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Markian Jaworsky has completed

Inferential Statistics

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6 hr
11,700 XP
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Course Description

This course complements the course on Inferential Statistics at Coursera.
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  1. 1

    The Basics of R

    Free

    This lab is about teaching enough R to start using it for statistical analyses.

    Play Chapter Now
    How it works
    100 xp
    Little arithmetics with R
    100 xp
    Documenting your code
    100 xp
    Variable assignment I
    100 xp
    Variable assignment II
    100 xp
    Back to Apples and Oranges
    100 xp
    Discover Basic Data Types
    100 xp
    What's that data type?
    50 xp
    Coercion: Taming your data
    100 xp
    Create a vector I
    100 xp
    Vectors II
    100 xp
    Selection by comparison I
    100 xp
    Selection by comparison II
    100 xp
    Matrices
    100 xp
    Factors
    100 xp
    Dataframes: What's a data frame?
    100 xp
    Inspecting dataframes
    100 xp
    Constructing a dataframe yourself
    100 xp
    Indexing and selecting columns from a dataframe
    100 xp
    Lists
    100 xp
    Selecting elements from a list
    100 xp
  2. 2

    Introduction to R continued

    Free

    This lab continues with an introduction to R

    Play Chapter Now
    Making a start with functions: Getting help
    100 xp
    Functions continued
    100 xp
    Functions continued II
    100 xp
    Making your own functions
    100 xp
    Getting your data into R
    100 xp
    Reading in your own data
    100 xp
    Working directories in R
    100 xp
    Changing working directories in R
    100 xp
    Checking files in your working directory
    100 xp
    Importing R packages
    100 xp
  3. 3

    Comparing two groups

    Free

    The current lab treats differences between two independent groups, paired groups and proportions

    Play Chapter Now
    Recap hypothesis testing
    50 xp
    Recap hypothesis testing (2)
    50 xp
    Comparing two proportions
    100 xp
    Comparing two proportions (2)
    100 xp
    Comparing two proportions (3)
    100 xp
    Comparing two proportions (4)
    100 xp
    Comparing two proportions (5)
    100 xp
    Comparing two proportions (6)
    50 xp
    Comparing two means
    100 xp
    Comparing two means (2)
    100 xp
    Comparing two means (3)
    100 xp
    Comparing two means (4)
    100 xp
    Comparing two means (5)
    100 xp
    Comparing two means (6)
    50 xp
    Comparing two proportions for paired samples
    50 xp
    Comparing two proportions for paired samples (2)
    100 xp
    Comparing two proportions for paired samples (3)
    100 xp
    Comparing two proportions for paired samples (4)
    100 xp
    Comparing two means for paired samples
    50 xp
    Comparing two means for paired samples (2)
    100 xp
    Comparing two means for paired samples (3)
    100 xp
    Comparing two means for paired samples (4)
    100 xp
    Simpson's paradox
    50 xp
    Simpson's paradox (2)
    100 xp
    Simpson's paradox (3)
    50 xp
  4. 4

    Categorical Association

    Free

    Investigating associations between categorical variables.

    Play Chapter Now
    Contingency Table I
    100 xp
    Conditional Proportions
    100 xp
    Interpretting Conditional Proportions per Row
    50 xp
    Marginal Probabilities
    100 xp
    Expected vs. Observed Probabilities
    50 xp
    Expected Frequencies
    100 xp
    Residuals
    100 xp
    Chi-Square I
    100 xp
    Degrees of Freedom
    50 xp
    Chi-square II
    100 xp
    Cramér’s V
    100 xp
    Standardized Residuals
    100 xp
    Chi-squared Goodness of Fit I
    50 xp
    Chi-squared Goodness of Fit II
    100 xp
    Goodness of Fit III
    50 xp
    Assumptions
    50 xp
    Fisher's Exact Test
    100 xp
  5. 5

    Simple Regression

    Free

    Predicting a response variable one predictor variable.

    Play Chapter Now
    Correlation and Regression
    50 xp
    Predictor vs. Response Variable
    100 xp
    The Regression Equation
    100 xp
    Interpretting the Regression Coefficient
    50 xp
    Calculating the Intercept
    100 xp
    Finding the Slope and Intercept Using lm()
    100 xp
    Regression in the Population
    50 xp
    Predictive Power
    100 xp
    Pitfalls in Regression I
    50 xp
    Pitfalls in Regression II
    50 xp
    Hypotheses
    100 xp
    Testing The Regression Model I
    100 xp
    Testing the Regression Model II
    50 xp
    Testing The Regression Model III
    100 xp
    Testing Assumptions I
    100 xp
    Testing Assumptions II
    100 xp
    Testing Assumptions III
    100 xp
    Testing Assumptions IV
    50 xp
    Making a Prediction
    50 xp
    Prediction Interval
    100 xp
    Confidence Interval
    100 xp
    Exponential Regression
    50 xp
  6. 6

    Multiple Regression

    Free

    Predicting a response variable with more than one predictor variable.

    Play Chapter Now
    The Regression Equation
    50 xp
    The Regression Coefficients I
    100 xp
    The Regression Coefficients II
    50 xp
    Multiple R squared I
    100 xp
    Multiple R squared II
    100 xp
    Overall Tests I
    100 xp
    Overall Tests II
    50 xp
    Individual Tests of Predictors I
    100 xp
    Individual Tests of Predictors II
    50 xp
    Individual Tests of Predictors III
    100 xp
    Assumptions I
    100 xp
    Assumptions II
    100 xp
    Assumptions III
    100 xp
    Categorical Predictors I
    100 xp
    Categorical Predictors II
    50 xp
    Regression With a Categorical Response I
    100 xp
    Regression With a Categorical Response II
    50 xp
  7. 7

    Quantitative associations: ANOVA

    Free

    This lab treats the statistical methodology of the analysis of variance

    Play Chapter Now
    Hypothesis testing and the ANOVA
    50 xp
    Anova and types of variability
    100 xp
    Calculating the between group variance
    100 xp
    Calculating within group variance
    100 xp
    Calculating the F statistic
    100 xp
    Assumptions checking analysis of variance
    100 xp
    Explaining the anova function
    100 xp
    Interpreting the output from an ANOVA
    50 xp
    Anova: Multiple comparisons
    50 xp
    Anova: Multiple comparisons (2)
    100 xp
    Two-way Anova
    50 xp
    Two-way Anova (2)
    100 xp
    Two-way Anova (3)
    50 xp
    Two-way Anova (4)
    100 xp
    Two-way Anova (5)
    50 xp
  8. 8

    Nonparametric tests

    Free

    This lab will treat testing methods that can be employed when the assumptions of other methods are not met

    Play Chapter Now
    Nonparametric methods: Sign test
    50 xp
    Nonparametric methods: sign test (2)
    100 xp
    Nonparametric methods: The Wilcoxon Signed-Rank Test
    50 xp
    Nonparametric methods: The Wilcoxon Signed-Rank Test (2)
    100 xp
    Nonparametric methods: The Wilcoxon rank-sum test
    50 xp
    Nonparametric methods: The Wilcoxon rank-sum test (2)
    100 xp
    Nonparametric methods: The Kruskall-Wallis test
    50 xp
    Nonparametric methods: The Kruskall-Wallis test (2)
    100 xp
    Nonparametric correlation
    100 xp
    Nonparametric correlation (2)
    100 xp
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