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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:
  • Pictogram
  • No, a pictogram uses icons or pictures to represent data quantities.
  • Bar Graph
  • 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
  • Correct. A histogram is the specific type of bar graph used to show the frequency distribution of a numeric variable.
  • Bar Chart
  • 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.

Checkpoint 6.6.2.

What is the key distinction between descriptive and inferential statistics?
  • Descriptive statistics use the mean and median, while inferential statistics use the variance and standard deviation.
  • 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.
  • 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.
  • 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.
  • Right! The text explains we describe the sample we have to infer what the larger population we don’t have might be like.
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.
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.
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)
USstatePops <- data.frame(V1)
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