Correlaties per geslacht

Zoals je in een eerdere oefening zag, was de correlatie tussen abalone-age en shellWeight vrij sterk met Pearson’s r=0,63. Bereken deze correlatie opnieuw voor elke sex: "I" voor onvolwassen jongen, "F" voor volwassen vrouwtjes en "M" voor volwassen mannetjes.

Voor welke sex is de correlatie tussen age en shellWeight het grootst?

De abaloneKeep-gegevensset en de pakketten dplyr en psych zijn voor je geladen.

Deze oefening maakt deel uit van de cursus

R voor SAS-gebruikers

Cursus bekijken

Praktische interactieve oefening

Zet theorie om in actie met een van onze interactieve oefeningen.

Begin met trainen

Deze oefening maakt deel uit van de cursus

R voor SAS-gebruikers

SkillTag.level.beginnerSkillTag.label

4.8+

Begin de cursus gratis

This first chapter will get you oriented into the R programming environment. You'll learn how to get help, load a dataset, and increase functionality by adding packages. You'll begin working with the abalone dataset (through the dplyr package workflow) to get descriptive statistics and create helpful visualizations (using the ggplot2 package).

Exercise 1: Get help and load data in R Exercise 2: Getting help Exercise 3: Load dataset and get details Exercise 4: Add functionality with packages Exercise 5: Dataset contents and descriptive statistics Exercise 6: Load external dataset Exercise 7: Dataset contents Exercise 8: Descriptive statistics Exercise 9: Graphical visualizations Exercise 10: Histograms Exercise 11: Boxplots and violin plots Exercise 12: Scatterplots

Now that you are oriented in the R environment, this chapter will advance your understanding of R's versatility working with data objects. You'll learn how to create and modify variables in the abalone data set. Using your ggplot2 visualization skills, you will discover the data errors in the abalone data and then create a final cleaned data set ready for analysis and modeling.

Exercise 1: Objects - the building blocks of R Exercise 2: Create data objects in R Exercise 3: Create composite object types Exercise 4: Selecting elements from objects Exercise 5: Determine variable types Exercise 6: Select elements in objects Exercise 7: Manipulating datasets and data objects Exercise 8: Create new variables Exercise 9: Recode variables Exercise 10: Object type conversion Exercise 11: Data quality and cleaning Exercise 12: Variables inspection Exercise 13: Illogical weights Exercise 14: Check dimension measurements Exercise 15: Check final dataset

Once your data set has been cleaned, the next step is exploration. In chapter 3 you will learn how to compute descriptive statistics, explore associations (e.g., correlations) among the variables, and perform bi-variate statistical tests (e.g., t-tests and chi-square tests). You will also create graphical visualizations which illustrate the bi-variate associations and group comparison tests.

Exercise 1: Exploratieve data-analyse Exercise 2: Beschrijvende statistiek en functiemasking Exercise 3: Specifieke statistieken voor één of meer variabelen Exercise 4: Samenvattende statistieken per groep Exercise 5: Correlaties en t-toetsen Exercise 6: Bivariate correlaties Exercise 7: Spreidingsdiagrammen Exercise 8: Correlaties per geslacht

Huidige oefening

Exercise 9: Toetsen voor twee groepen Exercise 10: Categorische gegevens: analyseren en visualiseren Exercise 11: Chi-kwadraattoetsen Exercise 12: Mozaïekdiagrammen Exercise 13: Leeftijdscategorieën per schelpgewicht-categorieën

In this final chapter, you will learn how to work with one of the most versatile data object types in R called a list. These skills will enable you to save and manipulate your output from descriptive statistics, associations, and group comparison computations. You will also learn how to perform ANOVA (analysis of variance) and linear regression in R. All your skills are put to use in the final exercises to create the best models for predicting abalone ages from their sex, size, and weight measurements.

Exercise 1: Working with output objects Exercise 2: Descriptive statistics output Exercise 3: Summarise output Exercise 4: Group_by output Exercise 5: Working with lists Exercise 6: Hmisc describe output Exercise 7: Correlations output Exercise 8: t-tests output Exercise 9: Chi-square tests output Exercise 10: ANOVA and linear models Exercise 11: ANOVA Exercise 12: Linear regression Exercise 13: Final models evaluation Exercise 14: Abalone age predictors Exercise 15: Best model by sex Exercise 16: Course summary and recommendations