Descriptive Statistics Flashcards
Four Functions of data reduction
Summarization: condensing in to a few meaningful computations
Conceptualization: Visualization of what the summarization represents
Communication: translation into a more understandable form
Interpolation: Making estimates about the true values of the population
Types of descriptive analysis
Descriptive Analysis: To show general patterns (µ, std)
Inferential Analysis: Generalization based on sample findings to the target population
(Hypothesis testing)
Difference Analysis: determine wether real differences exist between groups (t-test, ANOVA)
Associative analysis: Determines the strength/ direction of relationship between two or more variables (correlation, cross tabulation)
Predictive Analysis: Predicting future events based on past results or relationships between two or more variables (regression analysis)
Descriptive Analysis and Associative analysis majors uses:
Descriptive; Most simple way to analyse data. Not much depth but good starting point
Identify 2 products or services that go well together / 2 elements of company and then make necessary arrangements to improve efficiency
Descriptive Analysis
1) Measures of central tendency
Mode Median Mean
2) Measures of variability
Frequency Distribution Range Standard dev
3)Other descriptive measures
Skewness (remember: right skewed, bump is on the left, positive, Median > Mean)
Kurtosis: Meausre of peakedness of distribution (+ = more peaked) (3 = very peaked, 0 = ~Norm. Distrib
Negative values + flat (-1.2 –> square)
Appropriate statistic for analysis - Nominal Scale
Central tendency - Mode
Variability - Frequency / Percentage distribution
Appropriate statistic for analysis - Ordinal Scale
Central Tendency - Median
Variability - Cumulative Percentage Distribution
Appropriate statistic for analysis - Interval Scale
Central Tendency - Mean
Variability - Stand dev
