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importance of statistics in daily life
discussion of descriptive statistics
the advantages of descriptive statistics
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Statistics are necessary for scientific research because they allow the researchers to analyze empirical data needed to interpret the findings and draw conclusions based on the results of the research. According to Portney and Watkins (2009), all studies require a description of subjects and responses that are obtained through measuring central tendency, so all studies use descriptive statistics to present an appropriate use of statistical tests and the validity of data interpretation. Although descriptive statistics do not allow general conclusions and allow only limited interpretations, they are useful for understanding the study sample and establishing an appropriate framework for the further analysis in the study. Further analysis using appropriate statistical methods allows the researchers to establish correlations between independent and dependent variables, define possible outcomes, and identify areas of potential study in the future accurately. Statistics is important for researchers because it allows them to investigate and interpret the data more accurately, and researchers will notice patterns in the data that would be overlooked otherwise and result in inaccurate and possibly subjective conclusions (Portney & Watkins, 2009).
Frequency distribution is a method used in descriptive statistics to arrange the values of one or multiple variables in a sample, so it will summarize the distribution of values in a sample. Frequency distribution is the most basic and frequently used method in statistics because it creates organized tables of data which can be used later to calculate averages or measure variability. The organized data frequency distribution provides continuous data that is easier to work with than raw data obtai...
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...loser to the population mean and the plot would display a normal curve because a sampling distribution always forms a normal curve (Portney & Watkins, 2009). When the frequency distribution graph shows a normal curve, it is possible to determine its variability and estimate the standard error of the mean in compliance with the sample data. Unlike probability, an estimate of the population distribution allows researchers to establish the probability of selecting a sample with a predictable mean. Although the sampling distribution for predicting single outcomes is not applicable in reality, sample data can be used to draw inferences about the entire population from one sample, but it is never used to measure variance directly.
However, sample data finds applications in several researches that require estimating unknown population parameters (Portney & Watkins, 2009)
Answer: Mean is the best measure of central tendency that can be used for frequency polygons because the highest mean is represented by the highest bar in the frequency polygon.
Many statistical ideas were mentioned in the Barron’s guide. In the topic called Graphing Display the Barron’s guide discusses the different types of graphs, measures of center and spread, including outliers, modes, and shape. Summarizing Distributions mentions different ways of measuring the center, spread, and position, including z-scores, percentile rankings, and the Innerquartile Range, and its role in finding outliers. Comparing Distributions discusses the different types of graphical displays and the situations in which each type is most useful or appropriate. The section on Exploring Bivariate Data explains scatter plots in depth, discussing residuals, influential points and transformations, and other topics specific to scatter plots. Conditional relative frequencies and association, and marginal frequencies for two-way tables were explained in the section entitled Exploring Categorical Data. Overview of Methods of Data Collection explained the difference between censuses, surveys, experiments, and observational studies. Surveys are discussed more in depth in Planning and Conducting Surveys, including characteristics of a well-designed and well-conducted survey, and sources of bias. Planning and Conducting Experiments explains experiments in depth; going over confounding, control groups, placebo effects, and blinding, as well as randomization. Basic rules for probability are discussed in Probability as Relative Frequency, including the law of large numbers, addition rule, and multiplication rule. Other topics discussed in this section include the different types of probability calculations. Combining Independent Random Variables discusses manners in which two variables can be compared to each other and things to be wary of while doing so.
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
Generally speaking, Karl Pearson and Ronald Aylmer Fisher are two persons who accounts for the greatest room in this book, due to their excellent work and philosophical difference in approach to distributions. Karl Pearson regarded statistical distributions as depicting a real image of data while Fisher viewed the collected data as the estimation
Statistics Project I have been given instructions to collect data for my GCSE statistics coursework and then to represent them by interpreting them using graphs and attributes, which I think influence the prices of a second hand car. Below is my coursework flowchart that will show the steps I will take to complete my coursework. FLOWCHART = ==
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages.
There are two histograms, showing information on GPA, and showing information on final grade. Histograms are commonly used with interval or ratio level data (Corty, 2007). The data in the GPA is distributed and slightly skewed to the right, which means it has a positive skew and has a peaked distribution. The final histogram also has a leptokurtic frequency distribution, but is skewed to the left meaning this has a negative skew.
Provide at least three examples or problem situations in which statistics was used or could be used.
...will fall within the first standard deviation, 95% within the first two standard deviations, and 99.7% will fall within the first three standard deviations of the mean. The Empirical Rule is used in statistics for showing final outcomes. After a standard deviation is found, and before exact data can be collected, this rule can be used as an estimate to the outcome of the new data. This probability can be used for gathering data that may be time consuming, or even impossible to found. When the mean equals the median and the values cluster around the mean and median, producing a bell-shaped distribution, then we can use the empirical rule to examine the variability. In this bell-shaped data set, we can calculate the mean and the standard deviation. The mean means the average value of the set of data. The standard deviation means the average scatter around the mean.
Normal distributions are very informative in statistics, it is type of continuous distribution. It is often used in both natural and social sciences to help shed light on random variables where their distri-bution is not known.
As a population, we are bombarded with percentages and statistics, but how does one know if what we are being told is correct? The book How to Lie With Statistics by Darrel Huff was written to help readers better understand statistics especially when they are presented to us in ways that can be misleading or misunderstood. The book is not meant as a guide on how to change or manipulate statistical numbers. However, if statistics are not presented properly or perhaps purposely misleading people, this book will help readers question or form their own opinions from data. Most people simply are not that interested when you hear the word statistics and many times people do not believe the numbers presented. This mistrust occurs most often for two reasons: the person not being able to see the raw data and where or how it was collected and the person not being able to verify the credibility of the information presented. Throughout the book, Huff discusses different statistical techniques that can be used improperly and how one can discern good statistics from those that may have been manipulated.
In evaluating statistical data one thing to consider is the measure that is used. By understanding the different statistical measurement tools and how they differ from one another, it is possible to judge whether a statistical graph can be accepted at face value. A good example is using the mean to depict averages. This was demonstrated by using the mean as a measure of determining the distribution of incomes. The mean income depicted was, $70,000 per year. At face value, it looks as though the sample population enjoys a rather high income. However, upon seeing individual salaries, it becomes obvious that only a few salaries are responsible for the high average income as depicted by the mean. The majority of the salaries were well under the $70,000 average. Therefore, the mean distributed income of $70,000 was at best misleading. By also looking at the median and mode measures of the income distributions, one has a clearer picture of the actual income distributions. Because this data contained extreme values, a standard deviation curve would have given better representation of salary distribution and would have highlighted the salaries at the high level and how they skewed the mean value.
Often uses random sampling to select a large statistically representative sample from which generalizations can be drawn.
In this article we can study bell curve normal distribution, which figure most significantly in statistical theory and in application. Normal distribution is also called as the Normal probability distribution. Let us see how to calculate normal distribution. The normal distribution looks like a bell shaped curve. Hence it is also known as normal curve of distribution.
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.