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 distributioncheck the assumptions of the paired samples t-testcompare and contrast population metrics with sample metricsconstruct hypothesis statements in the context of a statistical testdescribe the bias-variance trade-offestimate parameters of the population and interpret confidence intervalsexamine and interpret the data for regressionexamine and visualize data for regressionexplore and pre-process data before model fittingexplore and visualize the relationships in datafind the optimal number of clusters using the elbow method and Silhouette scorefit and interpret the S-curve of logistic regressionfit a straight line on data to build a regression model and evaluate the modelimplement the one-sample t-test and interpret resultsinterpret QQ plots for normally and non-normally distributed datainvestigate and visualize data before fitting a modeloutline the main characteristics of ensemble learningperform regression using decision treesperform regression using random forestperform simple linear regression with a single predictor
perform the one-sample t-test and interpret resultsperform the Wilcoxon signed-rank testposit the null hypothesis and alternative hypothesis of a statistical testrecall characteristics of overfitted and underfitted modelsrecall implications of the p-value and significance level alpharecall measures of central tendency and measures of dispersionrecall the assumptions made by the ANOVA testrecall the assumptions made by the one-sample t-testrecall the assumptions made by the two-sample t-testrecall the basic characteristics of machine learning modelsrecall the basic structure of decision tree modelsrecall the characteristics of discrete and continuous probability distributionsrecall the key metrics to evaluate classifiersrecall the sets of statistical tools used to understand datarecall the techniques used to evaluate clustering modelssample and analyze data that follows a uniform distributionsummarize the differences and use cases for parametric and non-parametric modelstrain a model on an imbalanced datasettrain and evaluate a logistic regression modeluse decision tree models for prediction
IN THIS COURSE
1.Statistical Analysis and Modeling in R36sUP NEXT
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