1.Data
and Statistics
Data sets & sources of data
Elements v. variables v. observations
Qualitative v. quantitative data
Scales of measurement (nominal, ordinal,
interval & ratio)
Crosssectional, time series, &
descriptive statistics
Define samples v. populations; population
v. parameter v. sample v. statistics
Data acquisition interpretation and
statistical inference
2 Descriptive Statistics
Frequency & relative frequency
distributions
Cumulative frequency & cumulative
relative frequency distributions
Data presentations  bar graphs, pie
charts, histograms, ogive, and Stemnleaf.
Numerical measures of location,
dispersion.
Sample statistics, population parameters
& point estimators
Measures of central location  mean,
median, mode, percentiles & quartiles
Measures of variability  range, interquartile
range, variance, standard deviation.
3.Introduction to
Probability
Experiments  sample space & sample
points
Methods of assigning probabilities 
classical, relative frequency &
subjective
Formulas for estimating probabilities
Basic relationships of probability 
complement events
Conditional probability  joint &
marginal probabilities; independent
events; multiplication
4 Discrete
Probability Distributions
Descrete Vs. Continuous Random Variables.
Binomial experiments, experimental
outcomes, probability function, expected
value & variance
Excel worksheets for computing binomial
properties, value & variance (BINOMDIST)
5. Continuous Probability
Distributions
Continuous variables (not discrete) 
difference in ways of computing
probabilities
Normal probability distribution
Computation of z & x values
6. Sampling and Sampling
Distributions
Definitions of simple random sample;
sampling distribution & point
estimation.
Point estimation
Sampling distributions,
7 Interval Estimation
Definitions of confidence interval, alpha,
sampling error, confidence level,
standard error
Subtracting and adding the margin of
error to the point estimate
Confidence intervals & estimates for
population means & proportions
Level of significance & confidence
coefficient
Effects of sample size, margin of error
& confidence
Different t distributions for different
cases & degrees of freedom
Implications of statistical findings
8. Hypothesis Testing
Definitions of null & alternative
hypotheses, type I & II error,
critical value, level of significance,
Development of null & alternative
hypotheses
Analysis of sample data
Evaluation of conclusions
Steps of hypothesis testing; using the
test statistic, p value & critical
values
9 Comparisons about two
populations
Properties of sampling distributions;
independent v. matched samples
Point estimators of differences in means
Expected value & standard deviation
of M1  M2
Pooled variance estimators & point
estimators
Hypothesis tests about populations
Contingency tables to test for
independence
Test of independence contingency tables,
expected frequencies, test statistic
10. Simple Linear Regression
Analysis
Scatter diagrams
Interpretation of covariance &
correlation as measures of association
between variables
Simple linear regression to model the
relationship
Method of least squares 
Coefficient of determination
Model assumptions  error term &
distribution values and shapes
Testing for significance
Cautions interpretation of significance
tests
Estimation & prediction
Residual analysis
11. Multiple Regression Analysis
Dependent v. independent;
multicollinearity among independent
variables
Multiple regression model
Coefficient of determination
Testing for significance
Multiple coefficient of determination
Tests for significance  Ftest, overall
significance, test statistic, rejection
rule, ANOVA
ttest; multicollinearity impact on
interpreting results
Estimation & prediction estimated
regression equation (forecast y based on
a new x vector)
