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Statistics

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)

The Trainers
Ms. Jun Guan
Senior Data Analyst
Senior Biostatistian
Mater Degree in Statistics, U of T

Ms. Joan Lin
Ph.D. in Computer Application
Senior Scientisit
Senior Statistian
The Achievements
Achievement
Consultation

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