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Introduction to Data Science Using R Runestone Version 1

Dr. Jan Pearce, Editor

Section 6.6 Chapter Challenge and Questions

Checkpoint 6.6.1.

A bar graph that displays the frequencies of occurrence for a numeric variable is called a:
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  • Pictogram
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  • No, a pictogram uses icons or pictures to represent data quantities.
  • Bar Graph
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  • While a histogram is a type of bar graph, "Histogram" is the more specific and correct term for displaying the frequency of a numeric variable.
  • Histogram
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  • Correct. A histogram is the specific type of bar graph used to show the frequency distribution of a numeric variable.
  • Bar Chart
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  • This is another term for a bar graph, which is generally used for comparing discrete categories rather than showing the frequency distribution of a continuous numeric variable.
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Checkpoint 6.6.2.

What is the key distinction between descriptive and inferential statistics?
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  • Descriptive statistics use the mean and median, while inferential statistics use the variance and standard deviation.
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  • No, all of these are descriptive statistics that can be used as a basis for inference.
  • Descriptive statistics work on populations, while inferential statistics work on samples.
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  • No, it’s the other way around. We describe a sample to make an inference about a population.
  • Descriptive statistics are about pictures like histograms, while inferential statistics are about numbers.
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  • No, both fields use numbers and pictures. A histogram is a tool for describing data.
  • Descriptive statistics summarize a sample of data, while inferential statistics try to generalize from that sample to a larger population.
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  • Right! The text explains we describe the sample we have to infer what the larger population we don’t have might be like.
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In this chapter, we used rnorm() to generate random numbers that closely fit a normal distribution. We also learned that the state population data was a "Pareto" distribution. Do some research to find out what R function generates random numbers using the Pareto distribution. Then run that function with the correct parameters to generate 51 random numbers (hint: experiment with different probability values). Create a histogram of these random numbers and describe the shape of the distribution.
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In case you have difficulty with the read.DIF() or read.table() functions, the code shown below can be copied and pasted (or, in the worst case scenario, typed) into the R console to create the data set used in this chapter.
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V1 <- c(4779736,710231,6392017,2915918,37253956, 5029196,3574097,897934,601723,18801310,9687653, 1360301,1567582,12830632,6483802,3046355,2853118, 4339367,4533372,1328361,5773552,6547629,9883640, 5303925,2967297,5988927,989415,1826341,2700551, 1316470,8791894,2059179,19378102,9535483,672591, 11536504,3751351,3831074,12702379,1052567, 4625364,814180,6346105,25145561,2763885,625741, 8001024,6724540,1852994,5686986,563626)
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USstatePops <- data.frame(V1)
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