Descriptive Statistics for Data Analysis Course

Module 1: Arithmetic and Algebra for Statistical Calculations

• Review order of operations and algebraic manipulation used in formulas.
• Convert among fractions, decimals, and percentages for analysis.
• Apply exponent rules and roots in statistical computations.
• Set up and solve equations that appear in descriptive statistics.

Module 2: Frequency Distributions for Categorical and Quantitative Data

• Organize data into tables and class intervals.
• Create and interpret histograms, bar charts, and frequency polygons.
• Distinguish between categorical and quantitative distributions.

Module 3: Descriptive Statistics for Categorical Data: Proportions and Percentages

• Compute proportions, percentages, and rates from frequency data.
• Compare categories using relative frequency and percent distributions.
• Present categorical summaries with clear labels and scales.

Module 4: Two and Three-Way Contingency Tables for Categorical Data

• Construct cross-tabulations to explore relationships between variables.
• Calculate joint, marginal, and conditional percentages.
• Identify patterns and potential associations across categories.

Module 5: Descriptive Statistics for Quantitative Data: Averages

• Compute mean, median, and mode and know when to use each.
• Handle grouped data and weighted means.
• Assess sensitivity of measures of center to skew and outliers.

Module 6: Descriptive Statistics for Quantitative Data: Dispersion

• Calculate range, interquartile range, variance, and standard deviation.
• Interpret dispersion to understand variability and consistency.
• Use boxplots and standard deviation rules to summarize spread.

Module 7: Calculating Relative Position with a Z Score

• Standardize values to z scores for comparison across scales.
• Interpret positive/negative z scores and percentiles.
• Apply z scores to identify unusual observations.

Module 8: The Standard Normal Distribution

• Describe properties of the normal curve and symmetry.
• Use standard normal tables to find areas and probabilities.
• Relate empirical rule (68–95–99.7) to real-world data.

Module 9: How to Select a Random Sample from a Population

• Differentiate populations, samples, and sampling frames.
• Implement simple random sampling and avoid selection bias.
• Use random digits or software to draw unbiased samples.