Quantitative variables are those that can be measured and expressed numerically, allowing for mathematical operations. They can be further categorized into discrete variables, which take on specific values (like the number of students in a class), and continuous variables, which can take any value within a range (like height or temperature). Examples of quantitative variables include age, income, test scores, and distances.
They are variables that can take quantitative - as opposed to qualitative values. For example, the colour of peoples' eyes is a qualitative variable, but their age or shoe size are quantitative variables.
Y
Continuous!
For qualitative variables, appropriate descriptive statistics include frequencies and proportions, as they help summarize categorical data and show the distribution of different categories. For quantitative variables, measures such as mean, median, mode, range, variance, and standard deviation are suitable because they provide insights into the central tendency, spread, and overall distribution of numerical data. The choice of statistics depends on the nature of the data: qualitative data is categorical and non-numeric, while quantitative data is numeric and can be measured.
The month of birth is a qualitative variable because it categorizes individuals into distinct groups based on the month they were born. Unlike quantitative variables, which represent numerical values that can be measured or ordered, the months are labels that do not have inherent numerical significance.
Variables are characteristics or attributes that can take on different values or categories. They can be classified as qualitative (categorical) or quantitative (numerical). Qualitative variables describe qualities or characteristics, such as color or type, while quantitative variables represent measurable quantities, such as height or age. Additionally, variables can be independent or dependent, depending on whether they influence or are influenced by other variables in a study or experiment.
They are variables that can take quantitative - as opposed to qualitative values. For example, the colour of peoples' eyes is a qualitative variable, but their age or shoe size are quantitative variables.
nominal and ordinal is wrong; those are the two types of qualitative variables. Ratio and interval are the two types of quantitative variables.
Measurable variables, also known as quantitative variables, are characteristics or attributes that can be expressed numerically and can be measured on a scale. They include variables such as height, weight, temperature, and age, which can be quantified and analyzed statistically. These variables can be further classified into discrete (countable values) and continuous (infinite values within a range).
Variables can be categorized into several types, primarily including quantitative and qualitative variables. Quantitative variables are numerical and can be further divided into discrete (countable values) and continuous (infinite possible values within a range). Qualitative variables, on the other hand, represent categories or attributes and can be classified as nominal (unordered categories) or ordinal (ordered categories). Understanding these types helps in selecting appropriate statistical methods for analysis.
No, a crosstabulation does not have to include both categorical and quantitative variables. It is primarily used to summarize the relationship between two categorical variables. However, quantitative variables can be categorized into groups or bins to create a crosstabulation, but it's not a requirement.
No, it is quantitative.
They are variables that can take quantitative - as opposed to qualitative values. For example, the colour of peoples' eyes is a qualitative variable, but their age or shoe size are quantitative variables.
The answer depends on the nature of the variables: for a start, whether they are qualitative or quantitative.
Y
Interval and ratio
Continuous!