Final Exam: Statistical Analysis and Modeling in R

R Programming 4.0+
  • 1 Video | 35s
  • Includes Assessment
  • Earns a Badge
Final Exam: Statistical Analysis and Modeling in R will test your knowledge and application of the topics presented throughout the Statistical Analysis and Modeling in R track of the Skillsoft Aspire Data Analysis with R Journey.

WHAT YOU WILL LEARN

  • analyze data that follows a uniform distribution
    check the assumptions of the paired samples t-test
    compare and contrast population metrics with sample metrics
    construct hypothesis statements in the context of a statistical test
    describe the bias-variance trade-off
    estimate parameters of the population and interpret confidence intervals
    examine and interpret the data for regression
    examine and visualize data for regression
    explore and pre-process data before model fitting
    explore and visualize the relationships in data
    find the optimal number of clusters using the elbow method and Silhouette score
    fit and interpret the S-curve of logistic regression
    fit a straight line on data to build a regression model and evaluate the model
    implement the one-sample t-test and interpret results
    interpret QQ plots for normally and non-normally distributed data
    investigate and visualize data before fitting a model
    outline the main characteristics of ensemble learning
    perform regression using decision trees
    perform regression using random forest
    perform simple linear regression with a single predictor
  • perform the one-sample t-test and interpret results
    perform the Wilcoxon signed-rank test
    posit the null hypothesis and alternative hypothesis of a statistical test
    recall characteristics of overfitted and underfitted models
    recall implications of the p-value and significance level alpha
    recall measures of central tendency and measures of dispersion
    recall the assumptions made by the ANOVA test
    recall the assumptions made by the one-sample t-test
    recall the assumptions made by the two-sample t-test
    recall the basic characteristics of machine learning models
    recall the basic structure of decision tree models
    recall the characteristics of discrete and continuous probability distributions
    recall the key metrics to evaluate classifiers
    recall the sets of statistical tools used to understand data
    recall the techniques used to evaluate clustering models
    sample and analyze data that follows a uniform distribution
    summarize the differences and use cases for parametric and non-parametric models
    train a model on an imbalanced dataset
    train and evaluate a logistic regression model
    use decision tree models for prediction

IN THIS COURSE

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    Statistical Analysis and Modeling in R
    36s
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