Not every statistical operation can be used with every set of data. Data can be classified into four levels of measurement. They are from lowest to highest level :. Data that is measured using a nominal scale is qualitative. Categories, colors, names, labels and favorite foods along with yes or no responses are examples of nominal level data.
Nominal scale data are not ordered. Nominal scale data cannot be used in calculations. Data that is measured using an ordinal scale is similar to nominal scale data but there is a big difference. The ordinal scale data can be ordered. Like the nominal scale data, ordinal scale data cannot be used in calculations. Data that is measured using the interval scale is similar to ordinal level data because it has a definite ordering but there is a difference between data.
The differences between interval scale data can be measured though the data does not have a starting point. Temperature scales like Celsius C and Fahrenheit F are measured by using the interval scale.
Differences make sense. But 0 degrees does not because, in both scales, 0 is not the absolute lowest temperature. Interval level data can be used in calculations, but comparison cannot be done. There is no meaning to the ratio of 80 to 20 or four to one. Data that is measured using the ratio scale takes care of the ratio problem and gives you the most information.
Ratio scale data is like interval scale data, but it has a 0 point and ratios can be calculated. You will not have a negative value in ratio scale data.
For example, four multiple choice statistics final exam scores are 80, 68, 20 and 92 out of a possible points given that the exams are machine-graded.
The data is nominal and defined by an identity, can be classified in order, contains intervals and can be broken down into exact value. Weight, height and distance are all examples of ratio variables. Data in the ratio scale can be added, subtracted, divided and multiplied. The number zero means that the data has no value point. An example of this is height or weight, as someone cannot be zero centimetres tall or weigh zero kilos — or be negative centimetres or negative kilos.
Examples of the use of this scale are calculating shares or sales. Of all types of data on the scales of measurement, data scientists can do the most with ratio data points. To summarise, nominal scales are used to label or describe values. Ordinal scales are used to provide information about the specific order of the data points, mostly seen in the use of satisfaction surveys.
The interval scale is used to understand the order and differences between them. The ratio scales gives more information about identity, order and difference, plus a breakdown of the numerical detail within each data point.
Once data scientists have a conclusive data set from their sample, they can start to use the information to draw descriptions and conclusions. To do this, they can use both descriptive and inferential statistics. Descriptive statistics help demonstrate, represent, analyse and summarise the findings contained in a sample. They present data in an easy-to-understand and presentable form, such as a table or graph. Without description, the data would be in its raw form with no explanation.
One way data scientists can describe statistics is using frequency counts , or frequency statistics, which describe the number of times a variable exists in a data set.
Other examples include qualifications of education, such as high school diploma, a university degree or doctorate, and categories of marital status, such as single, married or divorced. To calculate continuous data points, such as age, data scientists can use central tendency statistics instead.
To do this, they find the mean or average of the data point. Using the age example, this can tell them the average age of participants in the sample. Inferential statistics are used to develop a hypothesis from the data set. It would be impossible to get data from an entire population, so data scientists can use inferential statistics to extrapolate their results. An example of using inferential statistics is in an election. Even before the entire country has voted, data scientists can use these kinds of statistics to make assumptions regarding who might win based on a smaller sample size.
Data visualisation describes the techniques used to create a graphic representation of a data sample by encoding it with visual pieces of information. It helps to communicate the data to viewers in a clear and efficient way. Effective visualisation can help individuals analyse complex data values and draw conclusions.
The goal of this process is to communicate findings as clearly as possible. A graphic display that features effective messaging will show the data clearly and allow the viewer to gain insights and trends from the data set and reveal the different findings between the data.
The best visual representation of a data set is determined by the relationship data scientists want to convey between data points. Do they want to present the distribution with outliers? Do they want to compare multiple variables or analyse a single variable over time? Are they presenting trends in your data set? Here are some of the key examples of data visualisation.
A bar chart is used to compare two or more values in a category and how multiple pieces of data relate to each other. A line chart is used to visually represent trends, patterns and fluctuations in the data set. Line charts are commonly used to forecast information. A scatter plot is used to show the relationship between data points in a compact visual form. A funnel chart is used to represent how data moves through different steps or stages in a process. Quantitative messages describe the relationships of the data.
Depending on the sample, there are different ways to communicate quantitative data. Nominal comparison: Sub-categories are individually compared in no particular order. Time series: An individual variable is tracked over a period of time, usually represented in a line chart. Ranking: Sub-categories are ranked in order, usually represented in a bar chart. Part-to-whole: Sub-categories are represented as a ratio in comparison with the whole, usually represented in a bar or pie chart.
Deviation: Sub-categories are compared with a reference point, usually represented in a bar chart. Researchers select A systematic samples B Random samples C stratified samples D cluster samples. A group of subjects selected from the group of allsubjects under study is called a A a quantitative data. B a qualitative data. C both qualitative and quantitative. D neither quantitative nor qualitative. Data that can be classified according to color aremeasured on what scale?
A researcher divided subjects into two groups according to gender and then selected members from each group for her sample. What sampling method was the researcher using? What is the term for a characteristic or attribute that can assume different values? Which statement below is false? A Zip codes are an example of qualitative data. B Data that can only be classified into categories is referred to as nominal level of measurement C The number of robberies reported in a city is an example of discrete data.
D A sample is a collection of all-possible individuals, objects or measurements of interest.
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