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Created by Claudia Miles
over 8 years ago
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| Question | Answer |
| Quantitative Data | Data that is numerical (Measured in numbers) This data will always have a mean |
| Qualitative Data | Data that is measured by the number of people in a sample This data will have proportions Categorical Data |
| Ordinal Data | Data that is categorical and conveys a ranking e.g. clothing size, age |
| Nominal Data | data that has labels e.g. ethnicity, gender |
| Discrete Data | Measured in specific Values/ numbers |
| Continuous Data | numerical data that can be measured with an infinite number of values |
| Mean | Measure of central location Average of the data set Add all the data points in a population and then divide the total by the number of points. |
| Median | Measure of central location The middle value of a listed data set |
| Mode | Measure of central location The value that occurs most often within a data set |
| Range | The difference between the largest and the smallest values of a data set Range = largest value - smallest value |
| Interquartile Range | A measure of variability, based on dividing a data set into quartiles |
| Variance | The expectation of the squared deviation of a random variable from its mean Variance = std dev ² |
| Standard Deviation | A quantity expressing by how much the members of a sample differ from the mean value for the group |
| Co-efficient of Variation | The coefficient of variation is a measure of spread that describes the amount of variability relative to the mean. CV = Std Dev / Sample Mean |
| Skewness | Measure of Normality The curve appears skewed either to the left or to the right |
| Kurtosis | Measure of normality The sharpness of the peak of a distribution curve |
| cross-sectional data | Data collected at a certain point in time across a number of units of interest |
| Time Series Data | Data collected over time for the same units of interest |
| Covariance | How changes in one variable are associated with changes in a second variable. |
| Correlation coefficient | A measure of the linear relationship between two variables. A positive correlation represents two variables that move in the same linear direction (negative = opposite direction) Between -1 and 1 |
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