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Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management

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Mastering R Programming: For Data Science and Analytics

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Video Timeline

00:10	When should we use the independent two sample t-test and confidence interval in statistics & in research
00:53	How to access the help menu in R for t-test
01:04	How to visually examine the relationship between two variables in R
01:18	How to conduct an independent two-sided t-test with non-equal population variances in R using the "t.test" function
01:56	Introducing the null and alternative hypothesis, the confidence interval, and variance assumption with example
03:06	How to use the "mu" argument in two-sided t-test
03:12	How to use the "alt" argument to do a one-sided t-test
03:18	How to use the "conf" argument to change the confidence level for the t-test
03:24	How to use the "var.eq" argument to assume equal population variances for t-test
03:29	How to let R know that groups are paired or dependent using the "paired" argument
03:37	Two different ways for separating the groups in "t.test" command/function in R
04:18	How to decide if we should assume equal or non-equal variances using boxplot
04:38	How to decide if we should assume equal or non equal variances comparing the actual variances
05:02	How to test the null hypothesis "that the population variances are equal" using Levene's test using "leveneTest" function
More

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Apr 14, 2025 Last updated

Video Description: Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) for Database Management 2025 is part of Mastering R Programming: For Data Science and Analytics preparation. The notes and questions for Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) have been prepared according to the Database Management exam syllabus. Information about Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) covers all important topics for Database Management 2025 Exam. Find important definitions, questions, notes, meanings, examples, exercises and tests below for Two-Sample t Test in R: Independent Groups (R Tutorial 4.2).

Introduction of Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) in English is available as part of our Mastering R Programming: For Data Science and Analytics for Database Management & Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) in Hindi for Mastering R Programming: For Data Science and Analytics course. Download more important topics related with notes, lectures and mock test series for Database Management Exam by signing up for free.

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Video Lecture & Questions for Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management - Database Management full syllabus preparation | Free video for Database Management exam to prepare for Mastering R Programming: For Data Science and Analytics.

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	Mastering R Programming: For Data Science and Analytics 51 videos

Mastering R Programming: For Data Science and Analytics

51 videos

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Video Timeline

00:10	When should we use the independent two sample t-test and confidence interval in statistics & in research
00:53	How to access the help menu in R for t-test
01:04	How to visually examine the relationship between two variables in R
01:18	How to conduct an independent two-sided t-test with non-equal population variances in R using the "t.test" function
01:56	Introducing the null and alternative hypothesis, the confidence interval, and variance assumption with example
03:06	How to use the "mu" argument in two-sided t-test
03:12	How to use the "alt" argument to do a one-sided t-test
03:18	How to use the "conf" argument to change the confidence level for the t-test
03:24	How to use the "var.eq" argument to assume equal population variances for t-test
03:29	How to let R know that groups are paired or dependent using the "paired" argument
03:37	Two different ways for separating the groups in "t.test" command/function in R
04:18	How to decide if we should assume equal or non-equal variances using boxplot
04:38	How to decide if we should assume equal or non equal variances comparing the actual variances
05:02	How to test the null hypothesis "that the population variances are equal" using Levene's test using "leveneTest" function
More

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