ST. FRANCIS' CANOSSIAN COLLEGE
MATHEMATICS DEPARTMENT
CURRICULUM PLANNING (2003-2004)
SUBJECT
SUBJECT TEACHER
Month
Sep 2003
(cycles 1-2)
Sep
(cycles 2-3)
Ch.
Ch
10,
11
: Mathematics & Statistics
: Ms. K. Lee, Ms. Q. Kwok
Objectives
Basic statistical measures and their interpretation
1. To learn some ways of measuring central tendency and
dispersion of distributions
2. To be able to infer from these measures
3. To be able to construct and interpret graphical
representations of distributions, including stem-and-leaf
diagrams
12
1.
2.
3.
4.
5.
Sep - Oct
(cycles 4-5)
CLASSES
: F.7A, F.7S
TEXT BOOK : New Way Maths. & Stat. for HK AS-Level VOL. 2 (second edition)
13
1.
2.
3.
4.
Sample space, event and probability of an event
To understand the set notation for application in
probability
To understand the meaning of sample space, event and
probability of an event
To learn the concept of mutually exclusive, exhaustive
and complementary events
To find the probability of an event
To use simple permutations and combinations in finding
probabilities
More about Probability
To learn and apply the law
P( A  B)  P( A)  P( B)  P( A  B)
To define conditional probability & independent events
To learn and apply the law P( A  B)  P( A) P( B / A)
To learn and apply the Bayes' theorem for simple cases
Detailed Content
1. Measures of central tendency: mean, mode, modal
class and median
2. Measures of dispersion: range, interquartile range,
percentiles, variance and standard deviation
3. Frequency distribution, cumulative frequency
distribution and their graphical representations
including stem-and-leaf diagrams and their
interpretations
4. Box-and-whisker diagrams
Resources / Remarks
I.T.: Statistics & you
Getting latest daily-life
statistics figures from our
government homepage at
http://www.info.gov.hk
1. Set notation
2. Sample space, event and probability
3. Mutually exclusive, exhaustive and
complementary events
4. Further examples
I.T.: Experimental
probability
Using computer program
to simulate tossing a die
for any no. of time so as
to let students explore
experimental probability
1. The additional rule
2. Conditional probabilities
3. Bayes' Theorem
I.T.: Comparing different
data displaying method by
using excel
Month
Ch.
Oct - Nov
(cycles 6-9)
14,
15
Objectives
1.
2.
3.
4.
Detailed Content
Bernoulli, binomial, geometric & Poisson distributions and
their applications
To understand the concept of a random variable and a
probability function
To learn the probability function for the 4 different
distributions
To recognize the mean and variance of the distributions
To apply the formulae to practical problems
1. Random variable, probability function, discrete
probability distribution and expectation
2. Bernoulli distribution
3. Binomial distribution
4. Geometric distribution
5. Poisson distribution
6. Means and variances
7. Applications of Bernoulli, binomial, geometric
and Poisson distributions
Nov – Dec
(cycles 1011)
16
The normal distribution and its application
1. To learn the normal curve and standard normal curve
2. To understand the use of normal table
3. To solve practical problems
1.
2.
3.
4.
Dec
(cycles 1213)
17
Population Parameters and Sample Statistics
1. To learn related terminology
2. To acquire preliminary idea of sampling distribution
3. To learn some simple population estimators
1. Basic terminology
2. Sample mean distribution
3. Parameters estimation
22-26,31/12,
1/1/2004
21/1-28/1
Feb (cycles
16 - 17)
19/2
Normal distribution
Normal curve and standard normal curve
Normal table
Application of normal distribution
Christmas and New Year Holiday
18
Jan.
(cycles 1415)
Comparison of frequency distributions with fitted
probability distributions
1. To use statistical models to approximate observed
distributions
2. To compare frequency distributions with fitted
probability distributions
Resources / Remarks
1. Ideas of goodness-of-fit of an assumed probability
distribution to a set of observed data
2. Comparison with fitted distribution
Lunar New Year Holiday
Revision
Term Ends
Activity:
Investigation on the
distribution of our I.Q.