 Teacher resources and professional development across the curriculum

Teacher professional development and classroom resources across the curriculum  # Faculty Guides

Here's where you can find material to help you plan additional topic coverage as well as class activities. The Faculty Guides also provide solutions to all questions posed in the Student Guides.

Choose a specific unit below, or download the collated Faculty Guides for Units 2 - 11, Units 12 - 21, or Units 22 - 31.

Adobe Acrobat Reader is recommended for viewing the Guides.

Statistics is the art and science of gathering, organizing, analyzing and drawing conclusions from data. And without rudimentary knowledge of how it works, people can’t make informed judgments and evaluations of a wide variety of things encountered in daily life.

In this unit we cover:

• Constructing stemplots
• Interpreting stemplots
• Comparing stemplots
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In this unit we cover:

• Constructing histograms
• Modifying histograms
• Interpreting histograms
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In this unit we cover:

• Calculating the sample mean, median, and mode
• Selecting an appropriate measure of center
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In this unit we cover:

• Calculating a five-number summary
• Constructing boxplots
• Identifying outliers
• Comparing boxplots
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In this unit we cover:

• Selecting an appropriate measure of spread
• Exploring properties of the standard deviation
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In this unit we cover:

• Recognizing the characteristic shape of normal curves
• Defining density curves
• Specifying a normal distribution
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In this unit we cover:

• Approximating proportions using the 68-95-99.7% rule
• Calculating proportions using software or a standard normal table
• Calculating and interpreting z-scores
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In this unit we cover:

• Examining the shape of normal data
• Calculating percentiles of normal distributions
• Constructing a normal quantile plot
• Using normal quantile plots to assess normality
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In this unit we cover:

• Constructing scatterplots
• Describing patterns in scatterplots
• Identifying outliers
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In this unit we cover:

• Describing the least-squares criterion
• Calculating the equation of the least-squares line
• Checking the adequacy of a linear model
• Making predictions
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In this unit we cover:

• Calculating the correlation coefficient
• Interpreting correlation
• Understanding the effect of outliers on correlation
• Understanding that correlation does not imply causation
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In this unit we cover:

• Creating two-way tables of counts
• Calculating joint and marginal distributions
• Calculating conditional distributions
• Representing conditional distributions with bar graphs
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In this unit we cover:

• Distinguishing between experiments, retrospective studies, and prospective studies
• Understanding that association does not imply causation
• Knowing the level of evidence needed to imply causation
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In this unit we cover:

• Distinguishing between experiments and observational studies
• Describing the basic principles of experimental design
• Designing a randomized comparative experiment
• Using experimental results as evidence of causation
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In this unit we cover:

• Distinguishing between populations and samples
• Recognizing problems inherent in a census of a large population
• Recognizing the possibility of bias in voluntary or convenience samples
• Selecting simple random samples
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In this unit we cover:

• Knowing that most national data result from large-scale surveys
• Describing a variety of sampling plans
• Recognizing non-statistical aspects of sampling
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In this unit we cover:

• Identifying random phenomena from everyday life
• Developing the concept of probability
• Assigning probabilities
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In this unit we cover:

• Listing outcomes in a sample space
• Setting up probability models
• Defining independent and mutually exclusive events
• Working with probability rules
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In this unit we cover:

• Understanding the concept of a random variable
• Differentiating between continuous and discrete random variables
• Creating probability distributions for discrete random variables
• Constructing probability histograms
• Calculating the mean and standard deviation of discrete random variables
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In this unit we cover:

• Recognizing a binomial setting
• Defining a binomial random variable
• Calculating binomial probabilities
• Calculating the mean and standard deviation of binomial random variables
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In this unit we cover:

• Understanding the concept of sampling distributions
• Describing the sampling distribution of
• Applying the central limit theorem
• Setting up an control chart
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In this unit we cover:

• Understanding the rationale for statistical process control
• Constructing a run chart
• Constructing an control chart
• Making decisions based on run charts and control charts
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In this unit we cover:

• Constructing z-confidence intervals for a population mean
• Interpreting a confidence interval
• Understanding how the confidence level affects the margin of error
• Understanding how the sample size affects the margin of error
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In this unit we cover:

• Understanding the rationale behind significance tests
• Stating null and alternative hypotheses about a population mean
• Calculating z-test statistics
• Determining and interpreting p-values
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In this unit we cover:

• Describing t-distributions
• Checking the conditions for inference about a population mean
• Constructing t-confidence intervals for a population mean
• Conducting t-tests about a population mean
• Adapting one-sample t-procedures for use with matched-pairs data
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In this unit we cover:

• Calculating confidence intervals for the difference of two population means
• Conducting significance tests about the difference of two population means
• Checking conditions for inference about two population means
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In this unit we cover:

• Describing the sampling distribution of a sample proportion
• Calculating z-confidence intervals for a population proportion
• Conducting z-tests for a population proportion
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In this unit we cover:

• Calculating expected counts in two-way tables
• Calculating chi-square test statistics
• Checking conditions for chi-square tests
• Determining p-values from chi-square distributions
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In this unit we cover:

• Understanding the linear regression model
• Estimating model parameters
• Checking conditions for regression inference
• Conducting t-tests for population slopes
• Calculating t-confidence intervals for population slopes
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In this unit we cover:

• Understanding the idea of analysis of variance
• Setting up the null and alternative hypotheses for comparing several means
• Calculating F-test statistics
• Determining p-values from F-distributions
• Checking conditions for ANOVA inference
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Throughout the course, we’ve taken you inside statistics to show how these lessons can be valuable in ways you might never have expected. Check out how students can apply what they’ve learned in Against All Odds to a variety of careers, academic fields, and hobbies: View Unit