# Statistical Analysis and Modeling in R: Statistical Analysis on Your Data

R Programming    |    Expert
• 16 videos | 2h 6m 50s
• Includes Assessment
• Earns a Badge
Rating 4.6 of 34 users (34)
Hypothesis testing determines whether the educated guesses you've made about your data should be accepted or rejected. T-tests and ANOVA tests are some of the most commonly used methods in hypothesis testing. Knowing how to perform and interpret these tests are core data scientist skills. In this course, get hands-on running statistical tests on your sample data. Test assumptions made by statistical tests, run T-tests, perform ANOVA analysis, and interpret the results. Perform the one-sample t-test and the one-sample Z-test. Run the two-sample t-test to compare data from different groups or categories and the paired samples t-test to compare data across time. When you're finished with this course, you'll have the know-how to run and interpret statistical tests using the R programming language.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Illustrate the assumptions made one-sample t-tests
Perform the one-sample t-test and interpret results
Perform variations of the one-sample t-test, namely two-sided, greater, and less one-sample t-tests, and then interpret results
Perform the one-sample z-test and interpret results
Illustrate the assumptions made by the two-sample t-test
Run the two-sample t-test for equal variances
Run welch's two-sample t-test for unequal variances
• Perform the paired samples t-test
Check the assumptions of the paired samples t-test for violation
Perform the wilcoxon signed-rank test
Identify the assumptions made by the anova test
Run the one-way anova test and the tukey hsd test
Run the two-way anova test for additive and interaction models
Summarize the differences and use cases for parametric and non-parametric models
Summarize the key concepts covered in this course

## IN THIS COURSE

• In this video, you’ll learn more about the course and your instructor. In this course, you’ll gain experience running statistical tests on your sample data before you run t-tests or perform ANOVA analysis. You’ll test your data to see whether the assumptions made by the statistical tests are met. Then, you’ll perform a sample T-test to test the average value of your sample against a known value.
• In this video, you’ll watch a demo. In this demo, you’ll learn to run and interpret the one-sample t-test in R. First, you’ll paste in some datasets you’ll for the demo. You’ll start in your current working directory, called Skillsoft/R. Next, you’ll look at the directory structure at the bottom of your screen. Within this directory, there’s a subdirectory named code and this is the folder that contains all of your .R script files.
• 3.  Performing the One-sample T-test in R
In this video, you’ll watch a demo. In this demo, you’ll invoke rm(list = ls()). That gets rid of all of the objects that currently exist in your R memory. With this done, you'll first run the One Sample T-test on some artificially generated data. Then, you’ll see the purpose of your One Sample T-test is to check whether the average age of students is a certain value.
• 4.  Performing Variations of the One-sample T-test in R
In this video, you’ll watch a demo. In this demo, you’ll run the One Sample T-test on real world data, rather than working with artificially generated data. You’ll open up the datasets folder within R studio and click on the file, Mall_Customers.csv. You’ll right click and choose the View File option. This will allow you to view the contents of this file within R studio.
• 5.  Performing the One-sample Z-test in R
In this video, you’ll watch a demo. In this demo, you’ll see how to perform the z-test in R. The z-test is exactly like the t-test, and it's used in all cases where the t-test can be used. However, the assumptions made by the z-test are different from the t-test assumptions. Here, you’ll perform the one-sample z-test to compare the mean of your sample against a known value.
• 6.  Identifying Assumptions of the Two-sample T-test
In this video, you’ll watch a demo. In this demo, you'll see how to run the Two Sample T-test in R. The Two Sample T-test is used to compare the means of two groups or categories to see whether those averages are significantly different. The Two Sample T-test makes certain assumptions about your data and the T-test is valid only if these assumptions are true. This is the case for all hypothesis tests.
• 7.  Running Two-sample T-tests for Equal Variances in R
In this video, you’ll watch a demo. In this demo, you’ll run the T-test to check to see whether the SpendingScores of males and females are significantly different. First, you’ll need to understand more details about your data. You’ll invoke the table function on the Gender column to see how many records you have for Males and Females amongst your mall customers.
• 8.  Using Welch's two-sample T-test for Unequal Variance
In this video, you’ll watch a demo. In this demo, you'll run the Two Sample T-test once again, but you'll work with a different dataset. The data you’ll work with here is in the file called CarPrice_Assignment.csv. You’ll select and right click on that file to view its contents. This is the data you’ll work with. You’ll see the records contain a bunch of information about different cars.
• 9.  Using R to Perform the Paired Samples T-test
In this video, you’ll watch a demo. In this demo, you’ll learn to perform a t-test that works for a different scenario. This is the Paired Samples T-test. The Paired Samples T-test compares two means or averages from the same set of individuals, objects, or related units, measured at different times. This is a measurement taken at two different times, like a pre-test and a post-test with an intervention administered between the two time points.
• 10.  Checking Paired Samples T-test Assumptions Using R
In this video, you’ll watch a demo. In this demo, you'll run one more Paired Samples T-test. This time, you’ll work with real world data. The dataset you'll use will be the happiness score dataset. Onscreen, you have happiness scores for 2015 and 2016. You’ll see if these are significantly different. First, you’ll click on the 2015.csv file and choose the View File option.
• 11.  Performing the Wilcoxon Signed-rank Test Using R
In this video, you’ll watch a demo. In this demo, you’ll perform the Wilcoxon Singed-rank Test. This is a non-parametric test used to compare two related or matched samples. The differences between the matched pairs aren’t normally distributed, so you'll rely on the Wilcoxon Signed-rank Test to compare the Happiness Scores for 2015 and 2016. The Wilcoxon Signed-rank Test can be used when your assumption of normality doesn’t hold for the data you’re working with.
• 12.  Identifying Assumptions of the ANOVA Test Using R
In this video, you’ll watch a demo. In this demo, you’ll identify assumptions of the ANOVA test using R. You’ll learn one drawback of the Two Sample T-test is it cannot be used to compare more than two categories or groups. If you use the T-test for more than two categories, you’ll have to perform multiple pairs of T-tests which can lead to errors in your statistical analysis. Instead, you should use the ANOVA Test.
• 13.  Running the One-way ANOVA and Tukey HSD Tests in R
In this video, you’ll watch a demo. In this demo, you’ll see all of ANOVA's assumptions have been met. You’ll run the ANOVA test on this data. You'll use the aov function to run the One-way ANOVA analysis. The One-way ANOVA is the statistical test you’ll use when you have just one variable to group or categorize the data.
• 14.  Running the Two-way ANOVA Test for Different Models
In this video, you’ll watch a demo. In this demo, you’ll learn how to run the Two-way ANOVA test in R. The Two-way ANOVA is a statistical test that uses two variables to group or categorize data. So in this demo, rather than considering only the degree of the loan applicants, you’ll also consider the degree as well as the loan type.
• 15.  Parametric vs. Non-parametric Tests
In this video, you’ll learn statistical tests can be divided into two broad categories, Parametric tests and Non-parametric tests. The difference in the statistical tests arises from the methodologies they follow. These refer to methodologies in which the data you work with has a normal distribution vs. a non-normal distribution. Statistical tests are considered valid only if the assumptions you make about the data hold true.
• 16.  Course Summary
In this video, you’ll summarize what you’ve learned in the course. In this course, you used real datasets, ran statistical tests, and interpreted their results. You saw that every statistical test makes its own assumptions about the underlying data and the test is valid only when these assumptions are true for your dataset. You also ran the One Sample T-test which compares the mean of your sample with a known value.

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