Statistics with R – Beginner Level

Statistics with R – Beginner Level, available at $54.99, has an average rating of 4.4, with 46 lectures, based on 1572 reviews, and has 122673 subscribers.

You will learn about manipulate data in R (filter and sort data sets, recode and compute variables) compute statistical indicators (mean, median, mode etc.) determine skewness and kurtosis get statistical indicators by subgroups of the population build frequency tables build cross-tables create histograms and cumulative frequency charts build column charts, mean plot charts and scatterplot charts build boxplot diagrams check the normality assumption for a data series detect the outliers in a data series perform univariate analyses (one-sample t test, binomial test, chi-square test for goodness-of-fit) This course is ideal for individuals who are students or PhD candidates or academic researchers or business researchers or University teachers or anyone looking for a job in the statistical analysis field or anyone who is passionate about quantitative analysis It is particularly useful for students or PhD candidates or academic researchers or business researchers or University teachers or anyone looking for a job in the statistical analysis field or anyone who is passionate about quantitative analysis.

Enroll now: Statistics with R – Beginner Level

Summary

Title: Statistics with R – Beginner Level

Price: $54.99

Average Rating: 4.4

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

manipulate data in R (filter and sort data sets, recode and compute variables)
compute statistical indicators (mean, median, mode etc.)
determine skewness and kurtosis
get statistical indicators by subgroups of the population
build frequency tables
build cross-tables
create histograms and cumulative frequency charts
build column charts, mean plot charts and scatterplot charts
build boxplot diagrams
check the normality assumption for a data series
detect the outliers in a data series
perform univariate analyses (one-sample t test, binomial test, chi-square test for goodness-of-fit)

Who Should Attend

students
PhD candidates
academic researchers
business researchers
University teachers
anyone looking for a job in the statistical analysis field
anyone who is passionate about quantitative analysis

Target Audiences

students
PhD candidates
academic researchers
business researchers
University teachers
anyone looking for a job in the statistical analysis field
anyone who is passionate about quantitative analysis

If you want to learn how to perform the basic statistical analyses in the R program, you have come to the right place.

Now you don’t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a cross-table, how to build a scatterplot chart or how to compute a simple statistical test like the one-sample t test. Everything is here, in this course, explained visually, step by step.

So, what will you learn in this course?

First of all, you will learn how to manipulate data in R, to prepare it for the analysis: how to filter your data frame, how to recode variables and compute new variables.

Afterwards, we will take care about computing the main statistical figures in R: mean, median, standard deviation, skewness, kurtosis etc., both in the whole population and in subgroups of the population.

Then you will learn how to visualize data using tables and charts. So we will build tables and cross-tables, as well as histograms, cumulative frequency charts, column and mean plot charts, scatterplot charts and boxplot charts.

Since assumption checking is a very important part of any statistical analysis, we could not elude this topic. So we’ll learn how to check for normality and for the presence of outliers.

Finally, we will perform some basic, one-sample statistical tests and interpret the results. I’m talking about the one-sample t test, the binomial test and the chi-square test for goodness-of-fit.

So after graduating this course, you will know how to perform the essential statistical procedures in the R program. So… enroll today!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Data Manipulation in R

Lecture 1: Filtering Data Using Brackets

Lecture 2: Filtering Data With the Subset Command

Lecture 3: Filtering Data With dplyr

Lecture 4: Recoding Categorical Variables

Lecture 5: Recoding Continuous Variables

Lecture 6: Sorting Data Frames

Lecture 7: Compute New Variables

Lecture 8: R Codes File for the First Chapter

Lecture 9: Practical Exercises for the First Chapter

Chapter 3: Descriptive Statistics

Lecture 1: Using Base R to Generate Statistical Indicators

Lecture 2: Descriptive Statistics with the psych Package

Lecture 3: Descriptive Statistics with the pastecs Package

Lecture 4: Determining the Skewness and Kurtosis

Lecture 5: Computing Quantiles

Lecture 6: Determining the Mode

Lecture 7: Getting the Statistical Indicators by Group with DoBy

Lecture 8: Getting the Statistical Indicators by Group with DescribeBy

Lecture 9: Getting the Statistical Indicators by Group with stats

Lecture 10: R Codes File for the Second Chapter

Lecture 11: Practical Exercises for the Second Chapter

Chapter 4: Creating Frequency Tables and Cross Tables

Lecture 1: Frequency Tables in Base R

Lecture 2: Frequency Tables with plyr

Lecture 3: Building Cross Tables using xtabs

Lecture 4: Building Cross Tables with CrossTable

Lecture 5: R Codes File for the Third Chapter

Lecture 6: Practical Exercises for the Third Chapter

Chapter 5: Building Charts

Lecture 1: Histograms

Lecture 2: Cumulative Frequency Line Charts

Lecture 3: Column Charts

Lecture 4: Mean Plot Charts

Lecture 5: Scatterplot Charts

Lecture 6: Boxplot Charts

Lecture 7: R Codes File for the Fourth Chapter

Lecture 8: Practical Exercises for the Fourth Chapter

Chapter 6: Checking Assumptions

Lecture 1: Checking the Normality Assumption – Numerical Method

Lecture 2: Checking the Normality Assumption – Graphical Methods

Lecture 3: Detecting the Outliers

Lecture 4: R Codes File for the Fifth Chapter

Lecture 5: Practical Exercises for the Fifth Chapter

Chapter 7: Performing Univariate Analyses

Lecture 1: One-Sample T Test

Lecture 2: Binomial Test

Lecture 3: Chi-Square Test For Goodness-of-Fit

Lecture 4: R Codes File for the Sixth Chapter

Lecture 5: Practical Exercises for the Sixth Chapter

Chapter 8: Course Materials

Lecture 1: Download Links

Instructors

Bogdan Anastasiei
University Teacher and Consultant

Rating Distribution

1 stars: 28 votes
2 stars: 39 votes
3 stars: 224 votes
4 stars: 591 votes
5 stars: 690 votes

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