Difference between revisions of "Stat 202 Objectives"

Revision as of 21:56, 25 March 2018

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.
• Know to put gaps in stem plots when gaps exist in data.
• 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.

Chapter 6

• Know what it means for two variables to be associated, (knowing the value of one variable tells you something about the value of the other that you would not know otherwise).
• Be able to describe an association in terms of form, strength, direction.
• Be able to identify outliers in one or both variables (often x, and y) as possibly separate from outliers for the relationship.
• Know the difference between explanatory variables and response variables.
• Know how to draw a scatter plot.
• Know how to identify linear relationship (with scatter) between two variables.
• Know how to compute the correlation between two variables.

Chapter 7

• Know how to perform a simple linear regression to describe the relationship between a response and an explanatory variable.
• Know that if the explanatory and response variables are switched, the regression line changes, even when plotted on the same axes (unless there is no scatter in the data).
• Know that both correlation and simple linear regression are only appropriate if the form of the association between the variables is linear.
• Know that both correlation and regression are only appropriate for quantitative variables (not an objective: the book discusses alternatives for ordinal variables).
• Know that both correlation and regression can be sensitive to outliers.
• Know this example: if the scatter plot shows a perfect linear relationship between two variables---perfect that is except for one extreme outlier---then the correlation coefficient will suggest a weaker relationship between the data points than is actually there---weaker that linearly associated data points with the same correlation coefficient and no outliers. (Remember the Ascombie data sets.)
• Likewise, know that if the association between variables is nonlinear, the correlation coefficient will not reveal the full strength of the association.
• Know that the best way to discern the appropriateness of the linear model (on which both correlation and regression are based) is by looking at the residuals. The linear model is appropriate if the residuals are a horizontal band of points around zero, with no structure. Be able to plot the residuals versus X-values for this purpose.