Learning Outcomes

icon of a magnifying glass over a list The textbook content and assessments for Introduction to Statistics are aligned to the following learning outcomes.

Module 1: Sampling and Data

  • Recognize and differentiate between key terms.
  • Apply various types of sampling methods to data collection.
  • Create and interpret frequency tables.


Module 2: Descriptive Statistics

  • Display data graphically and interpret graphs: stemplots, histograms, and box plots.
  • Recognize, describe, and calculate the measures of location of data: quartiles and percentiles.
  • Recognize, describe, and calculate the measures of the center of data: mean, median, and mode.
  • Recognize, describe, and calculate the measures of the spread of data: variance, standard deviation, and range.


Module 3: Probability

  • Understand and use the terminology of probability.
  • Determine whether two events are mutually exclusive and whether two events are independent.
  • Calculate probabilities using the Addition Rules and Multiplication Rules.
  • Construct and interpret Contingency Tables.
  • Construct and interpret Venn Diagrams.
  • Construct and interpret Tree Diagrams.


Module 4: Discrete Random Variables

  • Recognize and understand discrete probability distribution functions, in general.
  • Calculate and interpret expected values.
  • Recognize the binomial probability distribution and apply it appropriately.
  • Recognize the Poisson probability distribution and apply it appropriately.
  • Recognize the geometric probability distribution and apply it appropriately.
  • Recognize the hypergeometric probability distribution and apply it appropriately.
  • Classify discrete word problems by their distributions.


Module 5: Continuous Random Variables

  • Recognize and understand continuous probability density functions in general.
  • Recognize the uniform probability distribution and apply it appropriately.
  • Recognize the exponential probability distribution and apply it appropriately.


Module 6: Normal Distribution

  • Recognize the normal probability distribution and apply it appropriately.
  • Recognize the standard normal probability distribution and apply it appropriately.
  • Compare normal probabilities by converting to the standard normal distribution.


Module 7: The Central Limit Theorem

  • Recognize central limit theorem problems.
  • Classify continuous word problems by their distributions.
  • Apply and interpret the central limit theorem for means.
  • Apply and interpret the central limit theorem for sums.


Module 8: Confidence Intervals

  • Calculate and interpret confidence intervals for estimating a population mean and a population proportion.
  • Interpret the Student’s t probability distribution as the sample size changes.
  • Discriminate between problems applying the normal and the Student’s t distributions.
  • Calculate the sample size required to estimate a population mean and a population proportion given a desired confidence level and margin of error.


Module 9: Hypothesis Testing With One Sample

  • Differentiate between Type I and Type II Errors
  • Describe hypothesis testing in general and in practice
  • Conduct and interpret hypothesis tests for a single population mean, population standard deviation known.
  • Conduct and interpret hypothesis tests for a single population mean, population standard deviation unknown.
  • Conduct and interpret hypothesis tests for a single population proportion.


Module 10: Hypothesis Testing With Two Samples

  • Classify hypothesis tests by type.
  • Conduct and interpret hypothesis tests for two population means, population standard deviations known.
  • Conduct and interpret hypothesis tests for two population means, population standard deviations unknown.
  • Conduct and interpret hypothesis tests for two population proportions.
  • Conduct and interpret hypothesis tests for matched or paired samples


Module 11: The Chi Square Distribution

  • Interpret the chi-square probability distribution as the sample size changes.
  • Conduct and interpret chi-square goodness-of-fit hypothesis tests.
  • Conduct and interpret chi-square test of independence hypothesis tests.
  • Conduct and interpret chi-square homogeneity hypothesis tests.
  • Conduct and interpret chi-square single variance hypothesis tests.


Module 12: Linear Regression and Correlation

  • Discuss basic ideas of linear regression and correlation.
  • Create and analyze scatter plots.
  • Create and interpret a line of best fit.
  • Calculate and interpret the correlation coefficient.
  • Use interpolation and extrapolation.
  • Calculate and interpret outliers.


Module 13: F-Distribution and the One-Way ANOVA

  • Interpret the F probability distribution as the number of groups and the sample size change.
  • Discuss two uses for the F distribution: one-way ANOVA and the test of two variances.
  • Conduct and interpret one-way ANOVA.
  • Conduct and interpret hypothesis tests of two variances.


Module 14: Multiple and Logistic Regression

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