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)
Cross-sectional, 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 Stem-n-leaf.
Numerical measures of location,
dispersion.
Sample statistics, population parameters
& point estimators
Measures of central location - mean,
median, mode, percentiles & quartiles
Measures of variability - range, inter-quartile
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 - F-test, overall
significance, test statistic, rejection
rule, ANOVA
t-test; multicollinearity impact on
interpreting results
Estimation & prediction estimated
regression equation (forecast y based on
a new x vector)
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