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

IN THIS COURSE

• 1.  Course Overview
  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
• 2.  Descriptive Statistics
  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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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