# Statistical Analysis and Modeling in R: Understanding & Interpreting Statistical Tests

R Programming 4.0+
• 10 Videos | 1h 4m 8s
• Includes Assessment
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Statistical analysis involves making educated guesses known as hypotheses and testing them to see if they hold up. Use this course to learn how to apply hypothesis testing to your data. Examine the use of descriptive statistics to summarize data and inferential statistics to draw conclusions. Learn how population parameters differ from summary statistics and how confidence intervals are used. Discover how to perform hypothesis testing on sample data, construct null and alternative hypotheses, and interpret the results of your statistical tests. Investigate the significance of the p-value of a statistical test and how it can be interpreted using the significance threshold or alpha level. Additionally, examine the most commonly used statistical tests, the T-test and the analysis of variance (ANOVA). When you're done, you'll have the confidence to set up the null and alternative hypotheses for your tests and interpret the results.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course recall measures of central tendency and measures of dispersion estimate parameters of the population and interpret confidence intervals construct hypothesis statements in the context of a statistical test posit the null hypothesis and alternative hypothesis of a statistical test
• recall implications of the p-value and significance level alpha interpret p-values using significance level alpha recognize the use of t-tests to compare the means of two groups explore the ANOVA (analysis of variance) test to compare the means of two or more groups summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Descriptive Statistics
• 3.
Estimating Parameters and Confidence Intervals
• 4.
Hypothesis Statements
• 5.
Null Hypothesis and Alternative Hypothesis
• 6.
P-values and Alpha Levels
• 7.
Interpreting P-values
• 8.
T-tests for Comparing the Means of Two Groups
• 9.
The ANOVA Test for Comparing the Means of Groups
• 10.
Course Summary

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