Ch. 2: Data

1. Use context to describe a data set
  1. Identify 5 W's and recognize when some information has not been provided
  2. Identify cases and variables in a data set
  3. Describe a variable in terms of the 5 W's and remark when the information is not provided
*Text Reference: p.8,9,12
2. Identify the types of variables - categorical/quantitative
  1. Classify a variable as categorical or quantitative depending on its use
  2. For a quantitative variable, identify the units of measure or note that they have not been provided
*Text Reference: p.10,11
*Extra practice: Take this pre-assesment for some more practice

Ch. 3: Display and Describe Categorical Data

1. Create a display of categorical data
  1. Recognize when a variable is categorical and choose an appropriate display for it
  2. Display the distribution of a categorical variable with a bar chart or pie chart
*Extra practice: Create an appropriate display for this set of Data from Student Information
*Text Reference: p.21-24, 28, 29
2. Describe the distribution of a categorical variable
  1. Summarize distribution of categorical data with a frequency table
  2. Know how to make and examine displays of the conditional distributions of one variable for two or more groups
  3. Describe the dist. of a categorical variable in terms of its possible values and relative frequencies
  4. Describe any anomalies or extraordinary features revealed by the display of a variable
*Extra practice: Describe the display you created from the data above
*Text Reference: p.25-27, 30,31
3. Determine if two variables are independent/not independent
  1. Understand how to examine the association between categorical variables by comparing marginal/conditional distributions
  2. Know how to make and examine a contingency table
  3. Describe and discuss patterns found in a contingency table and associated displays of conditional distributions
*Extra practice:
  • Choose two random categories from the Student Information above, and decide if they are independant or not.
  • Examine these segmented bar graphs, and decide if the varibles are independent or not
*Text Reference: p.28

Ch. 4: Display and Describe Quantitative Data

1. Create an appropriate display for a quantitative variable
  1. Be able to identify an appropriate display for any quantitative variable
  2. Know how to display the distribution of a quantitative variable with a stem-and-leaf display (by hand for small sets), dotplot, or histogram(technology for large data sets)Know how to make a timeplot of data that may vary over time
*Text Reference: p.46-50
2. Describe the distribution of a quantitative variable(shape)
  1. Be able to guess the shape of the distribution of a variable by knowing something about the data
  2. Be able to describe the distribution of a quantitative variable in terms of its shape, center, and spread
  3. Be able to describe any anomalies or extraordinary features revealed by the display of a variable
  4. Know how to describe patterns over time shown in a timeplot
  5. Be able to discuss any outliers in the data, noting how they deviate from the overall pattern of the data
*Extra practice: Take the 4.2 assesment for some more practice with describing distributions
*Text Reference: p.50-54

Ch. 5: Describe Distributions Numerically

1. Know basic properties & definitions of measures of center
  1. Know the basic properties of the median: Divides data into half below the median and half above the median
  2. Know the basic properties of the mean: the mean is the point at which the histogram balances
  3. Understand that the median and IQR resist the effects of outliers, while the mean and standard deviation do not
  4. Understand that in a skewed distribution, the mean is pulled in the direction of the skewness (toward the longer tail) relative to the median
*Text Reference: p.74-76
2. Know how to compute measures of center
  1. Know how to compute the mean and median of a set of data
*Text Reference: p.74,82
3. Know basic properties & definitions of measures of spread
  1. Know that the standard deviation summarizes how spread out all the data are around the mean
  2. Understand that the median and IQR resist the effects of outliers, while the mean and standard deviation do not
*Text Reference: p.82-84
4. Know how to compute measures of spread
  1. Know how to compute the standard deviation and IQR of a set of data
*Text Reference: p.75,83
5. Describe the distribution of a quantitative variable numerically
  1. Be able to select a suitable measure of center and a suitable measure of spread for a variable based on information about its distribution
  2. Be able to create a 5-number summary of a variable
  3. Be able to construct a boxplot by hand from a 5-number summary
  4. Know how to describe the summary measures in a sentence. In particular, know that the common measures of center and spread have the same units as the variable that they summarize and should be described in those units
  5. Be able to describe the distribution of a quantitative variable with a description of the shape of the distribution, a numerical measure of center, and a numerical measure of spread. Be sure to note any unusual features, such as outliers
  6. Know how to use the 1.5IQR rule to identify possible outliers. Interpret outliers found in boxplots made on a computer.
6. Compare the distributions of two or more quantitative variables
  1. Know how to compare the distributions of two or more groups by comparing their shapes, centers, and spreads
  2. Be able to compare two or more groups by comparing their boxplots
*Extra Practice: Check out your Box Office Mojo project and the comments that Mr. C made on it.

Ch. 6: The Normal Model


1.Standardize a variable/measurement and understand the importance of doing so
  1. Standardize an observation using the mean and standard deviation of the variable
  2. Understand how adding(subtracting) a constant or multiplying(dividing) by a constant changes the center and/or spread of a variable
  3. Recognize when standardization can be used to compare values.
  4. Understand that standardizing uses the standard deviation as a ruler
*Text Reference:p.102-107
2. Calculate, interpret, and explain z-scores
  1. Know how to calculate the z-score of an observation
  2. Know how to compare values of two different variables using their z-scores
  3. Know what z-scores mean
*Text Reference: p.103-104
3. Determine if the Normal model is appropriate for a given data set
  1. Know how to check whether a variable satisfies the Nearly Normal Condition by making a Normal probability plot or a histogram
*Text Reference: p.108
4. Use the Normal model to calculate probabilities above, below, and between values
  1. Be able to explain how extraordinary a standardized value may be by using a Normal model
  2. Be able to use Normal models and the 68-95-99.7 Rule to estimate the percentage of observations falling within 1, 2, 3 standard deviations of the mean
  3. Know how to find the percentage of observations falling below any value in a Normal model using a Normal table or appropriate technology
*Text Reference: p.108-109