One of two type of statistics. It consists of methods for organizing and summarizing information that has been collected. The purpose is simply to provide an overview of the information and describe its important characteristics.
One of two type of statistics. It consists of methods for making predictions or inferences about a larger population from observations and analyses of a smaller group of individuals and measuring the reliability of the conclusions.
It is information (e.g., a characteristic or quality) about a person or object that can be represented by numbers.
Also referred to as qualitative data. Numbers are assigned to certain data (e.g., Canada - 1, Japan - 2, Kongo - 3...). The number represent qualitative information.
Ordinal level variables
It is one of levels in Categorical variables. A data deal with categories that can be organised in some logical sequence known as "rank order". (e.g;. grading of teaching method: well done-fare-waste of time OR GCS)
Nominal level variables
It is one of levels in Categorical variables. It classifies characteristics into named categories which are assigned numbers. There is no implied order in the numbering of the categories. No information is lost or gained by changing their order.
A kind of variables that can be expressed with a measurement. For example, number of children in a household (3 daughters) and body weight (e.g., 37.62 kilograms) are quantitative as the numbers are meaningful. The number represent actual quantity of measurement.
One of two types of Quantitative Data. Expressed with whole number such as 3 daughters or 12 admissions instead of 2.4 daughters or 10.24 admissions.
One of two types of Quantitative Data. That does not have to be whole numbers. For example, weight as measured on a sophisticated electronic scale may be precisely as 65.25 kilograms. Time is another example. A day can be divided into hours, minutes, seconds, milliseconds, and so forth depending on the measurement standard.
Interval level variable
It is one of levels in Quantitative variables.
Its zero point is arbitrary. (e.g. Temp; F and C)
Ratio level variable
It is one of levels in Quantitative variables.
It has all the properties of the interval scale plus an absolute zero point determined by nature.
Because the zero point is not arbitrary, it is possible to multiply and divide across a ratio scale.
It divides the total range of the data into a number of equal groups.
If i = 3, three consecutive numbers from the data create a category (a group). Typically, you wanna see 10-15 categories in the whole data.
One of measures of variability. The highest number - lowest number +1
A kind of tabular display. It is the display of list of categories (titled "Class interval i ") and its frequency "f". (e.g. category = cough, frequency = 3 as three people with cough.)
It is a table records and analyzes the relation between two or more categorical variables
Measures of Central Tendency
mode, median, and mean.
It is the value that occurs most frequently in a data set.It is the only measure of central tendency for nominal level data.
bimodal or multimodal distribution
Two or more values occur with equal high frequency in a frequency distribution.
The value which is the middle of the ordered value. e.g. ) 14728 = 12478. the __ is 4.
One of measure of variability. It is the most commonly used with measure of central tendency. It locates the "centre of gravity" of the distribution. It is the arithmetic average of all values in a set of data. If the distribution has extreme values at one end of the distribution, the ___ is pulled in the direction of the extreme values. In such cases, the ___ gives a misleading indication of the average score of the data set.
One of measures of variability. They are data set divided into many equal parts. The most commonly used are "Quartiles" which divide the data set into four equal parts, "Percentiles" divides the data set into 100 equal parts. The "interquartile range" is the range of values that are found between the 25th percentile (first quartile) and the 75th percentile (third quartile). This statistic tells how the middle 50% of the data is spread. The median is used as the measure of central tendency. Interquartile ranges, like the median, are not affected by extreme values.
One of measures of variability. The most widely used. Like the mean, it takes into account all the values in the data set. It express the variation in a data set by indicating how far, on average, the data values are from the mean. It is large when the value is far from the mean, small when the value is close. The mean is used as the measure of central tendency.
Dichotomous (or binary) data
A special type of nominal data where there are only two categories (e.g. positive - 1, negative - 2).
Data obtained from observing two variables.
How the values scatters
Measures of location
It indicates where individual values are found in a data set
Measures of variability
A way to describe data set as a whole. It indicates the amount of variation or spread in a data set. These measures include the range, interpercentile range, and the standard deviation.
The range of values that are found between the 25th percentile (first quartile) and the 75th percentile (third quartile). This is a part of assessment when quartiles are used as measure of variability.
Distributions that have a few extremely low values. This causes the left tail of the distribution to stretch further to the left (the peak of mountain to be on the right side).
True Class Limit
More precise range of each categories in frequency distribution. e.g.) if a category range 2-4, TCL can be 2.0-4.9.
It expresses the frequency of a category as a proportion of the whole (in %). It is used in frequency distribution often.
It is cumulative percentage result used with relative frequency in frequency distribution.