Have you ever thought of finding the number of male and female students in your college? Show Or have you ever thought about measuring the weight or height of your classmates, or recording the ages of your classmates to determine who is the youngest or oldest in your class? All these are forms of data that can be counted and/or measured and represented in a numerical form. In statistics, these data are called quantitative variables. In this article, we are going to study deeper into quantitative variables and how they compare to another type of variable, the qualitative variables. Quantitative variables meaningQuantitative variables are variables whose values are counted. Examples of quantitative variables are height, weight, number of goals scored in a football match, age, length, time, temperature, exam score, etc. Qualitative variables in statisticsQualitative variables (also known as categorical variables) are variables that fit into categories and descriptions instead of numbers and measurements. Their values do not result from counting. Examples of qualitative variables include hair color, eye color, religion, political affiliation, preferences, feelings, beliefs, etc. Types of quantitative variablesQuantitative variables are divided into two types: discrete quantitative variables and continuous quantitative variables. Details and differences between these two types of quantitative variables are explained hereafter. Discrete quantitative variableDiscrete quantitative variables are quantitative variables that take values that are countable and have a finite number of values. The values are often but not always integers. The best way to tell whether a data set represents discrete quantitative variables is when the variables are countable and the number of possibilities is finite. Continuous quantitative variableContinuous quantitative variables are quantitative variables whose values are not countable. The best way to tell whether a data set represents continuous quantitative variables is when the variables occur in an interval. A discrete quantitative variable is a variable whose values are obtained by counting. A continuous quantitative variable is a variable whose values are obtained by measuring. When you count the number of goals scored in a sports game or the number of times a phone rings, this is a discrete quantitative variable. When you measure the volume of water in a tank or the temperature of a patient, this is a continuous quantitative variable. The table below contains examples of discrete quantitative and continuous quantitative variables,
Distinguish the types of the following variables between discrete and continuous.
Solution Continuous variables.
Discrete variables.
Primary data is the data collected by a researcher to address a problem at hand, which is classified into qualitative data and quantitative data. Qualitative variables deal with descriptions that can be noticed but not calculated. Quantitative variables focus on amounts/numbers that can be calculated. ✓ Both quantitative and qualitative data are used in research and analysis. ✓ Both are used in conjunction to ensure that the data gathered is free from errors. ✓Both can be obtained from the same data unit. Only their variables are different, i.e. numerical variables in case of quantitative data and categorical variables in case of qualitative data. Differences between quantitative and qualitative variables
Determine if the following variables are quantitative or qualitative variables,
Solution Qualitative variables.
Quantitative variables. These are the variables that can be counted or measured.
Quantitative variables can generally be represented through graphs. There are many types of graphs that can be used to present distributions of quantitative variables. ✓ Stem and leaf displays/plot. A graphical type of display used to visualize quantitative data. Stem and leaf plots organize quantitative data and make it easier to determine the frequency of different types of values. ✓ Histograms. A type of graph that summarizes quantitative data that are continuous, meaning they a quantitative dataset that is measured on an interval. Histograms represent the distinctive characteristics of the data in a user-friendly and understandable manner. ✓ Frequency polygons. A line graph used for a visual representation of quantitative variables. Frequency polygons indicate shapes of distributions and are useful for comparing sets of data. In this type of data visualization, the data are plotted on a graph and a line is drawn connecting points to each other to understand the shape of the variables. ✓ Box plots. A graphical representation method for quantitative data that indicate the spread, skewness, and locality of the data through quartiles. Box plots are also known as whisker plots, and they show the distribution of numerical data through percentiles and quartiles. ✓ Bar charts. A graph in the form of rectangles of equal widths with their heights/lengths representing values of quantitative data. A bar graph/chart makes quantitative data easier to read as they convey information about the data in an understandable and comparable manner. The horizontal axis of a bar graph is called the y-axis while the vertical axis is the x-axis. Bar graphs make a comparison between data easier and more understandable. ✓ Line graphs. This is a line or curve that connects a series of quantitative data points called ‘markers’ on a graph. Similar to box plots and frequency polygons, line graphs indicate a continuous change in quantitative data and track changes over short and long periods of time. ✓ Scatter plots. Scatter plots use cartesian coordinates to show values for two variables for a set of data. Scatter plots basically show whether there is a correlation or relationship between the sets of data. Note that some graph types such as stem and leaf displays are suitable for small to moderate amounts of data, while others such as histograms and bar graphs are suitable for large amounts of data. Graph types such as box plots are good when showing differences between distributions. Scatter plots are used to show the relationship or correlation between two variables. Quantitative Variables - Key takeaways
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