Statistical Analysis and Modeling in R: Statistical Analysis on Your Data
R Programming
 Expert
 16 videos  2h 6m 50s
 Includes Assessment
 Earns a Badge
Hypothesis testing determines whether the educated guesses you've made about your data should be accepted or rejected. Ttests 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 handson running statistical tests on your sample data. Test assumptions made by statistical tests, run Ttests, perform ANOVA analysis, and interpret the results. Perform the onesample ttest and the onesample Ztest. Run the twosample ttest to compare data from different groups or categories and the paired samples ttest to compare data across time. When you're finished with this course, you'll have the knowhow to run and interpret statistical tests using the R programming language.
WHAT YOU WILL LEARN

Discover the key concepts covered in this courseIllustrate the assumptions made onesample ttestsPerform the onesample ttest and interpret resultsPerform variations of the onesample ttest, namely twosided, greater, and less onesample ttests, and then interpret resultsPerform the onesample ztest and interpret resultsIllustrate the assumptions made by the twosample ttestRun the twosample ttest for equal variancesRun welch's twosample ttest for unequal variances

Perform the paired samples ttestCheck the assumptions of the paired samples ttest for violationPerform the wilcoxon signedrank testIdentify the assumptions made by the anova testRun the oneway anova test and the tukey hsd testRun the twoway anova test for additive and interaction modelsSummarize the differences and use cases for parametric and nonparametric modelsSummarize the key concepts covered in this course
IN THIS COURSE

2m 13sIn 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 ttests 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 Ttest to test the average value of your sample against a known value. FREE ACCESS

5m 49sIn this video, you’ll watch a demo. In this demo, you’ll learn to run and interpret the onesample ttest 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. FREE ACCESS

10m 15sIn 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 Ttest on some artificially generated data. Then, you’ll see the purpose of your One Sample Ttest is to check whether the average age of students is a certain value. FREE ACCESS

12m 12sIn this video, you’ll watch a demo. In this demo, you’ll run the One Sample Ttest 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. FREE ACCESS

10m 43sIn this video, you’ll watch a demo. In this demo, you’ll see how to perform the ztest in R. The ztest is exactly like the ttest, and it's used in all cases where the ttest can be used. However, the assumptions made by the ztest are different from the ttest assumptions. Here, you’ll perform the onesample ztest to compare the mean of your sample against a known value. FREE ACCESS

8m 47sIn this video, you’ll watch a demo. In this demo, you'll see how to run the Two Sample Ttest in R. The Two Sample Ttest is used to compare the means of two groups or categories to see whether those averages are significantly different. The Two Sample Ttest makes certain assumptions about your data and the Ttest is valid only if these assumptions are true. This is the case for all hypothesis tests. FREE ACCESS

11m 51sIn this video, you’ll watch a demo. In this demo, you’ll run the Ttest 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. FREE ACCESS

10m 57sIn this video, you’ll watch a demo. In this demo, you'll run the Two Sample Ttest 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. FREE ACCESS

12m 10sIn this video, you’ll watch a demo. In this demo, you’ll learn to perform a ttest that works for a different scenario. This is the Paired Samples Ttest. The Paired Samples Ttest 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 pretest and a posttest with an intervention administered between the two time points. FREE ACCESS

11m 55sIn this video, you’ll watch a demo. In this demo, you'll run one more Paired Samples Ttest. 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. FREE ACCESS

3m 4sIn this video, you’ll watch a demo. In this demo, you’ll perform the Wilcoxon Singedrank Test. This is a nonparametric 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 Signedrank Test to compare the Happiness Scores for 2015 and 2016. The Wilcoxon Signedrank Test can be used when your assumption of normality doesn’t hold for the data you’re working with. FREE ACCESS

8m 35sIn 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 Ttest is it cannot be used to compare more than two categories or groups. If you use the Ttest for more than two categories, you’ll have to perform multiple pairs of Ttests which can lead to errors in your statistical analysis. Instead, you should use the ANOVA Test. FREE ACCESS

4m 44sIn 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 Oneway ANOVA analysis. The Oneway ANOVA is the statistical test you’ll use when you have just one variable to group or categorize the data. FREE ACCESS

4m 59sIn this video, you’ll watch a demo. In this demo, you’ll learn how to run the Twoway ANOVA test in R. The Twoway 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. FREE ACCESS

5m 58sIn this video, you’ll learn statistical tests can be divided into two broad categories, Parametric tests and Nonparametric 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 nonnormal distribution. Statistical tests are considered valid only if the assumptions you make about the data hold true. FREE ACCESS

2m 40sIn 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 Ttest which compares the mean of your sample with a known value. FREE ACCESS
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