Statistical Analysis and Modeling in R: Statistical Analysis on Your Data
R Programming 4.0+
| 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. 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
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discover the key concepts covered in this courseillustrate the assumptions made one-sample t-testsperform the one-sample t-test and interpret resultsperform variations of the one-sample t-test, namely two-sided, greater, and less one-sample t-tests, and then interpret resultsperform the one-sample Z-test and interpret resultsillustrate the assumptions made by the two-sample t-testrun the two-sample t-test for equal variancesrun Welch's two-sample t-test for unequal variances
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perform the paired samples t-testcheck the assumptions of the paired samples t-test for violationperform the Wilcoxon signed-rank testidentify the assumptions made by the ANOVA testrun the one-way ANOVA test and the Tukey HSD testrun the two-way ANOVA test for additive and interaction modelssummarize the differences and use cases for parametric and non-parametric modelssummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview2m 13sUP NEXT
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2.Identifying One-sample T-test Assumptions5m 49s
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3.Performing the One-sample T-test in R10m 15s
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4.Performing Variations of the One-sample T-test in R12m 12s
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5.Performing the One-sample Z-test in R10m 43s
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6.Identifying Assumptions of the Two-sample T-test8m 47s
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7.Running Two-sample T-tests for Equal Variances in R11m 51s
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8.Using Welch's two-sample T-test for Unequal Variance10m 57s
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9.Using R to Perform the Paired Samples T-test12m 10s
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10.Checking Paired Samples T-test Assumptions Using R11m 55s
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11.Performing the Wilcoxon Signed-rank Test Using R3m 4s
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12.Identifying Assumptions of the ANOVA Test Using R8m 35s
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13.Running the One-way ANOVA and Tukey HSD Tests in R4m 44s
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14.Running the Two-way ANOVA Test for Different Models4m 59s
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15.Parametric vs. Non-parametric Tests5m 58s
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16.Course Summary2m 40s
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