There are four major types of descriptive statistics:
- Measures of Frequency: * Count, Percent, Frequency.
- Measures of Central Tendency. * Mean, Median, and Mode.
- Measures of Dispersion or Variation. * Range, Variance, Standard Deviation.
- Measures of Position. * Percentile Ranks, Quartile Ranks.
The mean, median, and mode are equal
The measures are usually equal in a perfectly (normal) distribution.Graphical displays are very useful for summarizing data, and both dichotomous and non-ordered categorical variables are best summarized with bar charts.
Increasing Sample SizeAs the sample sizes increase, the variability of each sampling distribution decreases so that they become increasingly more leptokurtic. The range of the sampling distribution is smaller than the range of the original population.
Definition: Standard deviation is the measure of dispersion of a set of data from its mean. It measures the absolute variability of a distribution; the higher the dispersion or variability, the greater is the standard deviation and greater will be the magnitude of the deviation of the value from their mean.
Numerical digits are the number text characters used to show numerals. For example, the numeral "56" has two digits: 5 and 6. The ten digits of the decimal system are: 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. Some numeral systems need more than ten digits.
Numerical data represent values that can be measured and put into a logical order. Examples of numerical data are height, weight, age, number of movies watched, IQ, etc. To graph numerical data, one uses dot plots, stem and leaf graphs, histograms, box plots, ogive graphs, and scatter plots.
These types include the exact numeric data types ( INTEGER , SMALLINT , DECIMAL , and NUMERIC ), as well as the approximate numeric data types ( FLOAT , REAL , and DOUBLE PRECISION ). The keyword INT is a synonym for INTEGER , and the keywords DEC and FIXED are synonyms for DECIMAL .
A data set (or dataset) is a collection of data. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. Each value is known as a datum. Data sets can also consist of a collection of documents or files.
Numerical data provides an organization with accurate inferences for critical decision making without any emotional or inaccurate bias. Generally represented in the form of diagrams, graphs, and charts, numerical data help evaluate a company's progress basis its past performance. It also helps in competitor analysis.
A numerical variable is a variable where the measurement or number has a numerical meaning. For example, total rainfall measured in inches is a numerical value, heart rate is a numerical value, number of cheeseburgers consumed in an hour is a numerical value.
Numeric variables have values that describe a measurable quantity as a number, like 'how many' or 'how much'. Therefore numeric variables are quantitative variables. Numeric variables may be further described as either continuous or discrete: A continuous variable is a numeric variable.
The mean, or the average, is calculated by adding all the figures within the data set and then dividing by the number of figures within the set. For example, the sum of the following data set is 20: (2, 3, 4, 5, 6). The mean is 4 (20/5).
Time is a continuous variable. You could turn age into a discrete variable and then you could count it. For example: A person's age in years.
A statistic is a numerical summary of a sample. By contrast, a numerical summary of a population is called a parameter. For example, the statistics of 63% from above would be a descriptive statistic, since it is simply a summary of our sample.
Summarize numerical data sets in relation to their context, such as by: Reporting the number of observations; describing the nature of the attribute under investigation, including how it was measured and its units of measurement; giving quantitative measures of center (median and/or mean) and variability (interquartile
In statistics, there are four data measurement scales: nominal, ordinal, interval and ratio. These are simply ways to sub-categorize different types of data (here's an overview of statistical data types) .
The two most commonly used quantitative data analysis methods are descriptive statistics and inferential statistics.
Descriptive Results
- Add a table of the raw data in the appendix.
- Include a table with the appropriate descriptive statistics e.g. the mean, mode, median, and standard deviation.
- Identify the level or data.
- Include a graph.
- Give an explanation of your statistic in a short paragraph.
Interpret the key results for Descriptive Statistics
- Step 1: Describe the size of your sample.
- Step 2: Describe the center of your data.
- Step 3: Describe the spread of your data.
- Step 4: Assess the shape and spread of your data distribution.
- Compare data from different groups.
Quantitative data is defined as the value of data in the form of counts or numbers where each data-set has an unique numerical value associated with it. Quantitative data is usually collected for statistical analysis using surveys, polls or questionnaires sent across to a specific section of a population.
1.2 Data: Quantitative Data & Qualitative Data
| Quantitative Data |
|---|
| Definition | Quantitative data are the result of counting or measuring attributes of a population. |
| Data that you will see | Quantitative data are always numbers. |
The information contained in a data field. It may represent a numeric quantity, a textual characterization, a date or time measurement, or some other state, depending on the nature of the attribute. ( NCI Thesaurus)
