Statistics II

Course Number:
STAT 244
Transcript Title:
Statistics II
Created:
Aug 15, 2022
Updated:
Jun 29, 2023
Total Credits:
5
Lecture Hours:
50
Lecture / Lab Hours:
0
Lab Hours:
0
Satisfies Cultural Literacy requirement:
No
Satisfies General Education requirement:
Yes
Grading Options
A-F, P/NP, Audit
Default Grading Options
A-F
Repeats available for credit:
0
Prerequisites

MTH 243 or STAT 243Z

Course Description

Investigates confidence interval estimation; tests of significance including z-tests, t-tests, ANOVA, and chi-square; and inference for linear regression. Applications are investigated from science, business, and social science perspectives. Graphing calculator with advanced statistical programs required and/or computer software. Prerequisites: MTH 243 or STAT 243Z. Audit available.

Course Outcomes

Upon successful completion of this course, students will be able to:

  1. Statistically analyze observational and experimental studies and critically assess results from the same.
  2. Clearly communicate statistical procedures and results.
  3. Read with understanding scholarly publications and critically assess public dissemination of statistical information.
  4. Adapt statistical techniques and reasoning to other disciplines and vocations.

Alignment with Institutional Learning Outcomes

Major
1. Communicate effectively using appropriate reading, writing, listening, and speaking skills. (Communication)
Major
2. Creatively solve problems by using relevant methods of research, personal reflection, reasoning, and evaluation of information. (Critical thinking and Problem-Solving)
Major
3. Extract, interpret, evaluate, communicate, and apply quantitative information and methods to solve problems, evaluate claims, and support decisions in their academic, professional and private lives. (Quantitative Literacy)
Not Addressed
4. Use an understanding of cultural differences to constructively address issues that arise in the workplace and community. (Cultural Awareness)
Minor
5. Recognize the consequences of human activity upon our social and natural world. (Community and Environmental Responsibility)

To establish an intentional learning environment, Institutional Learning Outcomes (ILOs) require a clear definition of instructional strategies, evidence of recurrent instruction, and employment of several assessment modes.

Major Designation

  1. The outcome is addressed recurrently in the curriculum, regularly enough to establish a thorough understanding.
  2. Students can demonstrate and are assessed on a thorough understanding of the outcome.
    • The course includes at least one assignment that can be assessed by applying the appropriate CLO rubric.

Minor Designation

  1. The outcome is addressed adequately in the curriculum, establishing fundamental understanding.
  2. Students can demonstrate and are assessed on a fundamental understanding of the outcome.
    • The course includes at least one assignment that can be assessed by applying the appropriate CLO rubric.

Suggested Outcome Assessment Strategies

The determination of assessment strategies is generally left to the discretion of the instructor. Here are some strategies that you might consider when designing your course: writings (journals, self-reflections, pre writing exercises, essays), quizzes, tests, midterm and final exams, group projects, presentations (in person, videos, etc), self-assessments, experimentations, lab reports, peer critiques, responses (to texts, podcasts, videos, films, etc), student generated questions, Escape Room, interviews, and/or portfolios.

Course Activities and Design

The determination of teaching strategies used in the delivery of outcomes is generally left to the discretion of the instructor. Here are some strategies that you might consider when designing your course: lecture, small group/forum discussion, flipped classroom, dyads, oral presentation, role play, simulation scenarios, group projects, service learning projects, hands-on lab, peer review/workshops, cooperative learning (jigsaw, fishbowl), inquiry based instruction, differentiated instruction (learning centers), graphic organizers, etc.

Course Content

Outcome #1: Statistically analyze observational and experimental studies and critically assess results from the same.

To address this outcome, the following shall be taught:

  1. Random variable and probability distributions (the instructional goal is to explore and analyze random variables and probability distributions)
    • Explore probability distributions:
    • Normal
    • Student’s t
    • F
    • Chi-square
  2. Estimation: one sample (the instructional goal is to estimate a population parameter by calculating a confidence interval)
    • Identify and describe terminology:
      • Confidence coefficient
      • Confidence level
      • Confidence interval
    • Check the conditions associated with a confidence interval for a population mean
    • Check the conditions associated with a confidence interval for a population proportion
    • Calculate and interpret a confidence interval for a population mean
    • Calculate and interpret a confidence interval for a population proportion
    • Calculate a sample size to attain a desired margin of error and confidence level
    • Using technology, input a sample and execute the commands to create a confidence interval; interpret the output
  3. Statistical inference: one sample (the instructional goal is to utilize sample information to test whether a population parameter is less than, not equal to, or greater than a specified value)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Calculate a sample size
    • Identify and describe terminology:
      • Null and alternative hypotheses
      • Test statistic
      • Distinguish between significance and hypothesis testing
      • Type I and Type II errors
      • Observed significance level: P-value
    • Check the conditions associated with a test of significance about a population mean
    • Check the conditions associated with a test of significance about a population proportion
    • Construct and interpret a z-test about a population mean
    • Construct and interpret a t-test about a population mean
    • Construct and interpret a z-test about a population proportion
    • Using technology, input a sample and execute the commands to perform a t-test or a z-test and interpret the output
    • Calculate and interpret the power of a z-test
  4. Estimation and statistical inference: two samples (the instructional goal is to utilize sample information to infer whether a difference exists between two population means or two population proportions)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population proportions using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using paired samples
    • Construct and interpret a confidence interval about differences between two population means using two independent samples
    • Construct and interpret a t-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population proportions using two independent samples
    • Construct and interpret a confidence interval about the mean difference between two populations using paired samples
    • Construct and interpret a t-test about the mean difference between two populations using paired samples
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of means test; interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions confidence interval and interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions test, and interpret the output
    • Using technology, input two paired samples and execute the commands to perform a one-sample t-test and interpret the output
  5. Analysis of variance, ANOVA (The instructional goal is to design and analyze a sampling experiment to compare the means of more than two populations)
    • Identify and describe technology:
      • Response (dependent) variable
      • Factor (independent variable, stimulus)
      • Levels (treatments) of a factor
      • Sum of squares for treatments (SST) and error (SSE)
      • Mean square for treatments (MST) and error (MSE)
    • Check the conditions associated with an ANOVA
    • Compare the treatment means
    • Summarize the results of an F-test in an ANOVA table.
    • Using technology, input sample data and execute the commands to perform ANOVA and interpret the output
    • Use a multiple comparisons method to determine which pairs of means differ and interpret the results
  6. Chi-square tests and contingency tables (the instructional goal is to explore a non-parametric procedure on categorical variables)
    • Identify and describe terminology:
      • Multinomial probabilities
      • Contingency tables
      • Marginal probabilities
    • Check the conditions associated with a goodness-of-fit test
    • Check the conditions associated with a test of independence
    • Check the conditions associated with a test of homogeneity
    • Perform a goodness-of-fit test about the probability distribution of a random variable
    • Determine whether two classifications of nominal data are independent using a contingency table, multinomial probabilities, and a chi-square test
    • Using technology, input sample data, choose commands to perform an appropriate chi-square test and interpret the output
  7. Simple linear regression and correlation (The instructional goal is to explore a straight-line relationship between two random variables, and use the least-squares line as a basis for inference about a population)
    • Identify the explanatory variable and the response variable
    • Check the conditions associated with constructing a least-squares linear regression model
    • Construct a scatter plot of the sample data
    • Identify the least-squares estimates of the intercept and the slope population parameters
    • Specify the probability distribution of the random error term, and estimate the standard deviation of this distribution
    • Evaluate the utility of the model:
      • Conduct a test of significance to determine whether the data provide sufficient evidence to indicate that the explanatory variable contributes information for the linear prediction of the response variable
      • Construct and interpret a confidence interval to estimate the slope of the population regression model
      • Calculate and interpret the sample correlation coefficient
      • Calculate and interpret the sample coefficient of determination
    • Use the least-squares line for estimation and prediction:
      • Construct and interpret a confidence interval for the mean value of the response value when the explanatory variable takes on a specific value
      • Construct and interpret a prediction interval for an individual value of the response value when the explanatory variable takes on a specific value
    • Using technology, input sample data and execute the commands to produce a least-squares regression equation, a fitted line, a residual plot, and interpret the output

Outcome #2: Clearly communicate statistical procedures and results.

To address this outcome, the following shall be taught with an emphasis on clear communication of the included statistical procedures and results:

  1. Random variable and probability distributions (the instructional goal is to explore and analyze random variables and probability distributions)
    • Explore probability distributions:
      • Normal
      • Student’s t
      • F
      • Chi-square
  2. Estimation: one sample (the instructional goal is to estimate a population parameter by calculating a confidence interval)
    • Identify and describe terminology:
      • Confidence coefficient
      • Confidence level
      • Confidence interval
    • Check the conditions associated with a confidence interval for a population mean
    • Check the conditions associated with a confidence interval for a population proportion
    • Calculate and interpret a confidence interval for a population mean
    • Calculate and interpret a confidence interval for a population proportion
    • Calculate a sample size to attain a desired margin of error and confidence level
    • Using technology, input a sample and execute the commands to create a confidence interval; interpret the output
  3. Statistical inference: one sample (the instructional goal is to utilize sample information to test  whether a population parameter is less than, not equal to, or greater than a specified value)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Calculate a sample size
    • Identify and describe terminology:
      • Null and alternative hypotheses
      • Test statistic
      • Distinguish between significance and hypothesis testing
      • Type I and Type II errors
      • Observed significance level: P-value
    • Check the conditions associated with a test of significance about a population mean
    • Check the conditions associated with a test of significance about a population proportion
    • Construct and interpret a z-test about a population mean
    • Construct and interpret a t-test about a population mean
    • Construct and interpret a z-test about a population proportion
    • Using technology, input a sample and execute the commands to perform a t-test or a z-test and interpret the output
    • Calculate and interpret the power of a z-test
  4. Estimation and statistical inference: two samples (the instructional goal is to utilize sample information to infer whether a difference exists between two population means or two population proportions)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population proportions using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using paired samples
    • Construct and interpret a confidence interval about differences between two population means using two independent samples
    • Construct and interpret a t-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population proportions using two independent samples
    • Construct and interpret a confidence interval about the mean difference between two populations using paired samples
    • Construct and interpret a t-test about the mean difference between two populations using paired samples
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of means test; interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions confidence interval and interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions test, and interpret the output
    • Using technology, input two paired samples and execute the commands to perform a one-sample t-test and interpret the output
  5. Analysis of variance, ANOVA (The instructional goal is to design and analyze a sampling experiment to compare the means of more than two populations)
    • Identify and describe technology:
      • Response (dependent) variable
      • Factor (independent variable, stimulus)
      • Levels (treatments) of a factor
      • Sum of squares for treatments (SST) and error (SSE)
      • Mean square for treatments (MST) and error (MSE)
    • Check the conditions associated with an ANOVA
    • Compare the treatment means
    • Summarize the results of an F-test in an ANOVA table.
    • Using technology, input sample data and execute the commands to perform ANOVA and interpret the output
    • Use a multiple comparisons method to determine which pairs of means differ and interpret the results
  6. Chi-square tests and contingency tables (the instructional goal is to explore a non-parametric procedure on categorical variables)
    • Identify and describe terminology:
      • Multinomial probabilities
      • Contingency tables
      • Marginal probabilities
    • Check the conditions associated with a goodness-of-fit test
    • Check the conditions associated with a test of independence
    • Check the conditions associated with a test of homogeneity
    • Perform a goodness-of-fit test about the probability distribution of a random variable
    • Determine whether two classifications of nominal data are independent using a contingency table, multinomial probabilities, and a chi-square test
    • Using technology, input sample data, choose commands to perform an appropriate chi-square test and interpret the output
  7. Simple linear regression and correlation (The instructional goal is to explore a straight-line relationship between two random variables, and use the least-squares line as a basis for inference about a population)
    • Identify the explanatory variable and the response variable
    • Check the conditions associated with constructing a least-squares linear regression model
    • Construct a scatter plot of the sample data
    • Identify the least-squares estimates of the intercept and the slope population parameters
    • Specify the probability distribution of the random error term, and estimate the standard deviation of this distribution
    • Evaluate the utility of the model:
      • Conduct a test of significance to determine whether the data provide sufficient evidence to indicate that the explanatory variable contributes information for the linear prediction of the response variable
      • Construct and interpret a confidence interval to estimate the slope of the population regression model
      • Calculate and interpret the sample correlation coefficient
      • Calculate and interpret the sample coefficient of determination
    • Use the least-squares line for estimation and prediction:
      • Construct and interpret a confidence interval for the mean value of the response value when the explanatory variable takes on a specific value
      • Construct and interpret a prediction interval for an individual value of the response value when the explanatory variable takes on a specific value
    • Using technology, input sample data and execute the commands to produce a least-squares regression equation, a fitted line, a residual plot, and interpret the output

Outcome #3: Read with understanding scholarly publications and critically assess public dissemination of statistical information.

To address this outcome, the following shall be taught with an emphasis on the application of the included statistical concepts and procedures to the understanding of scholarly publications and to the critical assessment of public dissemination of statistical information:

  1. Random variable and probability distributions (the instructional goal is to explore and analyze random variables and probability distributions)
    • Explore probability distributions:
      • Normal
      • Student’s t
      • F
      • Chi-square
  2. Estimation: one sample (the instructional goal is to estimate a population parameter by calculating a confidence interval)
    • Identify and describe terminology:
      • Confidence coefficient
      • Confidence level
      • Confidence interval
    • Check the conditions associated with a confidence interval for a population mean
    • Check the conditions associated with a confidence interval for a population proportion
    • Calculate and interpret a confidence interval for a population mean
    • Calculate and interpret a confidence interval for a population proportion
    • Calculate a sample size to attain a desired margin of error and confidence level
    • Using technology, input a sample and execute the commands to create a confidence interval; interpret the output
  3. Statistical inference: one sample (the instructional goal is to utilize sample information to test whether a population parameter is less than, not equal to, or greater than a specified value)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Calculate a sample size
    • Identify and describe terminology:
      • Null and alternative hypotheses
      • Test statistic
      • Distinguish between significance and hypothesis testing
      • Type I and Type II errors
      • Observed significance level: P-value
    • Check the conditions associated with a test of significance about a population mean
    • Check the conditions associated with a test of significance about a population proportion
    • Construct and interpret a z-test about a population mean
    • Construct and interpret a t-test about a population mean
    • Construct and interpret a z-test about a population proportion
    • Using technology, input a sample and execute the commands to perform a t-test or a z-test and interpret the output
    • Calculate and interpret the power of a z-test
  4. Estimation and statistical inference: two samples (the instructional goal is to utilize sample information to infer whether a difference exists between two population means or two population proportions)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population proportions using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using paired samples
    • Construct and interpret a confidence interval about differences between two population means using two independent samples
    • Construct and interpret a t-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population proportions using two independent samples
    • Construct and interpret a confidence interval about the mean difference between two populations using paired samples
    • Construct and interpret a t-test about the mean difference between two populations using paired samples
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of means test; interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions confidence interval and interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions test, and interpret the output
    • Using technology, input two paired samples and execute the commands to perform a one-sample t-test and interpret the output
  5. Analysis of variance, ANOVA (The instructional goal is to design and analyze a sampling experiment to compare the means of more than two populations)
    • Identify and describe technology:
      • Response (dependent) variable
      • Factor (independent variable, stimulus)
      • Levels (treatments) of a factor
      • Sum of squares for treatments (SST) and error (SSE)
      • Mean square for treatments (MST) and error (MSE)
    • Check the conditions associated with an ANOVA
    • Compare the treatment means
    • Summarize the results of an F-test in an ANOVA table.
    • Using technology, input sample data and execute the commands to perform ANOVA and interpret the output
    • Use a multiple comparisons method to determine which pairs of means differ and interpret the results
  6. Chi-square tests and contingency tables (the instructional goal is to explore a non-parametric procedure on categorical variables)
    • Identify and describe terminology:
      • Multinomial probabilities
      • Contingency tables
      • Marginal probabilities
    • Check the conditions associated with a goodness-of-fit test
    • Check the conditions associated with a test of independence
    • Check the conditions associated with a test of homogeneity
    • Perform a goodness-of-fit test about the probability distribution of a random variable
    • Determine whether two classifications of nominal data are independent using a contingency table, multinomial probabilities, and a chi-square test
    • Using technology, input sample data, choose commands to perform an appropriate chi-square test and interpret the output
  7. Simple linear regression and correlation (The instructional goal is to explore a straight-line relationship between two random variables, and use the least-squares line as a basis for inference about a population)
    • Identify the explanatory variable and the response variable
    • Check the conditions associated with constructing a least-squares linear regression model
    • Construct a scatter plot of the sample data
    • Identify the least-squares estimates of the intercept and the slope population parameters
    • Specify the probability distribution of the random error term, and estimate the standard deviation of this distribution
    • Evaluate the utility of the model:
      • Conduct a test of significance to determine whether the data provide sufficient evidence to indicate that the explanatory variable contributes information for the linear prediction of the response variable
      • Construct and interpret a confidence interval to estimate the slope of the population regression model
      • Calculate and interpret the sample correlation coefficient
      • Calculate and interpret the sample coefficient of determination
    • Use the least-squares line for estimation and prediction:
      • Construct and interpret a confidence interval for the mean value of the response value when the explanatory variable takes on a specific value
      • Construct and interpret a prediction interval for an individual value of the response value when the explanatory variable takes on a specific value
    • Using technology, input sample data and execute the commands to produce a least-squares regression equation, a fitted line, a residual plot, and interpret the output

Outcome #4: Adapt statistical techniques and reasoning to other disciplines and vocations.

To address this outcome, the following shall be taught with an emphasis on the adaptation on the included statistical techniques and forms of reasoning to other disciplines and vocations:

  1. Random variable and probability distributions (the instructional goal is to explore and analyze random variables and probability distributions)
    • Explore probability distributions:
      • Normal
      • Student’s t
      • F
      • Chi-square
  2. Estimation: one sample (the instructional goal is to estimate a population parameter by calculating a confidence interval)
    • Identify and describe terminology:
      • Confidence coefficient
      • Confidence level
      • Confidence interval
    • Check the conditions associated with a confidence interval for a population mean
    • Check the conditions associated with a confidence interval for a population proportion
    • Calculate and interpret a confidence interval for a population mean
    • Calculate and interpret a confidence interval for a population proportion
    • Calculate a sample size to attain a desired margin of error and confidence level
    • Using technology, input a sample and execute the commands to create a confidence interval; interpret the output
  3. Statistical inference: one sample (the instructional goal is to utilize sample information to test whether a population parameter is less than, not equal to, or greater than a specified value)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Calculate a sample size
    • Identify and describe terminology:
      • Null and alternative hypotheses
      • Test statistic
      • Distinguish between significance and hypothesis testing
      • Type I and Type II errors
      • Observed significance level: P-value
    • Check the conditions associated with a test of significance about a population mean
    • Check the conditions associated with a test of significance about a population proportion
    • Construct and interpret a z-test about a population mean
    • Construct and interpret a t-test about a population mean
    • Construct and interpret a z-test about a population proportion
    • Using technology, input a sample and execute the commands to perform a t-test or a z-test and interpret the output
    • Calculate and interpret the power of a z-test
  4. Estimation and statistical inference: two samples (the instructional goal is to utilize sample information to infer whether a difference exists between two population means or two population proportions)
    • Perform a two-sided test using:
      • A test of significance
      • A confidence interval
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population proportions using two independent samples
    • Check the conditions associated with a confidence interval or test of significance about the difference between two population means using paired samples
    • Construct and interpret a confidence interval about differences between two population means using two independent samples
    • Construct and interpret a t-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population means using two independent samples
    • Construct and interpret a confidence interval about the difference between two population proportions using two independent samples
    • Construct and interpret a z-test about the difference between two population proportions using two independent samples
    • Construct and interpret a confidence interval about the mean difference between two populations using paired samples
    • Construct and interpret a t-test about the mean difference between two populations using paired samples
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of means test; interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions confidence interval and interpret the output
    • Using technology, input two independent samples and execute the commands to perform a two-sample difference of proportions test, and interpret the output
    • Using technology, input two paired samples and execute the commands to perform a one-sample t-test and interpret the output
  5. Analysis of variance, ANOVA (The instructional goal is to design and analyze a sampling experiment to compare the means of more than two populations)
    • Identify and describe technology:
      • Response (dependent) variable
      • Factor (independent variable, stimulus)
      • Levels (treatments) of a factor
      • Sum of squares for treatments (SST) and error (SSE)
      • Mean square for treatments (MST) and error (MSE)
    • Check the conditions associated with an ANOVA
    • Compare the treatment means
    • Summarize the results of an F-test in an ANOVA table.
    • Using technology, input sample data and execute the commands to perform ANOVA and interpret the output
    • Use a multiple comparisons method to determine which pairs of means differ and interpret the results
  6. Chi-square tests and contingency tables (the instructional goal is to explore a non-parametric procedure on categorical variables)
    • Identify and describe terminology:
      • Multinomial probabilities
      • Contingency tables
      • Marginal probabilities
    • Check the conditions associated with a goodness-of-fit test
    • Check the conditions associated with a test of independence
    • Check the conditions associated with a test of homogeneity
    • Perform a goodness-of-fit test about the probability distribution of a random variable
    • Determine whether two classifications of nominal data are independent using a contingency table, multinomial probabilities, and a chi-square test
    • Using technology, input sample data, choose commands to perform an appropriate chi-square test and interpret the output
  7. Simple linear regression and correlation (The instructional goal is to explore a straight-line relationship between two random variables, and use the least-squares line as a basis for inference about a population)
    • Identify the explanatory variable and the response variable
    • Check the conditions associated with constructing a least-squares linear regression model
    • Construct a scatter plot of the sample data
    • Identify the least-squares estimates of the intercept and the slope population parameters
    • Specify the probability distribution of the random error term, and estimate the standard deviation of this distribution
    • Evaluate the utility of the model:
      • Conduct a test of significance to determine whether the data provide sufficient evidence to indicate that the explanatory variable contributes information for the linear prediction of the response variable
      • Construct and interpret a confidence interval to estimate the slope of the population regression model
      • Calculate and interpret the sample correlation coefficient
      • Calculate and interpret the sample coefficient of determination
    • Use the least-squares line for estimation and prediction:
      • Construct and interpret a confidence interval for the mean value of the response value when the explanatory variable takes on a specific value
      • Construct and interpret a prediction interval for an individual value of the response value when the explanatory variable takes on a specific value
    • Using technology, input sample data and execute the commands to produce a least-squares regression equation, a fitted line, a residual plot, and interpret the output

Suggested Texts and Materials

Moore, D. et al., Introduction to the Practice of Statistics, 7th Ed., W. H. Freeman, 2012.

Department Notes

This is the second term of a two-term sequence (STAT 243Z and STAT 244). This course is intended to provide an introduction to statistics in a data-based setting.