What Is Statistics: Crash Course Statistics #1
Who uses statistics?
There are many professions and industry applications for statistics. Some of the professions and industries where statistics can be heavily used include:
Essentially, the professional fields that may use statistics commonly include scientific fields and research-based industries like environmental and ecological study, information technology or product development. Statistics can also be used in economic and market industries like real estate, city planning and other fields that rely on population surveys and census data.
For example, a real estate agency might collect and organize data regarding the average price range over a given period for homes in the area. Real estate brokers might then display the data to interpret and analyze how the average price of a home has changed during that time. Using statistics in this way can allow the real estate agency to determine how to price its service fees.
What is statistics?
Statistics is a field of mathematics that focuses on the collection, organization and interpretation of data. When working with statistics, it is not uncommon to represent data visually by displaying it through charts, graphs and other representations.
Many industries apply the use of statistics to solve problems, find solutions and implement operations and business processes that help achieve objectives that can influence a businesss success in its industry.
Types of statistics
There are two main types of statistics. Descriptive statistics is a discipline that summarizes sample data by using the mean or standard deviation. Inferential statistics focus on concluding sample data that can exhibit random variation. These two methods can be applied to the analysis and evaluation of data collected from a sample, such as a demographic survey.
Descriptive statistics focuses on using aspects of data analysis to describe a sample or set of data. Using descriptive statistics, researchers, statisticians and scientists can describe characteristics and patterns of the data set. For instance, a census analyst can find the mean, range of distribution, variability and central tendency of a given set of data regarding household income brackets. Descriptive statistics is the most commonly used method of data collection, organization and display.
For example, a demographer surveys a population of 500 homeowners to find out the average number of people who live in one household. When asked how many people live in the home, the mean of the data set resulted in four people per household. The standard deviation of the sample resulted in 190, meaning out of 500 homeowners, 310 have an average of four people per household. The standard deviation results can show the demographer how far the sample ranges from the average or how near to the average the population is.
Inferential statistics focuses more on the probability of outcomes following specific data tests to conclude a given sample of data. Statisticians use inferential statistics to make generalizations about a given population or the probability of a given population in relation to an inquiry.
For instance, a demographic surveyor might use inferential statistics when surveying a sample of 100 people about which retailers they shop at most. After analyzing the data, they can conclude that 40% of the population surveyed prefers one retailer over another. Based on this conclusion, the surveyor could then make a prediction (the probability) about the outcome of asking the same survey question to a new sample group.
Uncertainty, probability and variation in statistics
Statistics essentially seeks to measure and interpret data to help make predictions about a given outcome. Some factors can affect the outcome of any given study, however, and these factors can contribute to the relationship between a hypothesis and the actual results. Uncertainty and variation in statistics refer to how a set of data gets used to predict outcomes, and probability focuses on creating a hypothesis to test around a set of data.
Variation and uncertainty in statistics
In many different events, an outcome can be uncertain. For example, a meteorologist analyzes the variation in air temperatures, wind movements and moisture levels to predict an outcome that has not yet been determined. The meteorologist in this case is using information about the variation in weather patterns to draw conclusions about the uncertainty of rainy weather for the next day. Variation and uncertainty are aspects of statistics that lead to the probability of an outcome.
Statistics and probability
Probability in statistics focuses on using given information about a data sample to draw conclusions about the probability of an outcome. In the case of the meteorologist, the conclusion they come to when using variation (determined previous weather patterns) and uncertainty (undetermined weather for the next day) can give them enough mathematical information to determine the probability of rainy weather for the next day.
What is the simple definition of statistics?
- SPSS (IBM) …
- R (R Foundation for Statistical Computing) …
- MATLAB (The Mathworks) …
- Microsoft Excel. …
- SAS (Statistical Analysis Software) …
- GraphPad Prism. …
What is statistics and example?
What are the 3 types of statistic?
1 : a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data. 2 : a collection of quantitative data.
What is statistics and its types?