This is an assignment about Statistics course by using the R Studio.

R Homework for Chapter 4

The Numbeo website (www.numbeo.com) provides access to a variety of data. One table lists prices of certain items in selected cities around the world. They also report an overall cost-of-living index for each city compared to the costs of hundreds of items in New York City. For example, London at 110.69 is 10.69% more expensive than New York. In the data file Cost_of_living_2013.txt included are the Cost of Living Index, a Rent Index, a Groceries Index, a Restaurant Price Index, and a Local Purchasing Power Index that measures the ability of the average wage earner in a city to buy foods and services. All indices are measured relative to New York City, which is scored 100.

You can find the data file on Blackboard. Download it and put it in the same folder as your R program file. Then, use the following command to read in the data cost <- read.table('Cost_of_Living_2013.txt', sep = '\t', header = TRUE)

and answer the questions below.

1. Produce a scatterplot between the Cost of Living Index and EACH of the other index variables. As a result, there should be 4 scatterplots in total. Examine the relationship shown in each scatterplot in terms of its form, strength and direction.

2. Compute the correlation coefficients for all the scatterplots obtained above.

3. Verify the conditions for EACH correlation coefficient computed above.

4. Fit a linear regression model between the Cost of Living Index and each of the other index variables. As a result, there should be 4 regression models in total. Interpret the resulting estimated slope in each model.

5. Based on the correlation coefficients and the regression models obtained above, which item would be the best predictor of overall cost in these cities? Which would be the worst? Explain.

6. Find the cost of living as predicted by Groceries Index and its residual for Beijing, China. (Hint: Find row index of Beijing in the dataset, and then use that index to extract the corresponding fitted value and residual from the regression result.)

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