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Statistics By Jim

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data types

Covariates: Definition & Uses

By Jim Frost 2 Comments

What is a Covariate?

Covariates are continuous independent variables (or predictors) in a regression or ANOVA model. These variables can explain some of the variability in the dependent variable.

That definition of covariates is simple enough. However, the usage of the term has changed over time. Consequently, analysts can have drastically different contexts in mind when discussing covariates. [Read more…] about Covariates: Definition & Uses

Filed Under: ANOVA Tagged With: conceptual, data types

Qualitative vs Quantitative Data Differences

By Jim Frost 2 Comments

Qualitative vs quantitative data is a fundamental distinction between two types of information you can gather and analyze statistically. These types of variables seem diametrically opposed, but effective research projects will use them together.

In this post, I’ll explain the difference between qualitative and quantitative data and show effective ways to graph and analyze them for your research. [Read more…] about Qualitative vs Quantitative Data Differences

Filed Under: Basics Tagged With: conceptual, data types

Nominal Data: Definition & Examples

By Jim Frost Leave a Comment

What is Nominal Data?

Nominal data are divided into mutually exclusive categories that do not have a natural order, nor do they provide any quantitative information. The definition of nominal in statistics is “in name only.” This definition indicates how these data consist of category names—all you can do is name the group to which each observation belongs. Nominal and categorical data are synonyms, and I’ll use them interchangeably.

For example, literary genre is a nominal variable that can have the following categories: science fiction, drama, and comedy. [Read more…] about Nominal Data: Definition & Examples

Filed Under: Basics Tagged With: conceptual, data types

Ordinal Data: Definition, Examples & Analysis

By Jim Frost 4 Comments

What is Ordinal Data?

Ordinal data have at least three categories that have a natural rank order. The categories are ranked, but the differences between ranks may not be equal. These data indicate the order of values but not the degree of difference between them. For example, first, second, and third places in a race are ordinal data. You can clearly understand the order of finishes. However, the time difference between first and second place might not be the same as between second and third place. [Read more…] about Ordinal Data: Definition, Examples & Analysis

Filed Under: Basics Tagged With: data types

Nominal, Ordinal, Interval, and Ratio Scales

By Jim Frost 6 Comments

The nominal, ordinal, interval, and ratio scales are levels of measurement in statistics. These scales are broad classifications describing the type of information recorded within the values of your variables. Variables take on different values in your data set. For example, you can measure height, gender, and class ranking. Each of these variables uses a distinct level of measurement. [Read more…] about Nominal, Ordinal, Interval, and Ratio Scales

Filed Under: Basics Tagged With: conceptual, data types

Discrete vs. Continuous Data: Differences & Examples

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Discrete vs continuous data are two broad categories of numeric variables. Numeric variables represent characteristics that you can express as numbers rather than descriptive language.

When you have a numeric variable, you need to determine whether it is discrete or continuous.

In broad strokes, the critical factor is the following:

  • You count discrete data.
  • You measure continuous data.

[Read more…] about Discrete vs. Continuous Data: Differences & Examples

Filed Under: Basics Tagged With: data types

Pareto Chart: Making, Reading & Examples

By Jim Frost Leave a Comment

What is a Pareto Chart?

A Pareto chart is a specialized bar chart that displays categories in descending order and a line chart representing the cumulative amount. The chart effectively communicates the categories that contribute the most to the total. Frequently, quality analysts use Pareto charts to identify the most common types of defects or other problems.

Learn how to use and read Pareto charts and understand the Pareto principle and the 80/20 rule that are behind it. I’ll also show you how to create them using Excel. [Read more…] about Pareto Chart: Making, Reading & Examples

Filed Under: Graphs Tagged With: choosing analysis, data types, interpreting results, quality improvement

Scatterplots: Using, Examples, and Interpreting

By Jim Frost 4 Comments

Use scatterplots to show relationships between pairs of continuous variables. These graphs display symbols at the X, Y coordinates of the data points for the paired variables. Scatterplots are also known as scattergrams and scatter charts. [Read more…] about Scatterplots: Using, Examples, and Interpreting

Filed Under: Graphs Tagged With: analysis example, choosing analysis, data types, interpreting results

Pie Charts: Using, Examples, and Interpreting

By Jim Frost Leave a Comment

Use pie charts to compare the sizes of categories to the entire dataset. To create a pie chart, you must have a categorical variable that divides your data into groups. These graphs consist of a circle (i.e., the pie) with slices representing subgroups. The size of each slice is proportional to the relative size of each category out of the whole. [Read more…] about Pie Charts: Using, Examples, and Interpreting

Filed Under: Graphs Tagged With: analysis example, choosing analysis, data types, interpreting results

Bar Charts: Using, Examples, and Interpreting

By Jim Frost 4 Comments

Use bar charts to compare categories when you have at least one categorical or discrete variable. Each bar represents a summary value for one discrete level, where longer bars indicate higher values. Types of summary values include counts, sums, means, and standard deviations. Bar charts are also known as bar graphs. [Read more…] about Bar Charts: Using, Examples, and Interpreting

Filed Under: Graphs Tagged With: analysis example, choosing analysis, data types, interpreting results

Line Charts: Using, Examples, and Interpreting

By Jim Frost 2 Comments

Use line charts to display a series of data points that are connected by lines. Analysts use line charts to emphasize changes in a metric on the vertical Y-axis by another variable on the horizontal X-axis. Often, the X-axis reflects time, but not always. Line charts are also known as line plots. [Read more…] about Line Charts: Using, Examples, and Interpreting

Filed Under: Graphs Tagged With: analysis example, choosing analysis, data types, interpreting results

Dot Plots: Using, Examples, and Interpreting

By Jim Frost Leave a Comment

Use dot plots to display the distribution of your sample data when you have continuous variables. These graphs stack dots along the horizontal X-axis to represent the frequencies of different values. More dots indicate greater frequency. Each dot represents a set number of observations. [Read more…] about Dot Plots: Using, Examples, and Interpreting

Filed Under: Graphs Tagged With: analysis example, choosing analysis, data types, distributions, interpreting results

Empirical Cumulative Distribution Function (CDF) Plots

By Jim Frost Leave a Comment

Use an empirical cumulative distribution function plot to display the data points in your sample from lowest to highest against their percentiles. These graphs require continuous variables and allow you to derive percentiles and other distribution properties. This function is also known as the empirical CDF or ECDF. [Read more…] about Empirical Cumulative Distribution Function (CDF) Plots

Filed Under: Graphs Tagged With: analysis example, choosing analysis, data types, interpreting results

Contour Plots: Using, Examples, and Interpreting

By Jim Frost 1 Comment

Use contour plots to display the relationship between two independent variables and a dependent variable. The graph shows values of the Z variable for combinations of the X and Y variables. The X and Y values are displayed along the X and Y-axes, while contour lines and bands represent the Z value. The contour lines connect combinations of the X and Y variables that produce equal values of Z. [Read more…] about Contour Plots: Using, Examples, and Interpreting

Filed Under: Graphs Tagged With: choosing analysis, data types, interpreting results

Spearman’s Correlation Explained

By Jim Frost 45 Comments

Spearman’s correlation in statistics is a nonparametric alternative to Pearson’s correlation. Use Spearman’s correlation for data that follow curvilinear, monotonic relationships and for ordinal data. Statisticians also refer to Spearman’s rank order correlation coefficient as Spearman’s ρ (rho).

In this post, I’ll cover what all that means so you know when and why you should use Spearman’s correlation instead of the more common Pearson’s correlation. [Read more…] about Spearman’s Correlation Explained

Filed Under: Basics Tagged With: analysis example, choosing analysis, conceptual, data types, Excel, graphs

Time Series Analysis Introduction

By Jim Frost 28 Comments

Time series analysis tracks characteristics of a process at regular time intervals. It’s a fundamental method for understanding how a metric changes over time and forecasting future values. Analysts use time series methods in a wide variety of contexts. [Read more…] about Time Series Analysis Introduction

Filed Under: Time Series Tagged With: conceptual, data types, graphs

Using Histograms to Understand Your Data

By Jim Frost 23 Comments

Histograms are graphs that display the distribution of your continuous data. They are fantastic exploratory tools because they reveal properties about your sample data in ways that summary statistics cannot. For instance, while the mean and standard deviation can numerically summarize your data, histograms bring your sample data to life.

In this blog post, I’ll show you how histograms reveal the shape of the distribution, its central tendency, and the spread of values in your sample data. You’ll also learn how to identify outliers, how histograms relate to probability distribution functions, and why you might need to use hypothesis tests with them.
[Read more…] about Using Histograms to Understand Your Data

Filed Under: Basics Tagged With: choosing analysis, data types, graphs

Boxplots vs. Individual Value Plots: Comparing Groups

By Jim Frost 27 Comments

Use boxplots and individual value plots when you have a categorical grouping variable and a continuous outcome variable. The levels of the categorical variables form the groups in your data, and the researchers measure the continuous variable. Both types of charts help you compare distributions of measurements between the groups. Boxplots are also known as box and whisker plots. [Read more…] about Boxplots vs. Individual Value Plots: Comparing Groups

Filed Under: Basics Tagged With: choosing analysis, data types, graphs

Probability Distribution: Definition & Calculations

By Jim Frost 73 Comments

What is a Probability Distribution?

A probability distribution is a statistical function that describes the likelihood of obtaining all possible values that a random variable can take. In other words, the values of the variable vary based on the underlying probability distribution. Typically, analysts display probability distributions in graphs and tables. There are equations to calculate probability distributions.

Suppose you draw a random sample and measure the heights of the subjects. As you measure heights, you create a distribution of heights. This type of distribution is useful when you need to know which outcomes are most likely, the spread of potential values, and the likelihood of different results.

In this blog post, you’ll learn about probability distributions for both discrete and continuous variables. I’ll show you how they work and examples of how to use them. [Read more…] about Probability Distribution: Definition & Calculations

Filed Under: Basics Tagged With: conceptual, data types, distributions, graphs, interpreting results, probability

Guide to Data Types and How to Graph Them in Statistics

By Jim Frost 34 Comments

In the field of statistics, data are vital. Data are the information that you collect to learn, draw conclusions, and test hypotheses. After all, statistics is the science of learning from data. However, there are different types of variables, and they record various kinds of information. Crucially, the type of information determines what you can learn from it, and, importantly, what you cannot learn from it. Consequently, it’s essential that you understand the different types of data. [Read more…] about Guide to Data Types and How to Graph Them in Statistics

Filed Under: Basics Tagged With: data types, graphs

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