Karteikarten: Fundamentals of Data Analysis Techniques — 24 Karten

Data analysis — definition?

Techniques to discover and simplify data structure.

Exploratory vs Inferential — role?

Describe data vs interpret for populations.

Multidimensional Data Analysis — purpose?

Uncover relationships in multiple variables.

Descriptive vs Explanatory — methods?

Summarize data vs reveal variable relationships.

Primary vs Secondary Data — difference?

Collected firsthand vs pre-existing.

Univariate Data Analysis — focus?

Single variable characterization.

Quantitative Discrete Variables — example?

Integer counts like number of children.

Quantitative Continuous Variables — example?

Any value within a range, e.g., height.

Qualitative Variables — type?

Categorical data like color or profession.

Bivariate Data Analysis — aim?

Study relationship between two variables.

Correlation vs Dependence — difference?

Linear relationship strength vs linked variation.

Quantitative-Quantitative Relationships — analysis?

Using correlation or covariance.

Analysis of Data — goal?

Discover structure, simplify, visualize.

Multidimensional Data — methods?

PCA, AFC, clustering, regression.

Descriptive Methods — purpose?

Summarize and synthesize data.

Explanatory Methods — purpose?

Identify and analyze relationships.

Types of Data — categories?

Primary, secondary, time series, cross-sectional, panel.

Univariate Analysis — tools?

Means, medians, histograms, boxplots.

Discrete Variables — graphical tools?

Bar diagrams, cumulative diagrams.

Continuous Variables — graphical tools?

Histograms, cumulative curves.

Qualitative Variables — analysis focus?

Distribution of categories, association tests.

Bivariate Analysis — key?

Joint distribution, dependence, correlation.

Correlation — measure?

Strength and direction of linear relation.

Dependence — implication?

Variables vary together; one influences other.