Statistics and probability are integral to any world wide web application. It can be easy to feel overwhelmed with the terminology, calculations, and seemingly endless formulas that need to be used for building a successful web app. However, each time you make a mistake or make an incorrect assumption about the best course of action, it's likely costing your team time and money. That's why it's important for every developer familiarize themselves with the basic principles of statistics and probability before embarking on any project that could potentially be impacted by their mistake or lack of knowledge.This document is an introduction into statistics based on real-life examples designed around applications related to social media analytics through Facebook & Twitter data sets. It covers the following topics:1. Installation and setup of R 2. A brief introduction to some of the most common statistical concepts such as distributions, sampling distributions, and statistical inference.3. Plotting functions, graphs, and data to display the results of critical calculations for users to visualize how they fit together for a given problem.4. Linear regression analysis: how to fit a straight line through a set of points and compute various statistics such as slope and y-intercept using the least-squares method.5. Quadratic regression analysis: how to fit a straight line through a set of points and compute various statistics such as slope, y-intercept, & x-intercept using the method of segments.6. Two sample t-test analysis of data to determine if the mean scores of two samples (X and Y) are equal or statistically different7. ANOVA analysis to determine if there is significant difference between the means of multiple samples (X, Y, Z...), where each sample is composed of multiple participants (N=2)8. One sample t-test analysis to compare the means (or medians) in multiple samples (X, Y,...). 9. Chi-squared test analysis to determine if the observed count of a variable matches a calculated probability using a χ2 test10.I'll close with some examples on how to obtain the most accurate results from calculations and figure out what you can do to improve your results. Data Sources:All data sources will be provided in the corresponding section of this document, for easy reference and comparison purposesExample 1: Would a straight line fit our data? I'll be using the pixiedust/facebook-analytics repo for our first set of calculations. Let's take a look at what we're working with ... Here is an excerpt from their code for this example: https://github. com/pixiedust/facebook-analytics/blob/master/facebookAnalytics.R# Get some metrics from Facebook, including number of fans for the last two daysresults <- facebookGet ( start_date = '2014-05-28' , end_date = '2014-05-30' ) %>%mutate ( num_fans = numberOfFans ( pageID )) Let's take a look at the output results First, I'll use dplyr library (http://www.rstudio.com/services/dplyr/) to do data manipulation and filtering. I'll be using it for all data manipulation tasks in this document.
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