Dr Nelson's Statistics Semester One, And Beyond

Classify data as categorical or quantitative to determine suitable analysis methods, with nominal and ordinal subtypes for categorical data, and discrete and continuous for quantitative data, including binary variables.

Identify variable types by classifying act score as quantitative and continuous, place of birth and favorite candy as categorical and nominal, grade as ordinal, and scholarship award as quantitative.

Explore how statistics uncover relationships between variables, using study hours and exam scores as an example. Learn to chart data, see upward trends, and discuss prediction and error in models.

Explore how statistics reveal relationships between variables in program evaluation, using binary participation and continuous outcomes like acceptance rates and scholarships to judge meaningful differences.

Dr Nelson explains how to analyze survey data with categorical and ordinal variables, showing why averages don't apply, and how to use frequencies and most frequent responses to interpret surveys.

Create a continuous composite from related categorical survey items to quantify certainty about graduating college, enabling average, correlation, and regression analyses.

Explore measures of variation and dispersion, including variance, standard deviation, range, and inter quartile range, with sample and population formulas using sum of squared errors and n or n-1 denominators.

Compute the range by subtracting the lowest observation from the highest; for example, X range is 14, time spent studying 14 hours, and exam scores 33 points.

Calculate the standard deviation by hand from data by squaring errors, summing them to form the sample variance with n-1, then take the square root to obtain 4.89 hours.

Explore measures of dispersion in Python with pandas, revealing mean, standard deviation, variance, range, quartiles, and interquartile range via df.describe().transpose(), with notes on percentile differences.

Learn how standard deviation highlights meaningful differences between groups, using Stata to compare mean weights of domestic and foreign cars, and express differences as effect sizes such as Cohen's d.

Explore the standard error, a controversial statistic with multiple calculation methods, and compare different approaches used in tools, videos, and textbooks.

Prepare the room amount data from the integrated post-secondary education data system in Excel, creating a working copy and removing missing values to illustrate the standard error.

Learn to create violin plots in Stata with the Violin Plot package, install and run via plot, build horizontal and wage-by-industry visuals, and adjust labels with the graph editor.

discover how to obtain summary statistics for income in Stata, including mean, standard error, median, min, max, and percentiles, and interpret the 95 percent confidence interval for the population mean.

Learn to calculate confidence intervals by hand for a large sample using LSW 88 DTA dataset, focusing on the age variable and standard error, variance, and z-based margins in Stata.

Navigate data types as a continuum, weighing quantitative, ordinal, and nominal options for birth order, age, and student counts, and defend practical discrete or continuous choices.

Evaluate the effect size to compare mean scores between program participants and nonparticipants. Analyze year one and year two data with standard deviation to interpret score differences.

Explore how to compute Cohen's d to compare program outcomes using means and pooled standard deviation, illustrated with year one and year two differences and a 0.7 effect size.