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

# analysis example

## Line Charts: Using, Examples, and Interpreting

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

## Dot Plots: Using, Examples, and Interpreting

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

## Empirical Cumulative Distribution Function (CDF) Plots

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

## Using Excel to Calculate Correlation

Excel can calculate correlation coefficients and a variety of other statistical analyses. Even if you don’t use Excel regularly, this post is an excellent introduction to calculating and interpreting correlation.

In this post, I provide step-by-step instructions for having Excel calculate Pearson’s correlation coefficient, and I’ll show you how to interpret the results. Additionally, I include links to relevant statistical resources I’ve written that provide intuitive explanations. Together, we’ll analyze and interpret an example dataset! [Read more…] about Using Excel to Calculate Correlation

## Autocorrelation and Partial Autocorrelation in Time Series Data

Autocorrelation is the correlation between two observations at different points in a time series. For example, values that are separated by an interval might have a strong positive or negative correlation. When these correlations are present, they indicate that past values influence the current value. Analysts use the autocorrelation and partial autocorrelation functions to understand the properties of time series data, fit the appropriate models, and make forecasts. [Read more…] about Autocorrelation and Partial Autocorrelation in Time Series Data

## Using Combinations to Calculate Probabilities

Combinations in probability theory and other areas of mathematics refer to a sequence of outcomes where the order does not matter. For example, when you’re ordering a pizza, it doesn’t matter whether you order it with ham, mushrooms, and olives or olives, mushrooms, and ham. You’re getting the same pizza! [Read more…] about Using Combinations to Calculate Probabilities

## Using Permutations to Calculate Probabilities

Permutations in probability theory and other branches of mathematics refer to sequences of outcomes where the order matters. For example, 9-6-8-4 is a permutation of a four-digit PIN because the order of numbers is crucial. When calculating probabilities, it’s frequently necessary to calculate the number of possible permutations to determine an event’s probability.

In this post, I explain permutations and show how to calculate the number of permutations both with repetition and without repetition. Finally, we’ll work through a step-by-step example problem that uses permutations to calculate a probability. [Read more…] about Using Permutations to Calculate Probabilities

## Understanding Historians’ Rankings of U.S. Presidents using Regression Models

Historians rank the U.S. Presidents from best to worse using all the historical knowledge at their disposal. Frequently, groups, such as C-Span, ask these historians to rank the Presidents and average the results together to help reduce bias. The idea is to produce a set of rankings that incorporates a broad range of historians, a vast array of information, and a historical perspective. These rankings include informed assessments of each President’s effectiveness, leadership, moral authority, administrative skills, economic management, vision, and so on. [Read more…] about Understanding Historians’ Rankings of U.S. Presidents using Regression Models

## Spearman’s Correlation Explained

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

## Multiplication Rule for Calculating Probabilities

The multiplication rule in probability allows you to calculate the joint probability of multiple events occurring together using known probabilities of those events individually. There are two forms of this rule, the specific and general multiplication rules.

In this post, learn about when and how to use both the specific and general multiplication rules. Additionally, I’ll use and explain the standard notation for probabilities throughout, helping you learn how to interpret it. We’ll work through several example problems so you can see them in action. There’s even a bonus problem at the end! [Read more…] about Multiplication Rule for Calculating Probabilities

## Exponential Smoothing for Time Series Forecasting

Exponential smoothing is a forecasting method for univariate time series data. This method produces forecasts that are weighted averages of past observations where the weights of older observations exponentially decrease. Forms of exponential smoothing extend the analysis to model data with trends and seasonal components. [Read more…] about Exponential Smoothing for Time Series Forecasting

## Descriptive Statistics in Excel

Descriptive statistics summarize your dataset, painting a picture of its properties. These properties include various central tendency and variability measures, distribution properties, outlier detection, and other information. Unlike inferential statistics, descriptive statistics only describe your dataset’s characteristics and do not attempt to generalize from a sample to a population. [Read more…] about Descriptive Statistics in Excel

## Using Contingency Tables to Calculate Probabilities

Contingency tables are a great way to classify outcomes and calculate different types of probabilities. These tables contain rows and columns that display bivariate frequencies of categorical data. Analysts also refer to contingency tables as crosstabulation (cross tabs), two-way tables, and frequency tables.

Statisticians use contingency tables for a variety of reasons. I love these tables because they both organize your data and allow you to answer a diverse set of questions. In this post, I focus on using them to calculate different types of probabilities. These probabilities include joint, marginal, and conditional probabilities. [Read more…] about Using Contingency Tables to Calculate Probabilities

## How to Perform Regression Analysis using Excel

Excel can perform various statistical analyses, including regression analysis. It is a great option because nearly everyone can access Excel. This post is an excellent introduction to performing and interpreting regression analysis, even if Excel isn’t your primary statistical software package.

[Read more…] about How to Perform Regression Analysis using Excel

## Independent and Dependent Samples in Statistics

When comparing groups in your data, you can have either independent or dependent samples. The type of samples in your experimental design impacts sample size requirements, statistical power, the proper analysis, and even your study’s costs. Understanding the implications of each type of sample can help you design a better experiment. [Read more…] about Independent and Dependent Samples in Statistics

## Using Moving Averages to Smooth Time Series Data

Moving averages can smooth time series data, reveal underlying trends, and identify components for use in statistical modeling. Smoothing is the process of removing random variations that appear as coarseness in a plot of raw time series data. It reduces the noise to emphasize the signal that can contain trends and cycles. Analysts also refer to the smoothing process as filtering the data. [Read more…] about Using Moving Averages to Smooth Time Series Data

## A Tour of Survival Analysis

*Note: this is a guest post by Alexander Moreno, a Computer Science PhD student at the Georgia Institute of Technology. He blogs at www.boostedml.com*

Survival analysis is an important subfield of statistics and biostatistics. These methods involve modeling the time to a first event such as death. In this post we give a brief tour of survival analysis. We first describe the motivation for survival analysis, and then describe the hazard and survival functions. We follow this with non-parametric estimation via the Kaplan Meier estimator. Then we describe Cox’s proportional hazard model and after that Aalen’s additive model. Finally, we conclude with a brief discussion.

## Why Survival Analysis: Right Censoring

Modeling first event times is important in many applications. This could be time to death for severe health conditions or time to failure of a mechanical system. If one always observed the event time and it was guaranteed to occur, one could model the distribution directly. For instance, in the non-parametric setting, one could use the empirical cumulative distribution function to estimate the probability of death by some time. In the parametric setting one could do non-negative regression.

However, in some cases one might not observe the event time: this is generally called *right censoring*. In clinical trials with death as the event, this occurs when one of the following happens. 1) participants drop out of the study 2) the study reaches a pre-determined end time, and some participants have survived until the end 3) the study ends when a certain number of participants have died. In each case, after the surviving participants have left the study, we don’t know what happens to them. We then have the question:

*How can we model the empirical distribution or do non-negative regression when for some individuals, we only observe a lower bound on their event time?*

The above figure illustrates right censoring. For participant 1 we see when they died. Participant 2 dropped out, and we know that they survived until then, but don’t know what happened afterwards. For participant 3, we know that they survived until the pre-determined study end, but again don’t know what happened afterwards.

## The Survival Function and the Hazard

Two of the key tools in survival analysis are the survival function and the hazard. The survival function describes the probability of the event not having happened by a time *t*. The hazard describes the instantaneous rate of the first event at any time *t*.

More formally, let *t* be the event time of interest, such as the death time. Then the survival function is S(t) = P(T > t). We can also note that this is related to the cumulative distribution function:

For the hazard, the probability of the first event time being in the small interval (t,t+dt), given survival up to *t* is:

This is illustrated in the following figure.

Rearranging terms and taking limits we obtain

where f(t) is the density function of T and the second equality follows from applying Bayes theorem. By rearranging again and solving a differential equation, we can use the hazard to compute the survival function via

The key question then is how to estimate the hazard and/or survival function.

## Non-Parametric Estimation with Kaplan Meier

In non-parametric survival analysis, we want to estimate the survival function S(t) without covariates, and with censoring. If we didn’t have censoring, we could start with the empirical CDF:

This equation is a succinct representation of: how many people have died by time *t*? The survival function would then be: how many people are still alive? However, we can’t answer this question as posed when some people are censored by time *t*.

While we don’t necessarily know how many people have survived by an arbitrary time *t*, we do know how many people in the study are still at risk. We can use this instead. Partition the study time into 0 < t_{1} < . . . < t_{n-1} < t_{n}, where each t_{i} is either an event time or a censoring time for a participant. Assume that participants can only lapse at observed event times. Let Y(t) be the number of people at risk at just before time *t*. Assuming no one dies at exactly the same time (no ties), we can look at each time someone died. We say that the probability of dying at that specific time is 1/Y(t), and say that the probability of dying at any other time is 0. We can then say that the probability of surviving at any event time T_{i}, given survival at previous candidate event times is:

The probability of surviving up to a time *t* is then:

We call this [1] the Kaplan Meier estimator. Under mild assumptions, including that participants have independent and identically distributed event times and that censoring and event times are independent, this gives an estimator that is consistent. The next figure gives an example of the Kaplan Meier estimator for a simple case.

Learn more about Hazard Ratios.

### Kaplan Meier R Example

In R we can use the Surv and survfit functions from the survival package to fit a Kaplan Meier model. We can also use ggsurvplot from the survminer package to make plots. Here we will use the ovarian cancer dataset from the survival package. We will stratify based on treatment group assignment.

```
library(survminer)
library(survival)
kaplan_meier <- Surv(time = ovarian[['futime']], event = ovarian[['fustat']])
kaplan_meier_treatment<-survfit(kaplan_meier~rx,data=ovarian, type='kaplan-meier',conf.type='log')
ggsurvplot(kaplan_meier_treatment,conf.int = 'True')
```

## Semi-Parametric Regression with Cox’s Proportional Hazards Model

Kaplan Meier makes sense when we don’t have covariates, but often we want to model how some covariates affect death risk. For instance, how does one’s weight affect death risk? One way to do this is to assume that covariates have a multiplicative effect on the hazard. This leads us to Cox’s proportional hazard model, which involves the following functional form for the hazard:

The baseline hazard λ_{0}(t) describes how the average person’s risk evolves over time. The relative risk exp(β^{T}x) describes how covariates affect the hazard. In particular, a unit increase in x_{i} leads to an increase of the hazard by a factor of exp(β_{i}).

Because of the non-parametric nuisance term λ_{0}(t), it is difficult to maximize the full likelihood for β directly. Cox’s insight [2] was that the assignment probabilities given the death times contain most of the information about β, and the remaining terms contain most of the information about λ_{0}(t). The assignment probabilities give the following *partial likelihood*

We can then maximize this to get an estimator of β. In [3,4] they show that this estimator is consistent and asymptotically normal.

### Cox Proportional Hazards R Example

In R, we can use the Surv and coxph functions from the survival package. For the ovarian cancer dataset, we notice from the Kaplan Meier example that treatment is not proportional. Under a proportional hazards assumption, the curves would have the same pattern but diverge. However, instead they move apart and then move back together. Further, treatment does seem to lead to different survival patterns over shorter time horizons. We should not use it as a covariate, but we can stratify based on it. In R we can regress on age and presence of residual disease.

```
cox_fit <- coxph(Surv(futime, fustat) ~ age + ecog.ps+strata(rx), data=ovarian)
summary(cox_fit)
```

which gives the following results

```
Call:
coxph(formula = Surv(futime, fustat) ~ age + ecog.ps + strata(rx),
data = ovarian)
n= 26, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
age 0.13853 1.14858 0.04801 2.885 0.00391 **
ecog.ps -0.09670 0.90783 0.62994 -0.154 0.87800
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
age 1.1486 0.8706 1.0454 1.262
ecog.ps 0.9078 1.1015 0.2641 3.120
Concordance= 0.819 (se = 0.058 )
Likelihood ratio test= 12.71 on 2 df, p=0.002
Wald test = 8.43 on 2 df, p=0.01
Score (logrank) test = 12.24 on 2 df, p=0.002
```

this suggests that age has a significant multiplicative effect on death, and that a one year increase in age increases instantaneous risk by a factor of 1.15.

## Aalen’s Additive Model

Cox regression makes two strong assumptions: 1) that covariate effects are constant over time 2) that effects are multiplicative. Aalen’s additive model [5] relaxes the first, and replaces the second with the assumption that effects are additive. Here the hazard takes the form

As this is a linear model, we can estimate the cumulative regression functions using a least squares type procedure.

### Aalen’s Additive Model R Example

In R we can use the timereg package and the aalen function to estimate cumulative regression functions, which we can also plot.

```
library(timereg)
data(sTRACE)
# Fits Aalen model
out<-aalen(Surv(time,status==9)~age+sex+diabetes+chf+vf, sTRACE,max.time=7,n.sim=100)
summary(out)
par(mfrow=c(2,3))
plot(out)
```

This gives us

```
Additive Aalen Model
Test for nonparametric terms
Test for non-significant effects
Supremum-test of significance p-value H_0: B(t)=0
(Intercept) 7.29 0.00
age 8.63 0.00
sex 2.95 0.01
diabetes 2.31 0.24
chf 5.30 0.00
vf 2.95 0.03
Test for time invariant effects
Kolmogorov-Smirnov test
(Intercept) 0.57700
age 0.00866
sex 0.11900
diabetes 0.16200
chf 0.12900
vf 0.43500
p-value H_0:constant effect
(Intercept) 0.00
age 0.00
sex 0.18
diabetes 0.43
chf 0.06
vf 0.02
Cramer von Mises test
(Intercept) 0.875000
age 0.000179
sex 0.017700
diabetes 0.041200
chf 0.053500
vf 0.434000
p-value H_0:constant effect
(Intercept) 0.00
age 0.00
sex 0.29
diabetes 0.42
chf 0.02
vf 0.05
Call:
aalen(formula = Surv(time, status == 9) ~ age + sex + diabetes +
chf + vf, data = sTRACE, max.time = 7, n.sim = 100)
```

The results first test whether the cumulative regression functions are non-zero, and then whether the effects are constant. The plots of the cumulative regression functions are given below.

## Discussion

In this post we did a brief tour of several methods in survival analysis. We first described why right censoring requires us to develop new tools. We then described the survival function and the hazard. Next we discussed the non-parametric Kaplan Meier estimator and the semi-parametric Cox regression model. We concluded with Aalen’s additive model.

[1] Kaplan, Edward L., and Paul Meier. “Nonparametric estimation from incomplete observations.” Journal of the American statistical association 53, no. 282 (1958): 457-481.

[2] Cox, David R. “Regression models and life-tables.” In *Breakthroughs in statistics*, pp. 527-541. Springer, New York, NY, 1992.

[3] Tsiatis, Anastasios A. “A large sample study of Cox’s regression model.” *The Annals of Statistics* 9, no. 1 (1981): 93-108.

[4] Andersen, Per Kragh, and Richard David Gill. “Cox’s regression model for counting processes: a large sample study.” *The annals of statistics* (1982): 1100-1120.

[5] Aalen, Odd. “A model for nonparametric regression analysis of counting processes.” In *Mathematical statistics and probability theory*, pp. 1-25. Springer, New York, NY, 1980.

## How the Chi-Squared Test of Independence Works

Chi-squared tests of independence determine whether a relationship exists between two categorical variables. Do the values of one categorical variable depend on the value of the other categorical variable? If the two variables are independent, knowing the value of one variable provides no information about the value of the other variable.

I’ve previously written about Pearson’s chi-square test of independence using a fun Star Trek example. Are the uniform colors related to the chances of dying? You can test the notion that the infamous red shirts have a higher likelihood of dying. In that post, I focus on the purpose of the test, applied it to this example, and interpreted the results.

In this post, I’ll take a bit of a different approach. I’ll show you the nuts and bolts of how to calculate the expected values, chi-square value, and degrees of freedom. Then you’ll learn how to use the chi-squared distribution in conjunction with the degrees of freedom to calculate the p-value. [Read more…] about How the Chi-Squared Test of Independence Works

## How to Test Variances in Excel

Use a variances test to determine whether the variability of two groups differs. In this post, we’ll work through a two-sample variances test that Excel provides. Even if Excel isn’t your primary statistical software, this post provides an excellent introduction to variance tests. Excel refers to this analysis as F-Test Two-Sample for Variances. [Read more…] about How to Test Variances in Excel