statistics.