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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 donâ€™t have to scour the web endlessly in order to find how to compute the statistical indicators in R, how to build a crosstable, how to build a scatterplot chart or how to compute a simple statistical test like the onesample 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 crosstables, 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 weâ€™ll learn how to check for normality and for the presence of outliers.
Finally, we will perform some basic, onesample statistical tests and interpret the results. Iâ€™m talking about the onesample t test, the binomial test and the chisquare test for goodnessoffit.
So after graduating this course, you will know how to perform the essential statistical procedures in the R program. Soâ€¦ enroll today!
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Section 1: Introduction  

Lecture 1 
Introduction
Preview

05:45  
Section 2: Data Manipulation in R  
Lecture 2  07:56  
How to filter your data frames with brackets (in base R). 

Lecture 3  05:07  
How to filter your data frames using subsets. 

Lecture 4  04:03  
How to filter your data set using the dplyr package 

Lecture 5  05:46  
How to recode categorical variables in R 

Lecture 6  05:04  
How to recode continuous variables in R 

Lecture 7  04:10  
How to sort data sets using various criteria 

Lecture 8  01:52  
How to compute new variables based on the existing ones 

Lecture 9  00:06  
All the codes used in the lectures 28, for your reference 

Lecture 10  00:06  
Practical exercises for the lectures 28 

Section 3: Descriptive Statistics  
Lecture 11  03:36  
How to compute the statistical indicators (mean, median, standard deviation etc.) in base R 

Lecture 12  03:42  
How to compute statistical indicators with the psych package 

Lecture 13  04:56  
How to compute statistical indicators using the pastecs package 

Lecture 14  01:35  
How to compute skewness and kurtosis in R 

Lecture 15  02:15  
How to detemine the quantiles of a distribution 

Lecture 16  01:29  
How to compute the mode of a distribution 

Lecture 17  05:12  
How to compute the statistical indicators by groups using the DoBy package 

Lecture 18  02:42  
How to compute the statistical indicators with the DescribeBy package 

Lecture 19  04:29  
How to compute the statistical indicators with the stats package 

Lecture 20  00:06  
All the codes used in the lectures 1119, for your reference 

Lecture 21  00:06  
Practical exercises for the lectures 1119 

Section 4: Creating Frequency Tables and Cross Tables  
Lecture 22  06:40  
How to build frequency tables 

Lecture 23  05:00  
How to build frequency tables using the package plyr 

Lecture 24  01:24  
Creating crosstables with the xtabs command 

Lecture 25  04:20  
Creating crosstables with the CrossTable command 

Lecture 26  00:06  
All the codes used in the lectures 2225, for your reference  
Lecture 27  00:06  
Practical exercises for the lectures 2225 

Section 5: Building Charts  
Lecture 28  07:09  
How to create a histogram for your distribution 

Lecture 29  11:20  
How to create cumulative frequency line charts 

Lecture 30  05:36  
How to build column charts 

Lecture 31  13:57  
How to build mean plot charts 

Lecture 32  11:22  
How to build scatterplot charts 

Lecture 33  06:02  
How to build boxplot charts 

Lecture 34  00:06  
All the codes used in the lectures 2833, for your reference  
Lecture 35  00:06  
Practical exercises for the lectures 2833 

Section 6: Checking Assumptions  
Lecture 36  02:22  
How to check for normality using numerical methods 

Lecture 37  03:36  
How to check for normality using graphical methods 

Lecture 38  02:05  
How to detect the extreme values in your data series 

Lecture 39  00:06  
All the codes used in the lectures 3638, for your reference 

Lecture 40  00:06  
Practical exercises for the lectures 3638 

Section 7: Performing Univariate Analyses  
Lecture 41  03:41  
How to run and interpret the onesample t test 

Lecture 42  06:07  
How to run and interpret the binomial test 

Lecture 43  07:08  
How to perform the chisquare test for goodnessoffit 

Lecture 44  00:06  
All the codes used in the lectures 4143, for your reference 

Lecture 45  00:06  
Practical exercises for the lectures 4143 

Section 8: Course Materials  
Lecture 46  00:03  
Here you can download the csv files and the R files. 
My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have about 20 years experience in teaching and about 10 years experience in business consulting.