MATH 190 - Introductory Statistics

4 Credit: (4 lecture, 1 lab, 0 clinical) 5 Contact Hours:

1. Use appropriate statistical language in oral, written, and graphical forms.
2. Read and interpret graphs and descriptive statistics.          
3. Read short, authentic texts, such as graphical displays and journal and newspaper articles describing statistical studies. 
4. Evaluate the design, analysis, and conclusion of a given study both orally and in written form. 
5. Identify errors-such as inappropriate sampling methods, sources of bias, and potentially confounding variables-in both observational and experimental studies.
6. Use some form of spreadsheet application to organize information, make repeated calculations using simple formulas and statistical functions.
7. Construct and interpret graphical displays of distributions of univariate data.
8. Summarize distributions of univariate data and compare multiple distributions.
9. Explore bivariate data.
10. Explore categorical data.

Goal 2:  The student will read, use, and interpret probability vocabulary, perform basic probability computations, and be able to use probability rules to calculate probability. 

1. Calculate and interpret probabilities of simple events. 
2. Calculate and interpret probabilities of compound events. 
3. Calculate and interpret conditional probabilities. 
4. Use tables graphical displays to solve probability problems.   

Goal 3:  The student will solve problems by applying appropriate probability distributions including, by not limited to, both discrete and continuous, including the binomial, uniform, and normal distributions.

1. Determine whether a given situation can be modeled by a discrete or continuous distribution.
2. Determine whether a given situation can be modeled by distirbutions including, but not limited to, the binomial, uniform, or normal distributions. 
3. Analyze the meaning of a situation being modeled by a distribution. 

Goal 4:  The student will use the Central Limit Theorem to model sampling distributions and compute probabilities based on sampling distributions.

1. Apply the Central Limit Theorem to sampling distributions.
2. Compute probabilities based on the sampling distributions. 

Goal 5:  The student will construct and interpret confidence intervals of proportions and means for one population and the difference of proportions and means for two populations.

1. Construct and interpret confidence intervals of proportions for one population.
2. Construct and interpret confidence intervals of means for one population.
3. Construct and interpet confidence intervals of the difference of proportions for two populations.
4. Construct and interpet confidence intervals of the difference of means for two populations. 
5. Use confidence intervals to make decisions. 
6. Estimate population parameters using confidence intervals when appropriate.

Goal 6:  The student will formulate and test hypotheses about parameters for both one and two populations for means and proportions.

1. Understand what statistical question is being addressed, use appropriate strategies to answer the question of interest, and state conclusions using appropriate statistical language.
2. Support conclusions by providing appropriate statistical justifications.
3. Present short written or verbal justifications of decisions that include appropriate discussion of the mathematics involved.
4. Use probability, graphical and numerical summaries of data, and hypothesis testing methods to make decisions.
5. Use technology to calculate descriptive statistics and to test hypotheses.
6. Understand what conclusions are appropriate for a given design and whether conclusions can be generalized to a larger population. 
7. Conduct tests of significance when appropriate.

Goal 7:  The student will analyze bivariate data by generating and interpreting scatter plots, line of best fit, the related r and r-squared values by using output from a statistical software package.

1. Generate and interpret scatter plots using technology.
2. Create a line of best fit for bivariate data using technology.
3. interpreting scatter plots, line of best fit, the related r, and r-squared values
4. Interpret and apply output from a statistical software package.