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STATS - Exam 1
STATS
| Term | Definition |
|---|---|
| Categorical Value | Divides the cases (units) into groups. |
| Quantitative Variable | Measures a numerical quantity for each case. |
| Explanatory Variable | A factor that explains, predicts, or causes changes in another variable. |
| Response Variable | The outcome/ result that is measured, observed, or studies in an experiment to see hoe it changed when other factors are modified. |
| Population | Includes all individuals or objects of interest. |
| Sample | All the cases that data has been collected on. |
| Confounding Variable | Offers a plausible explanation for an association between 2 variables of interest. |
| Observation Study | A study in which the researcher does not actively control the value of any variable but simply observes the values as the naturally exist. |
| Experiment | A study in which the researcher actively controls 1 or more of the explanatory variables. |
| p̂ | Proportion for sample. |
| 𝘱 | Proportion for population |
| Sample Size (n) | The number of cases in a sample. |
| x̄ | Notation of sample mean. |
| μ | Notation of population mean |
| Median | Splits the data in half |
| Outlier | An observed value that is notable distinct from the other values in a dataset. |
| Standard Deviation | Gives a rough estimate of the typical distance of data from the mean. |
| s | Notation for sample standard deviation. |
| σ | Notation for sample population deviation. |
| Σ | Sum |
| Z-Score | A way to determine how unusual a single data value is that is independent of the units used. |
| Correlation Coefficient | a measure of the strength and direction of linear association between 2 quantitative variables. |
| Positive Association | Means that values of 1 variable tend to be higher when values of the other variable are higher. |
| Negative Association | Means that values of 1 variable tend to be lower when values of the other variable are higher. |
| Linear Regression | The process of fitting a line to a set of data. |
| Regression Line | The line of best fit. |