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Importance of statistics in daily life
How are statistics used in everyday life
Example of a Descriptive Statistics essay
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Recommended: Importance of statistics in daily life
Write a short summary that addresses the following criteria:
• Define “statistics”.
• Identify and describe different types of statistic and levels of measurement.
• Describe the role of statistics in business decision making.
• Provide at least three examples or problem situations in which statistics was used or could be used.
• Format your paper according to APA standards
• The University generally requires formal papers to use section headings to establish structure. For this short paper, section headings are not required, but they are encouraged.
Submit your paper as a Word document with file name QNT351_S1.
Statistics may simply refer to numerical information, or can be defined as “the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions (Lind, Marchal & Wathen, 2011). Types of statistics are Descriptive and Inferential (also known as Statistical Inference). As Statistics is the science, Descriptive Statistics is the method of performing the functions of Statistics and presenting the data in a useful ...
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
A researcher determines that 42.7% of all downtown office buildings have ventilation problems. Is this a statistic or a parameter; explain your answer.
The following article analysis review by Team B illustrates and identifies several examples of statistics abuse in the practical world as a result of flawed research. The following examples demonstrate how a manger could and in many examples, does make erroneous decisions due to inaccurate statistics. The team has compiled the results by detailing the respective articles.
Furthermore, the methods applied convey “the techniques or procedures used to gather and analyze data that is
1. Give some examples of how the results of a study might be significant statistically yet unimportant educationally. Could the reverse be true?
The article’s organization is a rhetorical method for navigation that are effective at showing purpose. For organization the author follows a rough IMRAD format, excluding methods and results. Each paragraph has a heading that is numbered;
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
In statistics, a population is a collection of individuals, things, events, etc. The population is the topic that one wants to make inferences on, whereas a sample is a subset of the population that is being collected—to be studied. After the sample is studied in statistics, one draws an inference of the population. There are four general sampling methods used in statistics: representative sample, random sample and quasi-random sample, stratified and quota sample, convenience sample, and purposive sample. A representative sample should be unbiased and thus properly indicate a characteristic of the entire population. In a random sample nothing is biased; in other words, every individual, thing or event in the population has the same chance of being selected for the sample. Therefore, because of the randomness of the sampling, the selection of one item from the population in no way effects the selection of another item. A quasi-random sample is simply a number (nth), which is
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages.
I used these statistics because I know the kids have little to no knowledge about it. These are not topics talked about in school so in my blog I wanted to educated
This study will focus on the issue of obesity among people. Obesity is one of the prominent issues that the society is facing due to the insufficient information that people have regarding the amount of food intake as well as how people ought to balance their diet. It seems that the problem of obesity is not only experienced in one country but in different countries and this does not only choose certain gender and age but a person who suffers obesity could be an adult or a child, female or male.
On the other hand, Quantitative research refers to “variance theory” where quantity describes the research in terms of statistical relationships between different variables (Maxwell, 2013). Quantitative research answers the questions “how much” or “how many?” Quantitative research is an objective, deductive process and is used to quantify attitudes, opinions, behaviors, and other defined variables with generalized results from a larger sample population. Much more structured than qualitative research, quantitative data collection methods include various forms of surveys, personal interviews and telephone interviews, polls, and systematic observations. Methods can be considered “cookie cutter” with a predetermined starting point and a fixed sequence of
This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However, reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain group’s agenda. Although most statistical data presented may not seem to affect us personally in our daily lives, it can however have an impact. For example, statistics can influence the way people vote on certain issues.
Probability and Statistics most widespread use is in the arena of gambling. Gambling is big all over the world and lots of money is won and lost with their aid. In horse racing especially the statistics of a horse in terms of its physical condition and winning history sway numbers of persons into believing that the mathematical evidence that is derived can actually be a good indicator of a race’s outcome. Usually it is if the odds or probability are great in favor of the desired outcome. However the future is uncertain and races can turn out any of a number of different ways.
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.