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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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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
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Nov 24, 2024 Last updated
Video Description: Two-Sample t Test in R: Independent Groups (R Tutorial 4.2) for Database Management 2024 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 2024 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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51 videos
Join Course for Free
Video Timeline
Video Timeline
arrow
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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