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# MAT 162 - Prin. of Business Statistics

Credits: 4
Lecture Hours: 3
Lab Hours: 2
Practicum Hours: 0
Work Experience: 0
Course Type: Core
Make inferences about population parameters. Conduct regression inferential analyses. Obtain, present and organize statistical data using measures of location and dispersion; the Normal distribution; sampling distributions; estimation and confidence intervals; inference for simple linear regression analysis. Use computers to visualize and analyze data.
Prerequisite: Minimum ALEKS scores of 46% or MAT 073  with a C- or better.
Competencies
1. Examine data distributions
1. Display distributions graphically
2. Describe distributions numerically
3. Use data to describe a population with a normal distribution
4. Introduce statistical inference
2. Examine relationships between variables
1. Display the relationship graphically with a scatterplot
2. Describe the relationship numerically with a correlation coefficient
3. Use least-squares linear regression to examine the relationship
4. Use data to conduct a regression analysis
5. Explain the advantages and limitations of correlation and regression for describing the relationship between variables
3. Consider population parameters and probability concepts
1. Examine probability.
2. Examine population parameters
3. Use probability to test parameters for significance
4. Infer conclusions about population parameters
1. Explore inferences about populations means
2. Examine matched pairs data
3. Explore inferences about the sample proportion
4. Determine the appropriate sample size for a stated margin of error
5. Explore regression inferential analyses
1. Define simple linear regression
2. Estimate least-squares linear regression parameters
3. Define and calculate the standard error estimate of the regression model?s standard deviation
4. State and discuss the conditions for regression inference
5. Define and discuss the sampling distribution of the regression parameter estimates
6. Conduct a test for zero population correlation
7. Examine the mean response and predict the value of an individual response
8. Conduct a preliminary data analysis for multiple regression
9. Determine the multiple regression equation using least-square to estimate coefficients
10. Examine multiple regression residuals
11. Calculate and discuss the multiple regression standard error
12. Use statistical software to perform a multiple regression analysis
13. Write a summary of a regression analysis including hypotheses, statistics used, results of tests, and inferences made