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

R Programming    |    Intermediate
  • 10 videos | 1h 3m 38s
  • 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

  • 2m 13s
    In this video, you’ll learn more about your instructor and this course. In this course, you’ll learn the use of descriptive statistics to summarize data and inferential statistics, to draw conclusions about the population from which your data is drawn. You’ll learn how population parameters differ from summary statistics and the use of confidence intervals when you estimate population parameters from sample data. You’ll also learn to perform hypothesis testing on your sample data. FREE ACCESS
  • 8m 3s
    In this video, you’ll learn more about Descriptive Statistics. You’ll see Descriptive Statistics is used to describe or summarize the data available. Descriptive Statistics draws no conclusions about the data. Descriptive Statistics simply gives you an overview of the data as it exists. There are two categories of quantitative measures used to describe your data. The first is referred to as measures of central tendency. The second category is called measures of dispersion. FREE ACCESS
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    3.  Estimating Parameters and Confidence Intervals
    7m 59s
    In this video, you’ll learn more about Inferential Statistics. Inferential statistics allow you to draw inferences or conclusions about the population using patterns you’ve discovered in a sample from the population. To draw conclusions about the population, you’ll test any hypothesis you have about the population as a whole using your sample. You’ll derive estimates from the sample. This is how Inferential Statistical analysis infers the property of a population. FREE ACCESS
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    4.  Hypothesis Statements
    5m 21s
    In this video, you’ll learn more about hypothesis testing. Hypothesis testing is a common technique used in inferential statistics used to test assumptions or educated guesses made about the data to see whether the assumption is true or false. Hypothesis testing helps you decide if an educated guess is true or not. In hypothesis testing, you'll start with an educated guess made about your data. You’ll express this guess as a statistical hypothesis. FREE ACCESS
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    5.  Null Hypothesis and Alternative Hypothesis
    8m 58s
    In this video, you’ll learn more about the Null Hypothesis and Alternative Hypothesis. Hypothesis testing involves testing an effect of some kind. Hypothesis tests involve running tests on data to prove or disprove these effects. There are two possible outcomes. You'll use the sample data to assess two mutually exclusive theories about the properties of the population. When you’re conducting a statistical test, your belief can be true or false. This is mutually exclusive. FREE ACCESS
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    6.  P-values and Alpha Levels
    11m 59s
    In this video, you’ll learn more about P-values and Alpha Levels. First, you’ll learn more about the actual process of hypothesis testing once you have a sample of data to work with. The first step is to state what you’re trying to prove in a hypothesis statement. This drives the remaining steps in the process. Next, you'll run a statistical test on the sample data you’ve collected. These tests vary depending. FREE ACCESS
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    7.  Interpreting P-values
    5m 16s
    In this video, you’ll learn more about interpreting p-values. You’ll learn how to run a statistical test and interpret the p-value you get against the significance threshold alpha. You’ll run through the example onscreen. Your first step is to set up a null hypothesis and an alternative hypothesis you can then test. The next step is to choose the right statistical test to test this hypothesis. FREE ACCESS
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    8.  T-tests for Comparing the Means of Two Groups
    5m 16s
    In this video, you’ll watch a demo. In this demo you'll explore t-tests. The t-test is one of the most popular and widely used statistical tests. The t-test is the simplest test to find the difference between two groups. These groups or categories can be anything and the t-test will tell you whether the differences between them are significant. T-tests are used to compare the averages between two groups or categories. FREE ACCESS
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    9.  The ANOVA Test for Comparing the Means of Groups
    6m 13s
    In this video, you’ll learn another statistical test that is very widely used. This is the ANOVA or the analysis of variance. While t-tests are very popular and widely used, they have several drawbacks. T-tests work well for two-group comparisons. However, if you have more than two groups or categories in your data, to figure out whether those groups are significantly different, you'll need multiple pairwise t-tests. FREE ACCESS
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    10.  Course Summary
    2m 22s
    In this video, you’ll summarize what you’ve learned in this course. You’ve learned how descriptive statistics are used to compute summaries or overviews of data, by computing measures of central tendency and measures of dispersion. You saw that computing descriptive statistics on populations gives you population parameters and descriptive statistics on samples. You also learned the role of inferential statistics and saw that parameter estimates involves the specification of confidence intervals. FREE ACCESS

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