Grading:                 Homework and attendance 5%

                                Exams   60%

                                Final Exam  35%  (Note: You must score at least 50% on the final to pass the class)

If you score at least 50% on the final then the following scale applies:  A>=90%, B>=80%, C>=70%, D>=60%, F<60%

Homework will be collected at the final exam.

Exams:    There will be 4 exams and 1 final exam.  There are no make-up exams.

Dropping scores: The lowest of the first 4 exam scores will be dropped. If you miss an exam, that is your lowest score.

Drop Policy 
If you decide to drop the course, it is your responsibility to take care of the necessary details in a timely manner.

Attendance:      Attendance at all classes is required (you will be dropped if you miss more that 4 classes).  Taping of lectures is not permitted.

Math Lab:  

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Student Learning Outcomes:

1.  Organize, describe, analyze, and interpret data through the use of statistical methods and research.

2.  Use statistical results to draw sound conclusions and make informed decisions.

3.  Apply the rules of probability and combinatorics to solve problems and interpret their results.

Student Performance Objectives:

1. Prepare frequency distribution, histograms and ogives from statistical information given in raw data (tallied) form.

2. Calculate (with a calculator), interpret and explain measures of dispersion and of central tendency.

3. Use the concepts of bivariate data to calculate linear correlation coefficient and interpret the significance of this coefficient using tables.

4. Perform a linear correlation and linear regression on bivariate data.

5. Use the rules of probability to calculate probabilities of outcomes to simple experiments involving complement, union, intersection, mutually exclusive events, independence and conditional probability.

6. Use the concept of random variables to prepare a probability distribution for a discrete random variable, and use the binomial distribution to solve problems in probability, and use the normal approximation to the binomial distribution.

7. Use the normal distribution to solve problems in sampling theory, applying the central limit theorem as needed.

8. Perform hypothesis tests, confidence interval calculations and calculations of sample size for inferences about population means, proportions and variances.

9. Perform multinomial experiments using goodness-of-fit.

10. Analyze contingency tables for independence and homogeniality.

11. Perform calculations on one-way analysis of variance.

Course Content Outline:

Learn Organization of Data: Forming frequency distributions.

Presentation of Data: Tabular, graphical - bar graph, frequency polygon line graph.

Use Summation Notation.

Analysis of Data - With or Without Frequency

Distribution: Measures of central tendency, mean, median, mode; measures of dispersion, range, mean deviation, standard deviation.

Elementary Probability: Definition - discrete vs. continuous probability distributions; elementary laws; conditional probability.

Binomial Distribution: Mean, standard deviation; theoretical frequency distribution.

Normal Distribution: As an approximation to the binomial distribution; as the limit of a frequency distribution of a continuous variable; use of tables of area under the normal probability curve.

Random Sampling (Large Samples): Distribution of sample mean: distribution of the difference between two-sample means.

Hypothesis-Testing (Large Samples): One and two-tailed tests; confidence limits.

Small Sample Methods: Students t-distribution; distribution of the difference between the means.

Non-Parametric Statistics: Chi-Square, rare order correlation

Inference from two samples - two proportions, two means - independent samples, inferences from matched pairs, comparing variation in two samples.

Linear correlation, linear regression.

Multinomial experiments and contingency tables

Analysis of variance

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