1. 1: Analyzing Categorical Data



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See if you can match the image on the right.


  • Using Histograms Wisely

    • Here are several cautions based on common mistakes students make when using histograms.

Cautions!

  1. Don’t confuse histograms and bar graphs.

  2. Don’t use counts (in a frequency table) or percents (in a relative frequency table) as data.

    • The data refers to the x-axis

  3. Use percents instead of counts on the vertical axis when comparing distributions with different numbers of observations.

  4. Just because a graph looks nice, it’s not necessarily a meaningful display of data.


Ex: About 1.6 million first-year students enroll in colleges and universities each year. What do they plan to study? The graph on the right displays data on the percents of first-year students who plan to major in several discipline areas.

  1. Is this a bar graph or a histogram? Explain.




  1. Would it be correct to describe this distribution as right-skewed? Why or why not?



1.3: Describing Quantitative Data with Numbers
EXAMPLE How many pets?

A group of elementary school children was asked how many pets they have. Here are their responses, arranged from lowest to highest:

1 3 4 4 4 5 7 8 9

How can we describe the center of this data set?


Measures of center (central tendency)

  • Median




  • Mean



  • Mode?

Note: Population mean vs. sample mean


Mean and Median Applet: http://digitalfirst.bfwpub.com/stats_applet/stats_applet_6_meanmed.html



Ex: People say that it takes a long time to get to work in New York State due to the heavy traffic near big cities. What do the data say? The stemplot shows the travel times in minutes of 20 randomly chosen New York workers. How would you summarize the center of the distribution?

Measure of spread/variability

  • Range




    • Variance


    • Standard deviation

Steps to find the standard deviation:

  1. Calculate the mean.

  2. Calculate each deviation.

deviation = observation – mean



  1. Square each deviation.

  2. Find the sum of the squared deviations.

  3. Divide the sum by n – 1 (basically finding the “average” squared deviation)…this is called the
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