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

R Programming 4.0+
  • 10 Videos | 1h 4m 8s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Course Overview
    2m 16s
    UP NEXT
  • Playable
    2. 
    Descriptive Statistics
    8m 6s
  • Locked
    3. 
    Estimating Parameters and Confidence Intervals
    8m 2s
  • Locked
    4. 
    Hypothesis Statements
    5m 24s
  • Locked
    5. 
    Null Hypothesis and Alternative Hypothesis
    9m 1s
  • Locked
    6. 
    P-values and Alpha Levels
    12m 2s
  • Locked
    7. 
    Interpreting P-values
    5m 18s
  • Locked
    8. 
    T-tests for Comparing the Means of Two Groups
    5m 19s
  • Locked
    9. 
    The ANOVA Test for Comparing the Means of Groups
    6m 16s
  • Locked
    10. 
    Course Summary
    2m 25s

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