Who, What, When, Where, Why and
of Data? How do we identify the cases and variables in a data set? How do we classify a variable in a data set? How do we identify the units in which variables have been measured for any quantitative variable? How do we describe a variable in terms of its
Who, What, When, Where, Why and How? Chapter 3: Displaying and Describing Categorical Data (Pages 1523) How do we identify whether a variable is categorical and choose an appropriate display for it? How do we examine the association between categorical variables by comparing conditional marginal percentages? Chapter 4: Displaying Quantitative Data (Pages 3646) How do we identify an appropriate display for any quantitative variable? How do we guess the shape of the distribution of a variable by knowing something about the data? How do we display the distribution of a quantitative variable with a stemandleaf display, a dotplot, or a histogram?
How do we make a timeplot of data that may vary over time? How do we describe the distribution of a quantitative variable in terms of its shape, center and spread? How do we describe any anomalies or extraordinary features revealed by the display of a variable? How do we compare the distributions of two or more groups by comparing their centers, shapes, centers and spreads? How do we describe patterns over time shown in a timeplot? How do outliers in a data deviate from the overall pattern of the data? Chapter 5: Describing Distributions Numerically (Pages 5768) How do we select a suitable measure of center and a suitable measure of spread for a variable based on information about its distribution? What are the basic properties of the median? What are the basic properties of the mean? What does the standard deviation tell you about the data? How do we compute the mean and median of a set of data? How do we compute the standard deviation and IQR of a set of data? How do we create a fivenumber summary of a variable? How do we construct a boxplot by hand from a fivenumber summary? Does the median and IQR resist the effects of outliers? What about the mean and standard deviation? How is the mean relative to the median in a skewed distribution? How do we describe summary measures in a sentence? How do we describe the distribution of a quantitative variable with a description of the shape of the distribution, a numerical measure of the center, and a numerical measure of the spread? How do we compare the distributions of two or more groups by comparing their shapes, centers and spreads? How do we compare two or more groups by comparing their boxplots? How do we use the 1.5 IQR rule to identify possible outliers?
Chapter 6: The Standard Deviation as a Ruler and the Normal Model (Pages 8397) How does adding, subtracting, multiplying or dividing by a constant change the center and/or spread of a variable? When can standardization be used to compare values? Does standardization use standard deviation as a ruler? When is the normal model appropriate? How do we calculate the zscore of an observation? How do we compare the values of two different variables y using their zscores
When using the normal model what percentage of observations should be within one standard deviation? What percentage should be within two standard deviations? What percentage should be within 3 standard deviations?
How can we find the percentage of observations that fall below any of the values in a normal model? How can we check if a variable satisfies the nearly normal condition with the use of a normal probability plot of a histogram? How to interpret zscores? (What do zscores mean?)