Predittori dell'età degli abalone

Per chiudere il corso, in questi ultimi due esercizi metterai in pratica molte delle competenze che hai imparato. Eseguirai due modelli di regressione confrontando l'adattamento del modello dell'età degli abalone in base al peso sgusciato oppure al peso del guscio.

Il pacchetto dplyr e il dataset abaloneKeep sono già caricati per te.

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R per utenti SAS

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Run lm() for age by shuckedWeight and by shellWeight
lmshucked <- lm(___ ~ ___, ___)
lmshell <- lm(___ ~ ___, ___)

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Questo esercizio fa parte del corso

R per utenti SAS

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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: Exploratory data analysis Exercise 2: Descriptive statistics and function masking Exercise 3: Specific statistics for one or more variables Exercise 4: Summary statistics by group Exercise 5: Correlations and t-tests Exercise 6: Bivariate correlations Exercise 7: Scatterplots Exercise 8: Correlations by sex Exercise 9: Tests for two groups Exercise 10: Categorical data: analyze and visualize Exercise 11: Chi-square tests Exercise 12: Mosaic plots Exercise 13: Age categories by shellweight categories

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: Lavorare con gli oggetti di output Exercise 2: Output delle statistiche descrittive Exercise 3: Riepiloga l'output Exercise 4: Output di group_by Exercise 5: Lavorare con le liste Exercise 6: Output di Hmisc describe Exercise 7: Correlations output Exercise 8: Output dei t-test Exercise 9: Output dei test chi-quadrato Exercise 10: ANOVA e modelli lineari Exercise 11: ANOVA Exercise 12: Regressione lineare Exercise 13: Valutazione dei modelli finali Exercise 14: Predittori dell'età degli abalone

Esercizio in corso

Exercise 15: Miglior modello per sesso Exercise 16: Riepilogo del corso e raccomandazioni