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Death Valley - Hot enough to fry an egg Death Valley in the US, located in Eastern California is considered the hottest place on Earth. Death Valley holds the record for the highest recorded air temperature on Earth, at 134 °F (57 °C). Summer temperatures often top 120 °F (49 °C).
Eye color Eye color is determined by iris (thin, circular structure in eye) pigmentation, this contains melanin which colors the skin too. The higher is the amount of melanin, the dark will be the eye color. This is a hereditary factor, depends on our parents' eyes color.
Auto racing Auto racing includes information about different styles of racing and auto racing safety. It is a kind of motor-sport involving the racing of automobiles for competition.
Quantitative versus Qualitative Data

Data: The word 'data' means information in the form of numerical figures or a set of given facts. It can be anything, that is, numbers, words, measurements, observations or even just descriptions of things. There are two types of data: Quantitative or numerical data and Qualitative or categorical data.

Quantitative or Numerical data: The data which are identified or measured on a numerical scale is known as Quantitative data or numerical data. Ex: number of students appearing for an AP Statistics examination, the daily temperatures in Death Valley, etc. Quantitative data can either be discrete or continuous.

The data are said to be discrete, if the values or observations belonging to it are distinct and separate, that is, they can be counted. Ex: number of members in a family. The data are said to be continuous, if the values or observations belonging to it are measured or take on any value within the finite or infinite interval. We can count, order and measure continuous data. Ex: weight, height, temperature, etc.

Qualitative or Categorical data: Qualitative or categorical data are the data that can be classified into a group. A set of data is said to be a categorical, if the values belonging to it can be sorted according to category. Ex: gender, eye color, ethnicity, etc. Qualitative data can be either nominal or ordinal.

The data in which there is no natural order between the categories is known as nominal data. We can count but not order or measure nominal data. Ex: eye color. The data in which an ordering exists between the categories is known as ordinal data. We can count and order, but not measure ordinal data. Ex: auto racing.

How to Use Statistics for Science Projects Statistics acts as a tool to strengthen the use of statistical techniques in science fair projects. They help summarize the data, interpret the results of experiments and draw conclusions. As it is the study of the collection, organization, analysis, interpretation and presentation of data, it deals with all aspects including the planning of data collection in terms of the design of surveys and experiments.
Application of EDA in a game of baseball Be it a match of cricket, or a game of baseball, it cannot be complete without the scores, and the statistical figures given for the same. The speed of ball, the runs scored by every player, and the records of their performance are interesting for every sports lover. The most common instances, where the application of descriptive statistical analysis can be observed are the various sports.
Overview of Statistics

Statistics definition: Statistics is a way of reasoning using a collection of tools and methods designed to help us understand the world. Statistics is the science of data analysis which deals with the collection, presentation, organization and analysis of data and interpretation of numerical data.

In statistics, emphasis is mostly on conceptual understanding and interpretation rather than on actual arithmetic computation. Major concepts that are covered in statistics are:
(i) Exploratory Data Analysis
(ii) Collecting Data
(iii) Anticipating Patterns
(iv) Statistical Inference.

i) Exploratory Data Analysis (EDA): An exploratory data analysis is also known as Descriptive statistics. It makes use of graphical and numerical methods to analyze distributions of data, including one-variable data, two-variable data and categorical data.

In one-variable data analysis, we learn about graphical displays (such as dotplots, boxplots, bar charts, histograms and stemplots), shape of a distribution, measures of center (median and mean), measures of spread (variance, standard deviation, range and interquartile range) and measures of position (z-score).

In two-variable data analysis, we learn about relationship between two or more variables, if so, what is the nature of that relationship (using correlation and regression) and graphical displays such as scatterplots.

U.S. News has started collecting data for the 2014 best college rankings U.S. News started to collect data from the three U.S. News statistical surveys - main, financial aid and finance. The U.S. News search tool will help you narrow your search to find the perfect college. These surveys collect information on such factors as enrollment, faculty, tuition, room and board, SAT and ACT scores, school finances, activities, admission criteria, graduation, retention rates, sports, financial aid and college majors.
Types of Research Analysis and Descriptive Study There are many different types of research studies. Types of research analysis and descriptive studies are qualitative and quantitative experiments, co-relational studies, quasi-experiments and comparison studies. Inferential statistics infer probability of correlation of data, which is not necessarily causation. Inferential statistics generalize from the research sample to a larger population.
Collecting Data and Anticipating Patterns

ii) Collecting Data: The data must be collected according to the well-developed plan if valid information on a conjecture is to be obtained. This plan includes clarifying the question and deciding upon a method of data collection and analysis. In this concept, we learn about overview of methods of data collection, planning and conducting surveys and planning and conducting experiments.

iii) Anticipating Patterns: Here, we explore random phenomena using probability and simulation. Probability is the tool used for anticipating what the distribution of data should look like under a given model. In this concept, we learn about basic rules of probability, discrete and continuous random variables, rules for combining random variables, normal distribution, binomial distribution, geometric distribution & sampling distribution.

iv) Statistical Inference: The common technique that is used in this concept is to select a random sample from the population and, based on an analysis of the data, make inferences about the population from which the sample was drawn. Values that describe a sample are called statistics and values that describe a population are called parameters. In statistical inference, we use statistics to estimate parameters. In this concept, we learn about confidence intervals & statistical significance, inference for means & proportions, inference for regression and inference for categorical data.

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