MAT 175 – Introductory Statistics

1. Understand and be able to use graphs to describe data sets including use of frequency histogram, frequency polygon, ogive, and pie charts.
2. Calculate measures of central tendency including mean, median and mode.
3. Calculate measures of dispersion including range, variance, and standard
   deviation.
4. Know and be able to use counting techniques including the multiplication rule, permutations, and combinations.
5. Understand basic terminology and concepts of probability including sample spaces, complementary events, independent events, and mutually exclusive events.
6. Calculate probabilities and expected values including the addition rule, multiplication rule, and conditional probability.
7. Know properties of the Binomial Distribution and be able to calculate binomial probabilities.
8. Know properties of the Normal Distribution and be able to calculate probabilities using the Normal Distribution, central limit theorem, and normal approximation to the Binomial Distribution.
9. Know the different sampling techniques including random sampling, stratified random cluster, and systematic sampling.
10. Calculate confidence interval estimates for the mean of a population and for proportions.
11. Use hypotheses testing procedure including test of mean, proportion, difference between two means, and difference between proportions.
12. Use analysis of variance to test three or more means.
13. Know and understand methods of simple linear regression and correlation.
14. Use the Chi-Square Test to test for goodness-of-fit and independence.
15. (Optional) Be able to use Quality Control methods to monitor product quality including X-charts, R-charts, P-charts, and test for randomness.
16. Use a scientific calculator and/or computer software to perform statistical analyses and assist in decision-making.

Objectives for MAT 175, Introductory Statistics

1. Employ sampling techniques to collect data and summarize data using graphs and descriptive statistics.
2. Solve counting and probability applications.
3. Determine whether or not a variable conforms to a particular probability distribution including the binomial distribution, normal distribution, F-distribution, or t-distribution.
4. Compute confidence interval estimates and perform hypothesis tests pertaining to one and two samples. Test multiple samples via analysis of variance.
5. Perform a linear regression analysis.