  
Student Learning Outcomes 
 Students will formulate conclusions about a population based on analysis of sample data.
 Students will develop conceptual understanding of descriptive and inferential statistics. They will demonstrate and communicate this understanding in a variety of ways, such as: reasoning with definitions and theorems, connecting concepts, and connecting multiple representations, as appropriate.
 Students will demonstrate the ability to compute descriptive statistics, calculate confidence intervals, and carry out tests of hypotheses.

Description  
 An introduction to modern methods of descriptive statistics, including collection and presentation of data; measures of central tendency and dispersion; probability; sampling distributions; hypothesis testing and statistical inference; linear regression and correlation; analysis of variance; use of microcomputers for statistical calculations. Illustrations taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science.


Course Objectives  
 The student will be able to:
 distinguish between quantitative and qualitative data; levels/scales of measurement; sample and population; descriptive statistics and inferential statistics and their implications.
 Identify the standard methods of obtaining data and identify advantages and disadvantages of each
 read a graph and conclude what information the graph is conveying about the data.
 calculate measures of central tendency, dispersion and relative standing and use these measures to solve application problems.
 compute basic probabilities and apply concepts of sample space.
 define discrete probability distributions; calculate the mean, variance and standard deviation of a discrete distribution; and use such distributions to solve application problems.
 define continuous probability distributions; calculate probabilities using the normal and student tdistributions; and use such distributions to solve application problems.
 define sampling distributions, state the Central Limit Theorem and use sampling distributions and the Central Limit Theorem to solve application problems.
 use confidence intervals to estimate population parameters, or the difference between two population parameters, using the appropriate formula and then interpret the result.
 determine the sample size required to estimate a population parameter.
 design, set up, and evaluate the results of hypothesis tests; determine and interpret levels of statistical significance including pvalues in hypothesis tests; and identify type I and type II errors.
 compare and contrast the use of confidence intervals and hypothesis tests to make inferences about population parameters.
 solve application problems utilizing techniques of regression and correlation.
 use analysis of variance to make inferences about more than two population means.
 solve application problems using categorical data analysis.
 demonstrate statistical understanding of inference by participating in a cooperative project.
 demonstrate proficiency in the use of the computer as a tool for doing statistics
 apply statistical methods to situations in a culturally diverse society including applications from business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science.
 discuss mathematical problems and write solutions in accurate mathematical language and notation.
 interpret mathematical solutions.

Special Facilities and/or Equipment  
  Graphing calculator
 Access to Microsoft Excel software
 When taught on Foothill Global Access: ongoing access to a computer with email software and email address.

Course Content (Body of knowledge)  
  Organization of Data
 Definitions
 population
 sample
 variables
 descriptive statistics
 inferential statistics
 levels/scales of measurement
 implications of levels/scales of measurement
 Frequency and relative frequency distributions
 Identify the standard methods of obtaining data and identify advantages and disadvantages of each
 Sampling Methods
 simple random
 stratified
 cluster
 systematic
 convenience
 advantages/disadvantages of different sampling methodologies
 Graphs and charts
 histograms
 piecharts
 stemandleaf graphs
 bar charts
 Pareto charts
 box plots
 dot plots
 ogives
 timeseries
 graph shapes
 Measures of Central Tendency and Dispersion
 Summation notion
 Measures of central tendency
 mean
 median
 mode
 Measures of dispersion
 range
 sample variance
 sample standard deviation
 coefficient of variation
 Chebyshev's Theorem
 Percentiles and Quartiles
 Probability
 Empirical probability
 Sample spaces and events
 addition rule
 mutually exclusive events
 complementary events
 applications
 Conditional probability
 independent events
 multiplication rule
 Discrete Probability Distributions
 Definition of random variables
 Discrete random variables
 mean of a discrete distribution
 variance of a discrete distribution
 standard deviation of a discrete distribution
 Properties of a probability distribution function
 The Binomial distribution
 the binomial probability distribution function
 mean
 variance
 standard deviation
 Applications taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science
 Continuous Probability Distributions
 Continuous random variables; equating area under a curve with probability
 Empirical Rule
 The normal distribution
 standardizing normal curves (zscores)
 finding zscores from areas under the standard normal curve
 calculating probabilities using normal distribution
 Applications taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science
 The normal approximation to the binomial distribution
 requirements
 adjusting the interval of the variable from discrete to continuous
 Sampling Distributions
 Sampling distribution of the mean
 mean
 standard deviation
 shape
 Central Limit Theorem
 Estimation
 Margin of Error
 Point estimation; biased and unbiased estimator
 Confidence interval for the mean when the variance is known
 maximal margin of error
 sample size for estimating the mean
 Confidence interval for the mean when the population variance is unknown
 maximal margin of error
 students tdistribution
 degrees of freedom
 Confidence interval for the population proportion
 maximal margin of error
 sample size for estimating the proportion
 Confidence interval for the difference between two means when population variances are known
 maximal margin of error
 Confidence interval for the difference between two means when population variances are unknown, but assumed unequal
 maximal margin of error
 Confidence interval for the difference between two means when population variances are unknown, but assumed equal
 maximal margin of error
 Confidence interval for the difference between two means when the samples are dependent
 Confidence interval for the difference between population proportions
 maximal margin of error
 Applications taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science
 Sample size
 sample size for estimating the mean
 sample size for estimating the binomial probability of success
 Hypothesis Testing
 Vocabulary
 null hypothesis
 alternate hypothesis
 right, left, and twotailed tests
 Mechanics of hypothesis testing
 type I error
 type II error
 pvalue
 calculating pvalues using the normal distribution
 calculating pvalues using the students tdistribution
 interpreting pvalues
 test statistic
 decision rule
 rejection and acceptance region
 interpret levels of statistical significance including pvalues
 Singlepopulation hypothesis testing
 for the population mean when the variance is known
 for the population mean when the variance is unknown
 testing population proportion
 Twopopulation hypothesis testing
 comparing two population means when the population variances are known
 comparing two population means when the population variances are unknown, but assumed equal
 comparing two population means when the population variances are unknown, but assumed unequal
 dependent samples
 testing difference in population proportions
 testing the variance
 Applications taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science
 Comparison of Hypothesis Tests and Confidence Intervals
 connection between hypothesis testing and confidence intervals
 statistical significance in confidence intervals and hypothesis tests
 Linear Regression and Linear Correlation
 Linear relations
 Linear regression
 scatter diagrams
 method of least squares
 regression analysis
 coefficient of determination
 Linear correlation
 Applications taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science
 One Way Analysis of Variance (ANOVA)
 Methodology
 Fdistribution
 Tukey pairwise comparisons
 Applications taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science
 Chi Square Tests
 Contingency tables
 Chisquare distribution
 Tests for dependence of categorical variables
 Tests for homogeneity
 Goodness of fit
 Applications taken from the fields of business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science
 Cooperative Project
 Hypothesis testing
 Confidence intervals
 Graphs
 Statistical inference
 Sampling methods
 Data analysis
 Computer as a Tool for Doing Statistics
 Statistical analysis using technology such as SPSS, EXCEL, Minitab, or graphing calculators.
 apply statistical methods to situations in a culturally diverse society including applications from business, economics, medicine, engineering, education, psychology, sociology, social sciences, life science, and health science.
 Examples used will be from different societies and cultures
 Discuss mathematical problems and write solutions in accurate mathematical language and notation.
 Application problems from other disciplines
 Proper notation
 Interpret mathematical solutions.
 Explain the significance of solutions to application problems.

Methods of Evaluation  
  Homework
 Quizzes, midterm exams
 Computer lab assignments
 Cooperative project
 Proctored comprehensive final examination: the final exam must be taken in person at the Los Altos Hills campus or at another approved facility administered by a proctor deemed acceptable by the instructor.

Representative Text(s)  
 Beoga.net Inc. Elementary Statistics. V4. 2014. Brase, H. and C. Brase. Understandable Statistics: Concepts and Methods, 10th ed. Cengage Learning, 2011. When taught on Foothill Global Access: lectures, handouts, and assignments are delivered via email and/or the world wide web.

Disciplines  
 Mathematics


Method of Instruction  
 Lecture, Discussion, Cooperative learning exercises.


Lab Content  
 Not applicable.


Types and/or Examples of Required Reading, Writing and Outside of Class Assignments  
  Homework Problems: Homework problems covering subject matter from text and related material ranging from 30  60 problems per week. Students will need to employ critical thinking in order to complete assignments.
 Lecture: Five hours per week of lecture covering subject matter from text and related material. Reading and study of the textbook, related materials and notes.
 Projects: Student projects covering subject matter from textbook and related materials. Projects will require students to discuss mathematical problems,write solutions in accurate mathematical language and notation and interpret mathematical solutions. Projects may require the use of Excel.
 Worksheets: Problems and activities covering the subject matter. Such problems and activities will require students to think critically. Such worksheets may be completed both inside and/or outside of class.
