# Introduction descriptive statistics

These are used to measure the amount of spread or variability within your data. This scenario shows how descriptive statistics — namely the meanmedianand standard deviation — can be used to quickly summarize a dataset.

The median is Therefore the median is a much more suited statistic, to report about your data. These expensive houses will heavily effect then mean since it is the sum of all values, divided by the number of values.

Physicist James Clerk Maxwell used the normal distribution to describe the relative velocities of gas molecules. Lastly, you learned about Leptokurtic, Mesokurtic and Platykurtic distributions. It is simply the square root of the variance and because of that, it is returned to the original unit of measurement.

You recently completed the first exam, and are now sitting in class waiting for your graded exam to be handed back. In the example Introduction descriptive statistics Figure 1the median fell slightly closer to the middle of the grade distribution than did the mean. The interquartile range IQR is a measure of statistical dispersion between upper 75th and lower 25th quartiles.

Normal Distribution Function A normal distribution with a mean of 0 and a standard deviation of 1 is called a standard normal distribution. You can see both for a positively skewed dataset in the image below: Therefore they are both derived from the mean.

To review again, descriptive statisticsorganize and summarize data and inferential statisticsanalyze samples to make inferences and predictions Inferential statistics in the social sciencesrefer to many statistics tests using samplesto describe the social characteristics of populations.

In other words, the mean and median roughly approximate the middle value of a dataset. It should look like the distribution on the picture below: Most distributions have only one peak but it is possible that you encounter distributions with two or more peaks.

The study of statistics can help you make reasonable guesses about the answers to these questions. To better understand the concept of a normal distribution, we will now discuss the concepts of modality, symmetry and peakedness. Central tendency determines the tendency for the values of your data to cluster around its mean, mode, or median.

Data sets with high kurtosis have heavy tails and more outliers and data sets with low kurtosis tend to have light tails and fewer outliers.

The mean score is Measures of Variability The most popular variability measures are the range, interquartile range IQRvariance, and standard deviation. Kurtosis Kurtosis measures whether your dataset is heavy-tailed or light-tailed compared to a normal distribution.

When you have a low standard deviation, your data points tend to be close to the mean. Area under the standard normal distribution curve would be 1. If there had been more variation in the exam scores, the standard deviation would have been even larger. Squaring the difference between each data point and the mean and averaging the squares renders a variance of Descriptive statistics are statistics methodsthat organize and summarize quantitative data.

It is simply the square root of the variance and because of that, it is returned to the original unit of measurement. This basically means, that if your data is not normally distributed, you need to be very careful what statistical tests you apply to it since they could lead to wrong conclusions. In the first class Class A — the light blue bars in the figureall of the students studied together in a large study group and received similar scores on the final exam. Because of the way the mean and median are calculated, the mean tends to be more sensitive to outliers — values that are dramatically different from the majority of other values.

Let's take a look at the most basic form of statistics, known as descriptive statistics. Without statistics this task would come down to simple guess work.

The picture below illustrates that perfectly.Descriptive statistics are statistics methods that organize and summarize quantitative data. Inferential statistics are statistics methods that analyze a sample which is a subset of a population to make inferences and predictions about a population.

This area of statistics is called “Descriptive Statistics.” You will learn how to calculate, and even more importantly, how to interpret these measurements and graphs. A statistical graph is a tool that helps you learn about the shape or distribution of a sample or a population.

Using descriptive statistics in science. As we’ve seen through the examples above, scientists typically use descriptive statistics to: Concisely summarize the characteristics of a population or dataset. Determine the distribution of measurement errors or experimental.

TOPIC 1 INTRODUCTION & DESCRIPTIVE STATISTICS BASIC CONCEPTS Situation: A journalist is preparing a program segment on what appears to be the relatively disadvantaged financial position of women and the incidence of female poverty in Australia.

Intro to Descriptive Statistics. Introduction. Doing a descriptive statistical analysis of your dataset is absolutely crucial. A lot of people skip this part and therefore lose a lot of valuable insights about their data, which often leads to wrong conclusions.

Take your time and carefully run descriptive statistics and make sure that the. Introduction: In Descriptive statistics you are describing, presenting, summarizing, and organizing your data, either through numerical calculations or graphs or tables.

Some of the common measurements in descriptive statistics are central tendency and others the variability of the dataset. Introduction descriptive statistics
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