Download STA 3111-STATISTICS I-3cr

STA 3111-STATISTICS I-3cr
Prerequisite: High school algebra
Terms Offered: Fall, Spring and Summer
Text: Statistics, 11th Edition, by James T. McClave and Terry Sincich
1. Statistics, Data, and Statistical Thinking (Chapter 1)
Introduce the types of statistical methods: descriptive and inferential.
Define the basic concepts of Inferential Statistics: population, sample,
random sampling, parameter, and statistic. Discuss the role of Statistics in
critical thinking and the scientific method. Introduce the types of
variables/data and data collection methods.
2. Methods for Describing Sets of Data (Chapter 2: Sections 2.1-2.7)
Discuss frequency tables and graphs for qualitative and quantitative data.
Introduce the measures of central tendency and measures of variability.
Discuss their computation and interpretation. Introduce the Chebyshev and
Empirical rules. Discuss measures of relative standing.
3. Probability (Chapter 3: Sections 3.1-3.7)
Discuss the basic concepts in probability: random experiment, sample
space, event. Compound events: intersection and union of events,
complement of an event. Mutually exclusive events. Venn diagrams.
Probability model and rules. Conditional probability and independent
events. Random sample.
4. Discrete Random Variables (Chapter 4: Sections 4.1-4.4)
Define random variable and introduce the types of random variable.
Probability distributions for discrete random variables. Computing the
mean and standard deviation of a discrete random variable. Characteristics
of a binomial experiment. Introduce the Binomial random variable. Use of
the Binomial probability formula and tables to find the probability for
possible outcomes of a binomial variable. Word problems. Mean and
Standard deviation for a Binomial variable.
5. Continuous Random Variables (Chapter 5: Sections 5.1 and 5.3)
Introduce probability distributions for continuous random variables with
emphasis on the normal distribution. Characteristics of the Normal curve.
Interpreting areas under the normal curve as probabilities. Standard
Normal variable. Use of the Standard Normal table to find probabilities
(areas) and z-values. Areas under any Normal curve. Word problems.
6. Sampling Distributions (Chapter 6: Sections 6.1-6.3)
Define sampling distribution of a sample statistic. Desired properties of a
good estimator. Introduce the sampling distribution of the sample mean.
Properties and Central Limit Theorem. Maximum sampling error.
7. Inferences Based on A Single Sample: Estimation with confidence
intervals (Chapter 7: Sections 7.1-7.5)
Introduce the concepts of point estimate, interval estimate, and confidence
coefficient/level. Define confidence interval. Computing confidence
intervals for a population mean  based on both large and small samples.
Introduce the “t” distribution and table. Computing confidence intervals
for a population proportion (the binomial parameter “p”) using large
samples. Determining the appropriate sample size.
8. Inferences Based on A Single Sample: Tests of Hypothesis (Chapter 8:
Sections 8.1-8.5)
Discuss the elements of a test of hypothesis. Define Type I and Type II
errors. Perform tests of hypotheses about  based on both large and small
samples. Perform tests of hypotheses about p using large samples.
Introduce the concept of p-value (observed significance level of the test).
Computing and interpreting p-values.