DESCRIPTIVE STATISTICS Assignment
Assignment: DESCRIPTIVE STATISTICS
DESCRIPTIVE STATISTICS Assignment
For the second SLP, using the data that you collected for the Module 1 SLP, please do the following:
- Calculate the mean, median, mode, variance, and standard deviation of the measurements taken in Module 1 SLP. Show your work and be sure to express each value in units.
- Discuss which measure of central tendency you think most accurately describes the variable that you measured. Provide a thorough explanation.
- Describe the spread/distribution of your data. Be sure to describe the variance of distribution and the concept of standard deviation as a measure of dispersion in your response.
- Conduct a scholarly search on the internet to find reported health statistics on the variable that you are measuring. For example, if you are measuring your total daily caloric intake, American Dietetic Association. Identify the source.
Submit your (2-3 pages) paper by the end of this module.
SLP Assignment Expectations
Assessment and Grading: Your paper will be assessed based on the performance assessment rubric that is linked within the course. Review it before you begin working on the assignment.
A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features from a collection of information,[1] while descriptive statistics (in the mass noun sense) is the process of using and analysing those statistics. Descriptive statistics is distinguished from inferential statistics (or inductive statistics) by its aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.[2] This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently non-parametric statistics.[3] Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented.[4] For example, in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects of each sex, the proportion of subjects with related co-morbidities, etc.
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Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or
dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness.
Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation.
For example, the shooting percentage in basketball is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots made divided by the number of shots taken. For example, a player who shoots 33% is making approximately one shot in every three. The percentage summarizes or describes multiple discrete events. Consider also the grade point average. This single number describes the general performance of a student across the range of their course experiences.[6]
The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot.
In the business world, descriptive statistics provides a useful summary of many types of data. For example, investors and brokers may use a historical account of return behaviour by performing empirical and analytical analyses on their investments in order to make better investing decisions in the future.
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