Statistics 50
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Statistics 50
Math 125

 

Syllabus Stat 50 – Elementary Statistics

Instructor: Dan Hurley

Course Description

Prerequisite(s): Math 131 or 132C or 133B or 134B. (Mathematics Division) Analysis of data relative to social and natural processes. Collecting, grouping and presenting numerical data by means of: frequency distributions, measures of central tendency and deviation, probability and sampling, measures of prediction and correlation, hypothesis testing. No credit if taken after Stat 15 or 18. Total of 90 hours lecture. Transfer Credit: CSU; UC credit limitations. See counselor. *CAN: STAT 2 Grading: Letter Grade or Credit/No Credit

Required Text:  Mario F. Triola,  Elementary Statistics, 11th edition, Addison Wesley   
or the PCC version of this at the bookstore

Web site:  http://www.mypearsonstore.com/bookstore/product.asp?isbn=0321500245

Tutoring: Call the hotline at (626) 575-7056

Calculator:  TI-83 or TI-83 Plus or TI-84 Plus The TI-84 is keystroke compatible with the TI-83.  It will be extremely difficult to complete this course if you do not buy this calculator.  I will assume for all exams and quizzes that you will have use of this type of calculator.  If you attempt to use ANY other calculator or computer, it is entirely your responsibility to learn to use it and any difficulties you encounter because of this choice will be your responsibility. 

 Calculator/Exam policy:  You will be able to use the TI-83/TI-84 on all exams.  You may not share or borrow any calculator during an exam or quiz.  If you forget your calculator, you will be doing the exam without one.  It is entirely your responsibility to have your calculator.  Due to academic honestly issues, you MAY NOT SHARE OR BORROW calculators during exams and quizzes.  I do not lend mine for use on exams or quizzes.
Cell phone use is not permitted during exams. Students must have a valid id at each exam.

Grading:                 Homework and attendance 5%

                                Exams   60%

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

Grading Scale:  A>=90%, B>=80%, C>=70%, D>=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 are responsible for lecture material and announcements given during 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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Objectives: 

  1. Statistically describe sets of data.

  2. Apply basic laws of probability

  3. Formulate a probability distribution.

  4. Formulate and test hypothesis.  Testing of one, two and more than two populations.

  5. Formulate and analyze point and interval estimates for parameters.

  6. Find the correlation between two variables and the linear regression equation describing the relation between two variables.

  Entry Level Skills: Skills you need to have known prior to enrollment in this course

  1. Solve linear and non-linear equations.

  2. Simplify advanced numerical expressions (order of operations.)

  3. Plot and interpret points on Cartesian coordinate system.

  4. Plot linear equations using slope-intercept method.

  5. Translate verbally stated problems into appropriate mathematical forms.

  6. Solve absolute value equations and inequalities in a single variable.

  7. Evaluate an exponential function.

  8. Solve literal equations for designated variables.

  9. Evaluate complex numerical expressions.

  10. Given the description of a line, write the equation of the line.

  11. Express the solution to an inequality using interval notation

        Exit Level Skills: Skills to be learned during this course

  1. Statistically describe sets of data.

  2. Apply basic laws of probability

  3. Formulate a probability distribution

  4. Formulate and test null hypothesis of one, two or more populations.

  5. Make point and interval estimates of parameters.

  6. Identify correlation between two variables and a linear relation between them.

  7. Use statistical functions on a calculator.

  8. Critically evaluate statistical claims.