Data 8 Midterm 1 Review Session

Vocabulary practice flashcards created from the Data 8 Midterm 1 review session notes covering experimental design, array operations, table methods, data visualizations, and functions.

Association

Any relation observed between a treatment and an outcome.

Causation

Occurs when an association is observed between a treatment and an outcome, and the treatment directly causes the outcome.

Observational Study

A study in which researchers observe outcomes without directly assigning treatments, making it difficult to establish causality due to potential confounding factors.

Confounding Factor

An underlying variable or difference between treatment and control groups (other than the treatment itself) that can confuse or distort conclusions about causality.

Randomized Control Trial (RCT)

An experimental setup where individuals are randomly assigned into control and treatment groups to minimize confounding factors and test for causality.

Placebo

A neutral treatment (such as a sugar pill) given to a control group to prevent psychological effects from influencing experimental outcomes.

Assignment Statement

A Python statement where a variable name is assigned to the value of the expression on the right side of the $=$ symbol.

np.arange(start, stop, step)

A NumPy function that returns an array starting at start (inclusive), incrementing by step size, and ending before stop (exclusive).

tbl.where(column, condition)

A table method that filters and returns rows from a table where values in the specified column satisfy a given predicate or condition.

are.between(x, y)

A Table.where predicate that filters for values greater than or equal to $x$ and strictly less than $y$.

tbl.sort(column, descending)

A table method that sorts table rows by the values in a specified column in ascending order by default, or descending order if descending=True.

tbl.select

A table method that creates and returns a new table containing only the specified column names or column indices. (column. only)

tbl.column

A table method that selects a single column by name or index and returns its values as an array. (one column only)

tbl.take

A table method that returns a new table containing only the rows specified by a given array of row indices.(Row only)

tbl.row(row_index)

A table method that returns an entire single Row object at the specified row index.

tbl.apply(func, col1, col2, …)

A table method that calls a specified function on every value (or combination of values across rows) of the specified column(s) and returns an array of results.

Categorical Variable

A variable whose values represent discrete categories or labels (e.g., zip codes) for which taking a numerical average is not meaningful.

Numerical Variable

A quantitative variable representing measurements or counts for which arithmetic operations like calculating an average are meaningful.

Scatter Plot

A visualization that displays the relationship between two numerical variables, where each point represents an individual observation.

Line Plot

A visualization displaying the relationship between two numerical variables, typically used for tracking chronological trends or sequential data over time.

Bar Chart

A visualization used for categorical data that displays categories on the y-axis, numerical counts on the x-axis, equal bar widths, and spaces between bars.

Histogram Bin Area

In a density histogram, the area of a bin is calculated as $\text{width} \times \text{height}$ and represents the percentage of data points in that bin.

Histogram Density Units

The unit of measurement on the y-axis of a density histogram, defined as percent per unit of the x-axis numerical variable.

Histogram Bin Bounds

The convention where histogram bins include their lower bound and exclude their upper bound $[L, R)$, with the exception of the last bin which includes both bounds.

Overlaid Histogram

A plot that overlays multiple histograms to compare the distribution of a numerical variable across different categories.

Return Statement

A statement inside a Python function that outputs a usable value back to the caller, distinct from a print statement which only displays output.

Local Scope

The rule stating that variables defined inside a Python function exist and can only be accessed from within that function.