and
y-intercept 
and
construct the equation of the regression line; TRI-COUNTY TECHNICAL COLLEGE
COURSE SYLLABUS
Course Prefix & Number: MAT 120_________________
Course Title: Probability and Statistics___
Class Hours Lab Hours Credits
Per week: __3_ Per Week: _0_ Awarded: _3_
Catalog Description: This course includes the following topics: introductory probability and statistics, including organization of data; sample space concepts; random variables; counting problems; binomial and normal distributions; central limit theorem; confidence intervals and tests of hypotheses for large and small samples; type I and II errors; linear regression and correlation.
Entry Level Skills: (1) basic algebraic manipulations, including equations and inequalities in one variable,
(2) the ability to think logically and follow step-by-step procedures.
Pre-requisites: Satisfactory math placement score
Co-requisites: None
Text(s)/Required Materials: Elementary Statistics by Mario F. Triola, 10th edition, Addison Wesley, 2007.
Equipment: Scientific calculator with one- and two-variable statistical functions, TI-83 or TI-83 Plus or TI-84 Plus recommended.
Course Competencies and Objectives/Major Course Topics:
Unit 1: Descriptive Statistics
Upon completion of this unit (sections 1-1 1-4, 2-1 2-4 and 3-1 3.5) the student should be able to:
1. distinguish between a population and a sample, and between a parameter and a statistic;
2. identify good and bad sampling techniques and their importance;
3. summarize data by constructing a frequency table;
4. construct graphs such as histograms, dotplots, box and whisker plots, stem-and-leaf plots, pie charts, and Pareto charts, and use these tools to describe, explore and compare data;
5. calculate measures of center including the mean, median, mode, and midrange;
6. calculate measures of variation including the standard deviation, variance, and range;
7. acquire a sense for the value of standard deviations using the range rule of thumb, empirical rule, and second Chebyshevs theorem as appropriate;
8. compare individual scores by using z scores and percentiles;
9. construct a box and whisker plot and use it to explore the nature of a distribution;
10. use the available statistical software to summarize and analyze data.
Unit 2: Probability Concepts and Distributions; Binomial Distributions
Upon completion of this unit (sections 4-1 4-4 [4-5 optional], 4-7, 5-1 5-4) the student should be able to:
1. use the relative frequency to approximate probability;
2. calculate probability for simple and compound events using the classical approach;
3. apply tree diagrams and Venn diagrams to illustrate sample spaces and probabilities;
4. use the basic properties of probability, and the complement of an event to aid in computing probability;
5. recognize events that are mutually exclusive and use the addition rule to calculate the probability of A or B;
6. determine whether events are independent, calculate conditional probabilities, and use the multiplication rule to calculate the probability of A and B;
7. use counting techniques such as factorials, permutations, and combinations to find the number of possible outcomes;
8. determine whether a given function on a discrete random variable satisfies the requirements for a probability distribution;
9. compute the mean (or expected value) and standard deviation for a discrete probability distribution;
10. determine whether a given procedure satisfies the requirements for a binomial experiment;
11. calculate the probability of an event in a binomial probability experiment;
12. calculate the mean and standard deviation for a binomial distribution.
Unit 3: Normal Distributions and Estimates
Upon completion of this unit (sections 6-1 6-7 and 7-1 7-4) the student should be able to:
1. use z scores and the standard normal distribution table to calculate probabilities for events in a normal distribution;
2. use the standard normal distribution table and z scores to find x values for given probabilities;
3. apply the central limit theorem to calculate probabilities associated with mean values;
4. use the normal distribution to approximate binomial probabilities;
5. examine a histogram, outliers, and a normal quantile plot to determine whether data have a normal distribution;
6. examine the sampling distribution of a statistic;
7. identify the sample mean as the best point estimate of the population mean;
8. demonstrate that some statistics (mean, variance, proportion) tend to target the population parameter while others do not;
9. construct a confidence interval for the population proportion based on whether σ is known or unknown;
10. determine the sample size necessary to produce a confidence interval accurate to a desired degree of confidence and margin of error.
Unit 4: Hypothesis Testing; Correlation and Regression
Upon completion of this unit (sections 8-1 8-5 and 10-1 10-3) the student should be able to:
1. determine the components of a hypothesis test: null hypothesis, alternative hypothesis, type of test (two-tailed, left-tailed, or right-tailed), type I error, type II error, test statistic, critical region, critical value, and statement of conclusion;
2. apply both the classical approach and the P-value approach to test a claim about the proportion of a single population;
3. apply both the classical approached the p-value approach to test a claim about the mean with σ known or unknown;
4. draw a scatter diagram and use its pattern to determine the relationship between x and y;
5. calculate and interpret the linear correlation coefficient r;
6.
calculate the slope and
y-intercept and
construct the equation of the regression line;
7. use the regression line to predict the value of a variable, given some value of the other variable.
Grade Calculation Method: Unit tests = 60 80 %
Projects = 0 20%
Homework/other = 0 10%
Final Exam = _ 20%__
100%
Additional Information Pertaining to Grades: A minimum grade of C is required for university transfer credit and recommended for progression to MAT 220.
Prepared by Debra Bindschatel______ Date written or revised: 03/03/2006_____