Stat 202 Objectives

By the end of the course, students will be able to ...

Chapter 1

• Given a data table and the story behind the data, identify the cases and list the variables.
• Identify a variable as either nominal, ordinal, identifier, binary, or quantitative.

Chapter 2

• Define and report the distribution of a categorical variable.
• Be able to convert between frequency, relative frequency, and percent.
• Tell when two plots of categorical data show the same distribution.

Chapter 3

• Define distribution of a quantitative variable.
• Create stem and leaf displays from data.
• Understand how histograms drawn with different bin widths can look different.
• Recognize when stem plots drawn with and without split stems are the same.
• Tell from a histogram whether a distribution is symmetric or left or right skewed.
• Tell whether a histogram is uniform, unimodal, bimodal, or multimodal and why.
• Tell whether a histogram shows outliers, or gaps.
• Describe how to compute the median.
• Describe how to compute the mean.
• Describe how to compute the lower and upper quartiles and the interquartile range (IQR).
• Describe how to compute the standard deviation.
• For summary statistics, describe the difference between resistant to outliers and sensitive to outliers.
• Compute the 5-number summary of data.
• Given data, compute the value for a specific percentile.
• Given data, compute the percentile for a specific value.

Chapter 4

• Identify the symmetry or left or right skew in boxplots.
• Comparing boxplots across groups, tell which groups have the greatest, and least, medians.
• Comparing boxplots across groups, tell which groups have the greatest, and least, interquartile range.

Chapter 5

• Find mean and standard deviation, and other summary statistics, for a quantitative variable.
• Given the mean and standard deviation for the raw data, compute the z-score for particular data points.
• Given the mean and standard deviation for the raw data, compute the raw score for particular z-scores.
• Interpret a z-score as the number of standard deviations of a data point above the mean.
• Know that, after transforming data to z-scores, the mean of the z-scores is 0.
• Know that, after transforming data to z-scores, the standard deviation of the z-scores is 1.
• Given several Normal probability plots, identify which looks most Normal (it should will be obvious).
• Use the Normal calculator in StatCrunch to compute percentiles and related quantities.
• Know the parameters for the standard Normal model (mean 0, standard deviation 1).
• Know that if the data follow a Normal distribution, the z-scores follow a standard Normal distribution.
• Know that if the data do NOT follow a Normal distribution, the z-scores do NOT follow Normal distribution either, standard or otherwise, although mean of the z-scores is always 0 and the standard deviation is always 1.