Applied Statistics using R with Data Processing

Getting started with data processing in R and Python, installing the necessary tools, and configuring the environment for statistical analysis.

Get started with data processing in r by configuring the environment, using the console and debugging tools, and understanding how programming interacts with the compiler.

Begin applying statistics with R and data processing as you get started with this course's first steps. Explore data, tools, and version options to set up your analysis workflow.

Learn to create a hello world application and run basic code in a programming language, gaining a first exposure to writing and executing simple programs.

Explore the data mining process from business understanding to deployment, including data understanding, preparation, modeling, evaluation, and reporting, with emphasis on domain experts, regression, correlation, and visualization.

Download the iris dataset previously used in this course, and learn how to locate and download a high risk dataset for statistical learning with R and data processing.

Read dataset and apply descriptive statistics to generate summary data, demonstrating how to run analyses and manage data structure for practical data processing.

Explore central tendency and descriptive statistics in R by calculating the mode, mean, and median, and building frequency tables to analyze data patterns.

Find the median from a dataset in R using data to illustrate median calculation in a programming language.

Compute the mean from data using programming and apply mean calculations within data processing in applied statistics with R.

Apply descriptive statistics to analyze data spread by examining central tendency and calculating the range as the difference between the largest and smallest values, compare with the mean or median.

Master how to compute a data range and the difference between the mean and maximum, using functions for large data sets in R during data processing.

Select and clean the dataset by choosing an attribute, removing duplicate values, and compute the range with a window, using seven point nine minus four point three as an example.

Learn to compute the interquartile range (IQR) in R using the IQR function, comparing the 75th and 25th percentiles to quantify data spread.

Master quantiles by exploring quartiles and percentiles (25%, 50%, 75%), and apply the quintile function to summarize data distribution in R for data processing.

Explore population variance in applied statistics using data processing in R, and follow steps involving N and N minus one to compute variance from data.

Explore the difference between population and sample variance, and learn to compute each with R functions, using the n-1 adjustment for sample variance.

Learn to compute population and sample variance in R using the n-1 adjustment, and apply the standard deviation formula with data processing steps.

Compute population standard deviation from a dataset by using variance formulas and the sd function in R; illustrate with copying data and applying the standard deviation method.

Explore the normal distribution, its mean and standard deviation, visualize with histograms, and assess normality in data using the Shapiro-Wilk test and p-values in an R workflow.

Explore skewness and kurtosis in applied statistics using R, employing the moments package to analyze heavy-tailed distributions and perform data processing tasks.

Explore summary() and str() in R to inspect data structures and compute descriptive statistics, including mean, median, and quartiles, across data columns for efficient data processing.

Explore how to assess linear relationships with correlation in R, connect regression concepts to inferential statistics, and use data generation, normalization, and correlation functions.

Explore how covariance measures variability between two variables and how to compute it with the covariance function. See a covariance value of -0.04372107 in the example.

Explore inferential statistics by formulating null and alternative hypotheses, computing p-values, and applying t-tests for continuous data and tests for categorical data to determine if means differ.

Explore one sample t tests, including assumptions of random sampling and a normally distributed population. Formulate hypotheses, compute p values, and interpret type I and II errors in R.

Perform a two-sample unpaired t test on independent samples in r to compare means, compute the p value, and decide whether to reject the null for a nonzero difference.

Compare two independent data sets using a two-sample unpaired t test with unequal variances, test mu1 minus mu2 equals zero, and reject the null when the p-value is extremely small.

Evaluate the mean difference for paired data with a two-sample paired t test. Test the null of zero difference against the alternative of nonzero; reject if p-value is below alpha.

Apply the chi-square test to assess whether two categorical variables are related. The null hypothesis claims independence, and the p-value decides if we reject it and infer a relationship.

learn to perform one-way ANOVA to test whether multiple group means differ, interpret the p value from the summary output, and decide on the null hypothesis at 95 percent confidence.

Perform a two-way analysis of variance in R to examine how two factors affect a response, compute p-values and F statistics, and test null hypotheses at 95 percent confidence.

Manova analyzes multiple response variables to test mean differences across groups using null and alternative hypotheses, p values, and 95% confidence intervals, with iris data examples.

Apply simple linear regression in R to model the relationship between a dependent and an independent variable as a predictive model, using a linear model, coefficients, p-values, and confidence intervals.

Explore multiple linear regression with two input variables, modeling Y as a function of X and S2, and interpret coefficients, intercept, p-values, and r-squared to assess significance and fit.

Master how to select variables in R for data processing, cleaning and visualization, using vectors and data frames, handling missing values, and preparing data for statistical analysis.

Sort data in R using the order function, toggle descending with decreasing = TRUE, and sort by multiple keys for data processing.

Apply data filtering to select data based on conditions like values greater than zero and less than one, using compact formulas in R.

Learn to remove missing values and duplicates in R, and perform basic data cleaning to prepare datasets for analysis.