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Bivariate Data - Coggle Diagram
Bivariate Data
Types of variables
Categorical Variables
Explanation: A categorical variable has values that you can put into distinct groups based on a characteristic. These groups are countable and not infinite
Example:
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grades, gender, blood group type, etc. In this case we have colours
Numerical Variables
Continuous Variables
Explanation: A variable is said to be continuous if it can assume an infinite number of real values within a given interval. It's basically values obtained by measuring
Example: For instance, consider the height of a student. The height can't take any values.
Discrete Variables
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Example: A variable that takes only a finite number of real values. (e.g., 1, 3, 5 and 1,000)
Data
Multivariate Data
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Example:based on the season, we cannot predict the weather of any given year. Several factors play an important role in predicting the same. Such as humidity, precipitation, pollution, etc.
Bivariate Data
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Bivariate data are pairs of data values,m with two pieces of data from the same subject i.e a person’s height and arm-spamEach pair of values is plotted as a point on SCATTER PLOT
Regression
Explanation: A regression line indicates a linear relationship between the dependent variables on the y-axis and the independent variables on the x-axis.
Example:
Causation
Explanation: Causation is a relationship between two events, or variables, in which one event or process causes an effect on the other event or process
Example: For example, research tells us that there is a positive correlation between ice cream sales and sunburns. Meaning, as ice cream sales increase, so do instances of sunburns.
Correlation
Explanation: Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate)
It's a common tool for describing simple relationships without making a statement about cause and effect.
Example:
Predictions
Extrapolations
Explanation: Extrapolation is the statistical technique aimed at inferring the unknown from the known.
Example: For example, It attempts to predict future data by relying on historical data, such as estimating the size of a population a few years in the future on the basis of the current population size and its rate of growth
Interpolation
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Example: Based on the given data set, farmers can estimate the height of trees for any number of days until the tree reaches its normal height. For example, based on the data below, the farmer wants to know the tree's height on the 7th day. He can find it out by interpolating the values below. The height of the tree on the 7th day will be 70 MM.
Variables
Response Variables
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Example: Observing the effect it has on this variable
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