Final Exam: Statistical Analysis and Modeling in R
R Programming 4.0+ | Intermediate
- 1 Video | 32s
- 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
recall the sets of statistical tools used to understand datacompare and contrast population metrics with sample metricsrecall the characteristics of discrete and continuous probability distributionsanalyze data that follows a uniform distributioninterpret QQ plots for normally and non-normally distributed datasample and analyze data that follows a uniform distributionrecall measures of central tendency and measures of dispersionestimate parameters of the population and interpret confidence intervalsconstruct hypothesis statements in the context of a statistical testposit the null hypothesis and alternative hypothesis of a statistical testrecall implications of the p-value and significance level alpharecall the assumptions made by the one-sample t-testrecall the assumptions made by the two-sample t-testsummarize the differences and use cases for parametric and non-parametric modelsrecall the assumptions made by the ANOVA testperform the Wilcoxon signed-rank testcheck the assumptions of the paired samples t-testimplement the one-sample t-test and interpret resultsexamine and interpret the data for regressionexamine and visualize data for regression
perform regression using decision treesperform regression using random forestoutline the main characteristics of ensemble learningperform the one-sample t-test and interpret resultsrecall the basic characteristics of machine learning modelsfit a straight line on data to build a regression model and evaluate the modelexplore and visualize the relationships in dataperform simple linear regression with a single predictorrecall the key metrics to evaluate classifiersfit and interpret the S-curve of logistic regressiontrain and evaluate a logistic regression modelrecall the basic structure of decision tree modelsexplore and pre-process data before model fittingtrain a model on an imbalanced datasetuse decision tree models for predictionrecall the techniques used to evaluate clustering modelsinvestigate and visualize data before fitting a modelfind the optimal number of clusters using the elbow method and Silhouette scorerecall characteristics of overfitted and underfitted modelsdescribe the bias-variance trade-off
IN THIS COURSE
1.Statistical Analysis and Modeling in R33sUP NEXT
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