Download 0301131 Principles of Statistics

The Jordan University
Mathematics Department
Course Description of Principles of Statistics (0301131)
Textbook: Raqab, M. Z., Awad, A. M., and Azzam, M.M (2010), Principles of Statistics , 3 rd edition,
Academic For Publishing & Distributing Co.
References:
1. Introduction to Probability and Statistics, 11th edition, W. Mendenhall, R. Beaver
and B. Beaver, 2003, Brooks/Cole
2. Introduction to Probability and Statistics, Principles and Methods, 3rd Edition, R.
A. Johnson and G. K. Bhattacharyya, 1996, Wiley, New York.
Course Outline
Lecture
Ch1
1
2
3
4
5
6
7
8
9
10
11
Ch2
12
13
14
15
16
17
18
19
Ch3
20
Topics of each 50 m. lecture
Descriptive Statistics:
Frequency table for discrete data, stem-and-leaf
Frequency table for continuous data, relative and cumulative frequency
Graphical presentation of discrete data: dot plot, line diagram, bar chart, pie chart
Graphical presentation of continuous data: histogram, frequency polygon, cumulative
frequency curve
Measures of location of raw data: arithmetic mean, median, mode, percentiles,
trimmed and Winsorized means
Measures of location of grouped data: arithmetic mean, median, mode, percentiles,
quartiles
Measures of variation for raw data: range, interquartile range, standard deviation,
mean deviation, median deviation.
Measures of variation for grouped data: standard deviation, mean absolute deviation,
median deviation, interquartile range, Box-Whisker plot.
Z-score, coefficient of variation, skewness, kurtosis, Chebyshev inequality, empirical
rule.
Updating mean and variance due to combining two data collections, linear
transformation of data, dropping, adding or correcting data points
Discussion 1
Elements of Probability:
Random experiment, sample space, event, sampling with and without replacement,
counting number of elements in sample space and of events using counting principle,
combination and permutation.
Probability concept and its rules
Conditional probability , independent events and Bayes rule
Univariate discrete random variable, pdf, cdf, expectation, mean , and variance
Bivariate discrete random variables, joint, marginal and conditional pdfs,
Expectation, mean , variance, covariance, and correlation, independence of bivariate
random vector.
Univariate continuous random variable, pdf, cdf, expectation, mean, variance.
Discussion 2
Discrete probability distributions
Binomial distribution: Bernoulli random experiment, pdf, binomial table, mean and
Notes
21
Ch4
22
23
24
Ch5
25
26
27
28
Ch6
29
30
variance
Poisson distribution: experiments leading to the distribution, pdf, mean, variance,
Poisson table, Poisson approximation to binomial
Normal Probability Distribution
Standard normal, pdf, tables to calculate probabilities and percentiles
General normal and converting to Z : N(0,1), using tables
Normal approximation to Binomial and Poisson distributions, CLT
Sampling Distributions
Sampling distribution of sample mean and of difference between two sample means
(normal or T), degrees of freedom, using t-table
Sampling distribution of proportion and difference between two proportions (normal)
Sampling distribution of sample variance (Chi-square), and of ratio of two variances
(F-distribution) using chi-square and F- tables
Discussion 3
Concepts of Statistical Inference
Point estimates for population mean, proportion, variance, Confidence interval for
population mean, sample size determination
Testing terminology: null and alternative hypotheses, simple, one-sided and two-sided
composite hypotheses, test statistic, test rule, level of significance, p-vale, type-I and
type-II errors and their probabilities
 ,  , & p value
31
Calculations of
Ch7
32
39
40
Inference about one population parameters
Tests about population mean ( small and large samples, known and unknown
variances)
Testing and CI for proportion (large sample), sample size determination
Testing and CI for variance, relation between testing and CI’s
Discussion 4
Inference about two populations parameters
Tests about difference of two independent populations means ( small and large
samples, known and unknown variances)
CI for difference of two independent populations means ( small and large samples,
known and unknown variances).
Tests and C.I. for difference of two dependent populations (paired) means
Tests and C.I. for difference of two independent proportions (large samples)
Tests and C.I. for ratio of two independent variances
Discussion 5
41
42
43
Review before Mid Term
Mid Term
Solution of Mid Term
44
45
46
Review before 2nd Exam
2nd Exam
Solution of 2nd Exam
33
34
35
Ch8
36
37
38
Evaluation: Mid term 30 %
Grade Scale:
F
DD
D+
0-34 35-44 45-49 50-54
2nd Exam: 20%
C55-59
C
60-64
C+
65-69
B70-74
Final: 50%
B
75-79
B+
80-84
A85-89
A
90-100