Created by Bogdan Anastasiei | Last updated 12/2020
Duration: 2h 48m | 8 sections | 46 lectures | Video: 1920x1080, 48 KHz | 3.6 GB
Genre: eLearning | Language: English + Sub

compute statistical indicators (mean, median, mode etc.

Basic statistical analyses using the R program

manipulate data in R (filter and sort data sets, recode and compute variables)

)

detee 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)

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R and R studioknowledge of basic statistics

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!Who this course is for:studentsPhD candidatesacad researchersbusiness researchersUniversity teachersanyone looking for a job in the statistical analysis fieldanyone who is passionate about quantitative analysis




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