Introduction to Statistics with R

Name: Introduction to Statistics with R
Rating: 4.3 (15 reviews)

Statistical Analysis Made Simple: Learn the Basics in This Comprehensive Introductory Course

Created byTensor Teach

Last updated 5/2023

English

English [Auto],

What you'll learn

Define and classify data types and apply appropriate statistical measure
Apply probability concepts and understand its relation to statistical analyses
Perform hypothesis testing, calculate confidence intervals, and differentiate between a Z-statistic and T-statistic
Apply regression models in practice

Course content

3 sections • 17 lectures • 1h 54m total length

Introduction2:31
Frame the motivation for statistics, comparing descriptive and inferential statistics, and show how small random samples support population inferences using probability and exploratory data analysis with R and Python.
Common Data Types3:03
Identify the data types—numerical and categorical—and distinguish numerical data as discrete or continuous, such as test scores. Examine nominal and ordinal categories with examples like color and Likert-scale survey responses.
Probability Concepts6:38
Define probability as the number of favorable outcomes divided by the total outcomes, then apply to a three-coin flip example using the sample space, events, and a tree diagram.
Common Statistical Measures8:58
Explore mean, median, and percentile to describe data and test hypotheses. Use variance and standard deviation to measure dispersion and volatility.
Skewness & Kurtosis5:29
Explore skewness and kurtosis as measures of distribution shape, including left and right skew and the impact on mean, median, and mode, and assess normality and bell curve assumptions.
Plotting Data3:45
Plot data with RStudio and ggplot2 to visualize symmetry and distribution shape using histograms and box plots, showing normal and beta distributions for left and right skew.
Descriptive Statistics

Intro to Inferential Statistics3:57
Central Limit Theorem9:00
Explore the central limit theorem, sampling distribution of sample means, and verify via an R simulation that the mean equals mu and standard error equals sigma over sqrt n.
Hypothesis Testing7:41
explains hypothesis testing with a null and alternative hypothesis, using sample means and a p-value against a significance level within a sampling distribution to decide whether to reject the null.
Confidence Intervals8:53
Compute a 95% confidence interval around the sample mean using the standard error and a z score (1.96) to capture the population mean within the interval.
Z-stat vs. T-stat - Overview5:23
Compare the z and t statistics for hypothesis testing, outlining when to use each; z when population standard deviation is known and n > 30, t when it is not.
Understanding The Difference Between a Z-statistic & T-statistic8:17
Compare the Z statistic and T statistic, explain one-tailed and two-tailed tests, and show how the standard normal and t distributions support hypothesis testing with degrees of freedom.
T-test: An Overview5:31
Master one-sample and independent two-sample t tests for small samples. Define null and alternative hypotheses, set a significance level, compute the t statistic, and interpret the p value to conclude.
Conducting One-sample & Two-sample T-tests11:02
Learn to perform one-sample and independent two-sample t-tests in R, including defining mu naught, calculating t statistics, p-values, and interpreting results with box plots.
Inferential Statistics

Correlation6:00
Explore correlation as a measure of the strength and direction of a linear relationship using scatter plots and Pearson's correlation coefficient, and note that correlation does not imply causation.
Simple Linear Regression12:10
Explore how simple linear regression models the relationship between an explanatory x and a dependent y, yielding a line of best fit, intercept, slope, and residuals to assess assumptions.
Multiple Linear Regression5:55
Explore multiple linear regression with two explanatory variables, estimating beta coefficients and residuals to fit a plane of best fit for sales revenue in R.
Correlation & Regression

Requirements

High school math skills
Some experience working with data

Description

Introduction to Statistics with R is an introductory-level course that provides an overview of the fundamental principles and techniques of statistics using the R programming language. This course aims to provide a comprehensive overview of statistical concepts and methods as well as skills in using statistical software.

The course is segmented into three sections, Descriptive Statistics, Inferential Statistics, and Regression. In the first section, we will cover topics such as Data Types, Probability Concepts, Common Statistical Measures, Skewness & Kurtosis, and Plotting Data. In the next section on Inferential statistics, we will use our knowledge from the previous section to gain an in-depth understanding of fundamental Inferential Statistics concepts such as the Central Limit Theorem, Hypothesis Testing, Confidence Intervals, Z-statistic & T-statistic, One-sample T-tests, and Two-sample T-tests. We will then finish up the course by talking about Correlation, Simple Linear Regression, and Multiple Regression.

Most of the concepts in this course are followed up with a coding example in R. R is a programming language and a free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. It is widely used among statisticians and data miners for developing statistical software and data analysis. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible.

In conclusion, the Introduction to Statistics course is designed to give students a foundational understanding of statistics, as well as to help students become proficient in using the R programming language. This course is suitable for anyone who wants to understand statistical analysis and its applications or is looking to pursue a career in data science or machine learning. With this course, students will develop skills in statistical analysis, interpretation, and communication, making them well-prepared for further statistical education and analysis

Who this course is for:

People interested in pursuing a career in data science
Individuals seeking an introduction to statistical concepts and analysis
Professionals who need to interpret and analyze data on a regular basis
Students in a variety of fields including business, social sciences, health science, and natural sciences

Introduction to Statistics with R

What you'll learn

Explore related topics

Course content

Descriptive Statistics6 lectures • 30min

Inferential Statistics8 lectures • 1hr

Correlation & Regression3 lectures • 24min

Requirements

Description

Who this course is for: