Statistics for Historians

Explore economic history through statistical methods: analyze long-lasting effects of geography on African development (Nunn & Puga), test Protestant ethic hypotheses using county-level data (Becker & Vesman), and examine persistent impacts of forced labor in Peru's mines (Dell). Learn about various dataset structures—cross-sectional, time series, panel, and repeated cross-sectional—in statistics for historians. Next, delve into variable types.

The tutor explores variable types in datasets: ordinal (rankable but no units, like Likert scale responses), interval (equal spacing with units, e.g., years of education), and categorical (no intrinsic ordering, e.g., ethnicity). Avoids misinterpreting arbitrary numeric codes for these variables. Subscribe to @AxiomTutoring.

This video introduces descriptive statistics, an essential first step when analyzing a new dataset. You'll learn how to get a foundational understanding of your data's variables, distributions, and potential patterns. The tutorial focuses on histograms, explaining how to interpret them, the significance of bin size, and their crucial role in detecting outliers that can skew analysis. It also differentiates histograms from bar charts, demonstrating when to use each for various data types, such as categorical or ordinal variables. The video begins by demonstrating how histograms provide insights into data concentration and distribution tails, using the example of Russian household sizes. It then illustrates the power of histograms in identifying extreme outliers, referencing the historical dataset of slave sale prices in Louisiana and explaining why such outliers necessitate data cleaning before further analysis. Finally, the video transitions to bar charts, showing their application for categorical variables like prisoner literacy levels, and clearly distinguishing their function from histograms by highlighting how bar charts represent each distinct value individually. Subscribe to @AxiomTutoringCourses for more tutorials.

In this video, we explore the fundamental measures of central tendency: mean, median, and mode. The sample mean is introduced as the average value of a variable within a dataset, with a detailed explanation of its formula and summation notation. We then delve into the median, defining it as the middle value of an ordered dataset, and illustrate its calculation for both odd and even sample sizes. Percentiles and quartiles are presented as extensions of the median concept, with the 50th percentile being the median itself. Finally, the mode is explained as the most frequently occurring value in a dataset, with considerations for grouped and continuous data, and the possibility of multiple modes. Subscribe to @AxiomTutoringCourses for more educational content.

In this video, we explore which types of variables are suitable for calculating the mean. We begin with a quick recap of how to calculate a sample mean using a simple data set. Then, we define and differentiate between interval, ordinal, and categorical variables, providing clear examples for each. We examine why the mean cannot be calculated for categorical and ordinal variables, highlighting the arbitrary nature of their numerical coding. Finally, we confirm that only interval variables are appropriate for mean calculations. Subscribe to @AxiomTutoringCourses for more educational content.

This video explains how to calculate the median for different types of variables. We start with a recap of what the median is for numerical data and then explore its applicability to categorical, ordinal, and interval variables. You'll learn why the median is not suitable for categorical data but is a valid measure for ordinal and interval variables, with clear examples for each. The video emphasizes the importance of ordering your data before finding the median. Subscribe to @AxiomTutoringCourses for more educational content.

This video explains which types of variables are compatible with calculating the mode. We begin by revisiting the definition of the mode as the most frequent value in a dataset. The video then explores whether the mode can be used with categorical, ordinal, and interval variables, providing clear examples for each. You'll learn that the mode is applicable to all three variable types. Subscribe to @AxiomTutoringCourses for more helpful tutorials.

In this video, we explore the distinctions between the mean, median, and mode and when to use each measure. We start with a data set representing household sizes to illustrate how these three central tendency measures yield different results and convey unique insights. Discover how the mode highlights the most common household size, the median indicates the midpoint of the population, and the mean provides an average that can be influenced by extreme values. We then examine scenarios where the mean can be misleading, such as when dealing with data entry errors or genuine outliers like extremely high incomes. Learn why the median is often preferred for skewed distributions, offering a more robust representation of typical values. Understand the crucial first step in data analysis: identifying whether an outlier is a mistake to be removed or a genuine observation to be considered when choosing your measure of central tendency. Subscribe to @AxiomTutoringCourses for more educational content.