Simple Linear Regression and Statistical Computing Skills with R

It is important to demonstrate statistical knowledge and practice R programming concepts using datasets from external sources.

For this activity, perform the following:

Research the concept of linear regression.

Explain the difference between simple and multivariate linear regression.

Formulate a question that can be answered with simple linear regression.

Select a dataset from "UCI Machine Learning Repository," located in the topic Resources.Note:Make sure to select a dataset where the Default Task is labeled 'Regression.' If you are not sure, consult with your instructor.

Identify the independent and dependent variables in the data.

For each variable inspect the dataset, decide:

a) whether outliers are present and if yes, make and justify a recommendation on how to handle them; and

b) whether the data needs to be cleaned up in some way; explain your rationale.

For each variable, explain, calculate, visualize, and interpret the measures of central tendency.

For each variable, explain, calculate, visualize, and interpret the measures of dispersion.

For each variable, explain, calculate, visualize, and interpret skewness and kurtosis.

For each variable, create a histogram depicting the frequency distribution and interpret the plot.

Perform simple linear regression in R, using the appropriate variable(s) from the dataset you downloaded.

Display the results and include plots you think are necessary to support your findings.

Interpret the results.

Then, submit a professionally written and formatted R Markdown document knitted as a PDF.

Make sure the documentation contains the R code, relevant plots, your analysis, and the appropriate citations and references.