DEFINE descriptive and inferential statistics, discrete and continuous random variables, random sample, population parameter, qualitative and quantitative data, elementary probability concepts, range, null and alternative hypotheses, significance level, critical value, decision rule, rejection and acceptance regions, and test statistic

STATE why samples are used to infer information about a population

RECOGNIZE the limitations of statistical data

LIST properties of distributions: binomial, normal, t-distribution, Poisson, sampling, chi-square

DESCRIBE a data set with a frequency distribution, a relative frequency distribution, and a cumulative frequency distribution, the sampling distribution

 COMPREHENSION:

The student will

FIND measures of central tendency, variation, probabilities of simple and compound events, conditional probability, probabilities for the binomial, normal, t and sampling distributions, linear correlation and regression analysis

CONVERT raw scores to z scores

ACQUIRE understanding of simple random sampling, cluster, stratified, sequential, systematic sampling

STATE the central limit theorem and explain its importance for making statistical inferences

ILLUSTRATE a sampling distribution on a statistics computing system

EXPLAIN the advantages and disadvantages of using a frequency distribution to describe data

DISTINGUISH between discrete and continuous random variables;

descriptive and inferential statistics;

simple random, stratified, systematic, cluster, and sequential sampling techniques

INTERPRET measures for raw and grouped data for samples and populations;

central tendency: mean, median, mode;

variation: standard deviation, variance, quartiles, percentiles;

computer output for sample statistics;

computer output for estimation and hypothesis testing;

computer output for histograms, frequency polygons, ogives, boxplots, dotplots, pie charts, stem and leaf graphs;

a confidence interval for the mean of a population

a confidence interval for a population proportion;

conclusions of hypothesis testing

ESTIMATE confidence intervals for means of populations,

population parameters using sampling concepts

INFER conclusions from hypothesis testing procedures:

one-sample and two-sample tests;

analysis of variance test; nonparametric tests

PREDICT & DEMONSTRATE  the effects of a change of sample size or confidence level on the length of the confidence

interval

 APPLICATION:

The student will

DEMONSTRATE writing, computing, critical thinking and decision making skills using statistical analysis

COMPUTE mean, median, mode, standard deviation, variance and the z scores for a set of data;

probabilities for simple and compound events;

the linear correlation coefficient by using a scientific calculator and statistical computer software;

linear regression analysis;

test statistics for the normal, binomial, sampling, ANOVA and chi-square distributions;

the maximum error with confidence;

the sample size necessary to attain a given level of confidence that the error does not exceed a given magnitude;

standard error of the mean;

a p-value for a test for the mean and proportion;

a one-way and two-way ANOVA test statistic on a computer and interpret the results;

nonparametric tests: Sign, Wilcoxon-Mann-Whitney rank sum, …

DEVELOP an understanding of sampling concepts;

an understanding of statistical process control tools

USE the computer to generate a random numbers table and the central limit theorem;

the calculator and computer to compute statistical measures

IDENTIFY random variables; distributions

DIFFERENTIATE

among different types of variables and different types of measurement
 ANALYSIS:
The student will

DISTINGUISH between probability distributions

IDENTIFY TYPE I and TYPE II errors

PLOT the scatter diagram for paired data;

the box and whisker graph, stem and leaf graph, histogram, frequency polygon, pie chart, bar chart, ogive, quality control charts;

quality control graphs for variable and attribute data using a computer and interpret the meaning