A previous post on this blog explained how to make sure footnotes are placed underneath floats (which is not a standard in LaTeX). This post is written for another trick with footnotes: resetting the footnote counter for every new page. The way this will be done is by making use of the pfnote package, although numerous alternatives exist.
Reading data from larger text files and databases in an optimal way
Loading data from the Web via parsing HTML, XML, JSON and interacting with APIs
Filtering, summarizing and restructuring data
Building and interpreting generalized linear models
Traditional multivariate statistical methods for dimension reduction and latent variables
Classification and clustering, including supervised and unsupervised statistical and machine learning methods
Handling outliers and missing values
Processing unstructured text data
A bit of social network analysis
Smoothing, seasonal decomposition and modeling time-series
Visualizing spatial data
Forecasting refers to the process of using statistical procedures to predict future values of a time series based on historical trends. For businesses, being able gauge expected outcomes for a given time period is essential for managing marketing, planning, and finances. For example, an advertising agency may want to utilizes sales forecasts to identify which future months may require increased marketing expenditures. Companies may also use forecasts to identify which sales persons met their expected targets for a fiscal quarter.
There are a number of techniques that can be utilized to generate quantitative forecasts. Some methods are fairly simple while others are more robust and incorporate exogenous factors. Regardless of what is utilized, the first step should always be to visualize the data using a line graph. You want to consider how the metric changes over time, whether there is a distinct trend, or if there are distinct patterns that are noteworthy.
There are several key concepts that we should be cognizant of when describing time series data. These characteristics will inform how we pre-process the data and select the appropriate modeling technique and parameters. Ultimately, the goal is to simplify the patterns in the historical data by removing known sources of variatiion and making the patterns more consistent across the entire data set. Simpler patterns will generally lead to more accurate forecasts.
Trend: A trend exists when there is a long-term increase or decrease in the data.
Seasonality: A seasonal pattern occurs when a time series is affected by seasonal factors such as the time of the year or the day of the week.
Autocorrelation: Refers to the pheneomena whereby values of Y at time t are impacted by previous values of Y at t-i. To find the proper lag structure and the nature of auto correlated values in your data, use the autocorrelation function plot.
Stationary: A time series is said to be stationary if there is no systematic trend, no systematic change in variance, and if strictly periodic variations or seasonality do not exist
Quantitative forecasting techniques are usually based on reression analysis or time series techniques. Regression approaches examine the relationship between the forecasted variable and other explanatory variables using cross-sectional data. Time series models use hitorical data that’s been collected at regular intervals over time for the target variablle to forecast its future values. There isn’t time to cover the theory behind each of these approaches in this post, so I’ve chosen to cover high level concepts and provide code for performing time series forecasting in R. I strongly suggest understandig the statistical theory behind a technique before running the code.
Quantitative forecasting techniques are usually based on reression analysis or time series techniques. Regression approaches examine the relationship between the forecasted variable and other explanatory variables using cross-sectional data. Time series models use hitorical data that’s been collected at regular intervals over time for the target variablle to forecast its future values. There isn’t time to cover the theory behind each of these approaches in this post, so I’ve chosen to cover high level concepts and provide code for performing time series forecasting in R. I strongly suggest understandig the statistical theory behind a technique before running the code.
First, we can use the ma function in the forecast package to perform forecasting using the moving average method. This technique estimates future values at time t by averaging values of the time series within k periods of t. When the time series is stationary, the moving average can be very effective as the observations are nearby across time.
A majority of Americans have a casual sexual experience before transitioning to adulthood. Little research has yet to examine how identity influences causal sexual behavior. The current study fills this gap in the literature by examining if subjective adult identity predicts casual sexual behavior net of life course transitions in a national sample of Americans. To answer this research question, the Longitudinal Study of Adolescent to Adult Health is utilized. Structural equation modeling results show the older and more adult-like individuals feel the less likely they are to report a recent casual sexual partner. Once life course factors are included in the model, subjective identity is no longer associated with casual sex. Practitioners who work with adult populations need to consider how life course transitions influence casual sexual behavior.
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