<![CDATA[Golden Key Cultural Center - Blog English]]>Fri, 05 Jan 2024 21:26:46 -0800Weebly<![CDATA[Ontario Grade 10 Literacy Test  (OSSLT) Tips]]>Tue, 11 Feb 2014 18:19:28 GMThttp://www.goldenkeycc.com/blog-english/ontario-grade-10-literacy-test-osslt-tips图片
Every student in Ontario need to pass the Grade 10 literacy test in order to graduate secondary school. The Ontario Secondary School Literacy Test (OSSLT) has been developed by the Education Quality and Accountability Office (EQAO) and is based on the expectations for reading and writing as set out by the Ontario curriculum policies and documents. The test is there to ensure students are on track on their literacy level. Most students pass the test, but if you fail, you can still take the test next year. 
This article will prepare you for the test and help you to get familiar with the format of the test. 

First, let's learn the format of the test:
 Duration: 2.5 hour in total. 
 Format: Two booklets, one answer sheet, you should finish each portion in 75 minutes
  
What's in the test? 
Reading component requires you to read articles and answer multiple choice questions
  • Informational Paragraph
  • News Article 
  • Dialogue 
  • Real-life Narrative
  • Graphic Paragraph 

The reading component is relatively easy compared to the writing. As long as you read the questions carefully, do not miss anything, you will be able to get most answers right. 


Writing component requires you to write short paragraphs or long essays 
  • Grammar structure  (1 page, multiple choice questions) 

       For example: on usages of comma, 
       Choose which one is correct: 
       a) Ice fishing, a popular activity in northern regions, requires skill. 
       b) First, you need to drill a hole, in the ice on a lake using an auger.
       c) Some avid anglers, use an ice shack, while waiting for the fish to bite.
       d) Ice fishing in the north requires patience a stool to sit on and warm winter clothing.
a) is correct, comma is used to separate explanation. 
  • Short Opinion Paragraph (1 paragraph opinion writing, half page) 

       Sample topic: Why is it important to learn different cultures around the world?
  • Long Opinion Paragraph ( minimum 3 paragraphs opinion essay, 2 pages)

       Sample topic: Is cellphone necessary in teenager's life?
  • News Article (  create a news article based on headline and picture, 1 page) 

       Sample headline: Students participate in important election
For a sample test, go to this site: EQAO Parent Resource
]]><![CDATA[How to Study Ontario Grade 12 Data Management MDM4U?]]>Fri, 24 May 2013 22:48:21 GMThttp://www.goldenkeycc.com/blog-english/how-to-study-ontario-grade-12-data-management-mdm4u

So you looked at your test for your Grade 12 Data Management and you have no idea what to do?  Check out this mega review list. 


For more help, contact us at (905) 896 7600 to book an tutoring appointment.
A great emphasis of this course is on Permutations, Combinations and Probability.  From unit 3 to unit 5 of the McGraw Hill Ryerson Grade 12 Mathematics of Data Management Book.


A another focus is the knowledge of one variable analysis( such as sampling techniques and types of bias) and two variable  analysis (such as finding the correlation coefficient, and cause and effect.)


The last section is about Probability Distributions. 


It is usually recommended to start with permutations, combinations and probability and then move on the data types and correlation, last study probability distributions. 


Here is a list of key points each units of the course ( Source: onstudynotes.com) 
If you found yourself understand each point, you are ready for the test, and able to get 90% + percent. If you feel you could only understand about 70- 80%, you need to crash study for 2-3 nights to get everything else. If you feel you only at about 60% or lower, find a tutor and study hard for 2 weeks! 


Come to Golden Key Cultural Centre, our teacher will help you to master the materials before your test. 


Click "Read more" for the mega-review page 
Unit 1: One Variable Analysis
Types of Data
  • Numerical Data
  • Discrete:  consists of whole numbers   (Ie. Number of trucks.)
  • Continuous: measured using real numbers (Ie, Measuring temperature.)
  • Categorical Data: cannot be qualitatively measured 
  • Nominal: Data which any order presented makes sense (Ie, Eye Colour, Hair Colour.)
  • Ordinal Data: better if sorted or ordered (Ie, Date and Time, scalar options)
Collecting Data
    • Primary: collected by yourself
    • Secondary: collected by someone else
Organizing Data
    • Micro Data: information about an individual
    • Aggregate Data: grouped data about a group; summarized data.
Data collection
    • Observational Data: group of people by characteristic, then observe
      • Group by adult/children then look at sunlight’s effect on them
    • Experimental Data: create groups and impose some treatment on them
      • Create experimental groups then apply placebo drug treatments on them.
Other Terms
    • Population: entire group of people being studied
    • Sample: the part of the population being studied
    • Inference: conclusion made about the population based on the sample
    • Binary Data: only 2 choices/outcomes
    • Non-Binary: more than 2 outcomes
Sampling Techniques

Characteristics of a good sample
-Each person must have an equal chance to be in the sample
-Sample must be vast enough to represent
  • Simple Random: each member has equal chance of being selected
    • Ie, picking members randomly apartments
  • Sequential Random: go through population sequentially and select members
    • Ie, Selecting every 5th person
  • Stratified Sampling: a strata is a group of people that share common charactoristics
    • Constraints the proportion of members in the strata from the population in the sample
    • Ie, Each strata is represented based on their proportion in the population
  • Cluster Sampling: random sample of 2 representative group
    • Ie, picking 1 floor of people and survey them
  • Multi-Stage Sampling: several levels of sampling
    • Ie, Randomly selecting provinces, random cities, then random people.
  • Voluntary Response Samples: invite members of the entire population to participate in the survey
    • Ie, Sending the survey to everyone in the hotel
  • Convenience Sample: easily accessible members are selected
    • Ie, Asking people at the mall who walks closest to you
Types of Bias
  • Good survey Questions are simple, specific, ethical, free of bias, and respects privacy
  • Survey questions should prevent jargon, abbreviations, negatives, leading questions, and insensitivity
  • Sampling Bias: occurs when the chosen sample doesn’t reflect the population
    • Ie, Asking basketball players about math issues
  • Non-Response Bias: occurs when particular groups are under-represented in a survey because they chose not to participate.
    • Ie, when respondents don’t respond, it leads the surveyor to make up their own thoughts
  • Measurement Bias: occurs when the data collection method consistently under- or overestimates a characteristic of the population
    • Leading questions also cause data over/under estimation
    • Ie, police radar gun measuring for average speed of the road
  • Response Bias: when participants in a survey give false or misleading answers
    • Question quality might  lead to response bias
    • Ie, A teacher asks class to raise their hands if they have completed their homework
Unit 2: Two Variable Analysis
  • Correlation
  • Scatter Plots graph data and is used to determine if there is a relation between the 2 variables
  • Linear Correlation: changes in one variable tend to be proportional to changes in other variables
    • The stronger the correlation, the more closely the data points cluster around the line of best fit.
  • Correlation Coefficient ( r ): a value between -1 and 1 that provides a measure of how closely data points cluster around the line of best fit.
    • -1 -  -0.62: negative, strong correlation
    • -0.61 -  -0.33: negative, moderate correlation
    • -0.32 -  0: negative, weak correlation
    • 0 – 0.32: positive, weak correlation
    • 0.33 – 0.61: positive, moderate correlation
    • 0.62 – 1: positive, strong correlation
  • Regression: finding a relationship that models the 2 variables
  • Generating lines of best fit and Outliers

Cause and Effect
    • A change in X causes a change in Y
      • Ie. Time and tree trunk diameter
  • Common Cause
    • An external factor causes two variables to change in the same way
      • Ie. Correlation between ski sales, and video rentals
        • Where it’s caused by colder weather
  • Reverse Cause and Effect
    • The dependent and independent variables are reversed in ascertaining which caused which.
      • Ie. Correlation between coffee consumption and anxiety theorized that drinking coffee causes anxiety and it is found that anxious people drink coffee
  • Accidental Relationships
    • A correlation without any casual relationship between the variables
      • Ie Increase in SUV sales causes increase in chipmunk population
  • Presumed Relationship
    • A correlation that does not seem to be accidental even though no cause-and-effect or common cause relationship is apparent
      • Ie. A correlation between the person’s level of fitness and the number of action movies they watch.
  • Critically Thinking about Data
  • When analyzing data, we should ask:
    • Source: How reliable/current is the source?
    • Sample: Does the sample reflect the opinions in the population?
      • Was the sampling technique free foam bias?
    • Graph: Is the graph accurately portrayed? (Axis starting at zero)
    • Correlation: Is the correlation between the variables strong enough to make inferences?
      • Is the causation assumed just because there is a correlation?
      • Are there extraneous variables impacting the results?
  • Number Manipulation
  • Percentage Points: means that it’s X percentage points / the value
    • Ie. 3 percentage points up from 75% is 75+(3/75*100) = 79%
  • Making Numbers Larger: In order to make better sense of numbers, sometimes people use smaller scales to make them seem bigger
    • Ie. 2,000,000 iPads sold in the first 3 months can be said as “2 iPads sold every second” to sound larger.

Unit 3: Permutations

Multiplication Principles

If one operation can be performed in K1 ways, and for each operation that can be performed K2 ways, and for each operation that can be performed K3 ways..
      • All of these ways can be performed K1 x K2 x K3.. ways
Additive Principles
    • If one mutually exclusive action can occur in K1 ways and a second can occur in K2 ways, then there are K1 + K2.. ways in which these actions can occur.
Methods
    • If a set of operations can be used to determine a result, then it’s called  Direct Method
    • However, if it is difficult to determine directly, an indirect method may be used by subtracting certain possibilities so they are eliminated
Factorial Notation
    • For the following: r < n
      • n! = n(n-1)(n-2)(n-3)(n-4)… (n-r+1)(n-r)!, n belongs to natural numbers
      • n!/(n-r)! = n(n-1)(n-2)(n-3)(n-4)… (n-r+1)(n-r)!/(n-r)!
      • ie. 6! = 6*5*4*3*2*1
Permutations with some elements alike
    • In general, the number of different arrangements of n objects K1 alike of one kind and k2 alike of another kind is:
n! / (k1!)(k2!)

  • ie. in the word “COOL”, the permutations are as follows:
4! / (2!)  = 12

Unit 4: Combinations
Venn Diagrams
    • Venn Diagrams: a number of overlapping circles each represent their own properties. Overlapped areas show values which share both properties. Center where all circles overlap show values which share all properties
    • Venn Diagrams placed in a rectangle have “s” to denote the universal set
    • Operations on Venn Diagrams
  • n(A): number of values with property A
  • n(A U B): number of values with property A or B (Union)
  • n(A n B): number of values with only A and B (Intersection)
  • Principle of Inclusion and Exclusion
n(A U B ) = n(A) + n(B) – n(A n B)
n(B U C U P) = n(B) + n(C) + n(P) – n(B U C) – n(B n P) – n(C n P) + n(B n C n P)

  • Combinations
    • Combination: a combination of n distinct objects taken r at a time is a selection of r of the n objects without regard to order.
      • Denoted as: C(n,r) or (n r) or nCr or “n choose r”    C(n,r) = n! / [(n-r)!*r!]  where n, r E W, n >= r
  • If some elements are alike and if at least one item is to be chosen, then the total number of selections from P alike items, Q alike items, R alike items and so on is: (P+1)(Q+1)(R+1).. -1
  • Each way P, Q, or R can be chosen is added by 1 for the possibility that it isn't chosen
  • 1 is subtracted for the possibility where all aren't chosen
Properties of Pascal’s Triangle
    • it’s symmetrical
    • potentially infinite in size
    • each number is the sum of the 2 numbers above it to the left and right
    • Combinations in the form C(row number, element number) also form Pascal’s Triangle
    • Pascal’s Identity: (n , r) = (n-1 , r-1) + (n-1 , r)
  • Row n: nC0*nC1*nC2*nC3*nC4… nCn
  • Sum of nth row: 2n
Unit 5: Probability
Experimental and Theoretical Probability
    • Probability: is the value between 0 and 1 that describes that likelihood of an occurrence of a certain event.
    • Experimental Probability: making predictions based on a large number of previous results.
    • Theoretical Probability: Make predictions based on a mathematical model.
    • In general, experimental probability will approach theoretical probability as the number of trials increase.
    • Discrete Sample Space: a sample space where you can count the number of outcomes ie. blue balls
    • Continuous Sample Space: decimal numbers with infinite possibilities ie. Time.
    • Event: is the occurrence of a specific outcome in the sample space.
P(A) = n(A) / n(S)

Probability of A is number of outcomes for A over total possibilities

  • P(A’) the probability that event A will not occur.
    • P(A’) = 1 – P(A)
  • Odds
    • Odds: a ratio used to represent a degree of confidence in whether or not an event will occur.
    • Odds In favour: P(A) : P(A’)
      • = n(A) : n(A’)
    • Odds Against: P(A’) : P(A)
      • = n(A’) : n(A)
Probability using counting principles
    • Instead of listing out all possibilities, counting principles such as combinations and permutations can be used to calculate all the possibilities of outcome and the possibilities of the event occurrence.
    • Refer to these links for information about counting principles
Independent and Dependent Event
    • Two events are independent if the occurrence of one event has no effect on the occurrence of another event.
    • If two events are independent, then P (A n B) = P(A) P(B)
    • Drawing tree diagrams with probability percentages on the branches can be multiplied
      • P(AA) = P(A)*P(A)
    • ie. When drawing disks from a bag, if the disks are replaced, the 2nd draw will be an independent event.
    • ie. When drawing disks from a bag, but the disks are not replaced, the 2nd draw will be a dependent event.
Mutually Exclusive Events
    • Two events are mutually exclusive if when one event occurs, the other event cannot occur.
    • If two events are mutually exclusive, then P(A U B) = P(A) + P(B)
    • If two events are not mutually exclusive, then P( A U B) = P(A) + P(B) – P(A U B)
      • ie Probability of picking a KING or a FOUR is a mutually exclusive event.
      • ie Probability of picking a KING or a RED card is non-mutually exclusive. 
Conditional Probability
    • The probability that an event will occur given that another compatible event that already occurred.
    • P(A / B) = P(A and B) / P(B)
      • Probability of A given the occurrence of B is equal to the probability of A and B over the probability that B has occurred.
    • ie. Probability of drawing a QUEEN if we know the chosen card is a face card is an example of conditional probability.
Unit 6: Probability Distributions

  • Basic Probability Distributions
    • Random Variable: By letting X be a random variable, can generalize the probability to obtain the number times something happens
    • Probability Distributions can be created in a Table then graphed into a histogram to analyze the probability of each event happening
    • Expected Value: Expectation or expected value, E(X), is the predicted average of all possible outcomes of a probability experiment. In essence, it is a weighted mean of all the outcomes.
E(x) = Summation of (X*P(x)

    • X: Random variable value, P: Probability of the random variable
  • Binomial Distribution
    • All trials are independent
    • Only 2 possible outcomes (Success or failures)
    • Probability of success is the same on every level
    • Usually replaceable items
    • Binomial Distribution Formula
    • P(x) = (nCx) Px Qn-x
    • n: number of trials, P: probability, Q: 1-probability, X: random variable
    • Shortcut Expected Value Formula
    • E(x) = np
    • n: number of trials, P: probability
  • Hypergeometric Distributions
    • Hypergeometric Distributions are used for sampling without replacement.
    • Expected value of the sample should be proportional to the population
    • Outcomes are still 2 possibilities (Success or Failures)
    • Probabilities are not the same each time
    • Dependent Events
    • Not replaceable
    • Formula for Hypergeometric Distributions
Unit 7: Continuous Probability Distributions

  • Continuous Probability Distributions
    • a random variable that can assume all possible random values (ie city temperature)
    • Probability Density Function: a function that describes how likely this random variable will occur at a given point.
    • Height formula:  height = 1/(b-a) where b is the top range, and a is the bottom range given.
  • The Normal Distribution
    • used to solve continuous probabilities
    • symmetry about the mean
    • total area under the curve is 1
    • standard deviation is the distance from the mean to the point of inflection
    • Any normal distribution can be described as by the mean and the variance: so we often write N(mean, variance) to describe a distribution
    • The distribution chart shows area under the graph from the X value to the left end
    • Z-Scores can be calculated using Normal distributions
      • Z = x – mean / standard deviation
      • Sometimes, you will have to subtract the mean to equalize. This makes it so the mean is on the center.
  • Normal Approximation
    • Step 1: Check if a normal approximation is appropriate. Test if np > 5 and nq > 5.
    • Step 2: Estimate the mean and standard deviation (mean = nq, SD = √(npq) )
    • Step 3: Estimate the probability using z-score method from above.
  • Confidence Interval
    • x- z * (σ/√n) < μ < x + z * (σ/√n)
      • where x is the mean of the sample
      • z is the z score of acceptable error
      • μ is the mean of population
      • n is the size of sample
      • σ is the standard deviation
    • Confidence levels and z- scores are retrieved from a given chart below:
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]]><![CDATA[Voice K English Improvement Class in Scarborough & Mississauga]]>Fri, 24 May 2013 17:27:24 GMThttp://www.goldenkeycc.com/blog-english/voice-k-english-improvement-class-in-scarborough-richmond-hillPicture
Do you see the following problems in your kid's writing? 
- Lack of vocabulary
- Redundant phrasing 
- Lack of understanding of literary device
- Lack of contents to write about
- Poor grammar and sentence structure

Join our After School English Improvement Program in Mississauga and Scarborough: 
What do we do in class? 
- Spelling Test
- Grammar Exercise
- Writing Exercise
 ( follow Ontario Curriculum. For younger students, we work on writing simple narrative stories, how to write letters, how to write book reviews. etc. For older students, we work on writing descriptive essays, persuasive essays, comparison essays etc.)
- Writing Mechanisms  (Use of words, literary devices, transitional words, etc)


Contact us for a free assessment (416) 587 5178
]]><![CDATA[Oakville Go Open Seminars]]>Sat, 18 May 2013 02:08:25 GMThttp://www.goldenkeycc.com/blog-english/oakville-go-open-seminarsPicture
Early this May, we did a free Go seminar at Golden Coast Tea Club in Oakville. Golden Coast Tea Club does not only serves the best tea in town, its owner, David Tang, is someone loves Go (围棋）and any other Chinese cultures. 

At the Seminar, Joanna taught introductory to go to kids and Louis talked about basic concepts of territory and Joseki. The children were able to play the game with each other after and use what they have just learned!


Our new Beginner Go class in Oakville is every Wednesday, 7:00 to 8:30 pm.  
Class location is: 278 Kerr St, Oakville, ON. Canada   L6K 3B3
]]><![CDATA[2013 Children Go / Weiqi Tournament Results]]>Tue, 12 Mar 2013 01:52:56 GMThttp://www.goldenkeycc.com/blog-english/2013-children-go-weiqi-tournament-resultsCompetition Results
On March 11, 2013. Golden Key hosted the first GTA Children's Go Tournament. The tournament served as a practice tournament for the 2013 Canadian Go pen. The Tournament attracted 14 players under age of 12. The first place went to Judy Liu (1K) with 3 wins, 2nd place went to John Nicholson (1K)  with 2 wins, third place went to Qingyuan Wu (1D).   

For other divisions, 5K to 9K, winner is Richard Bao (5K). 10K to 14K, winner is David Kang (10 K), and beginner division winner is Richie Zhang (15K)

Before the tournament
Before the tournament, Golden Key Go Instructor, Louis Liu (5D) give a simple lecture reviewing the 3-3 joseki, and explained some basic life and death go problems. The children also had some time to practice go before the tournament starts. During the practice rounds, Eric Yang (3K) won against both Judy Liu and Qingyuan Wu with his strong invasion and attacking skills.  Unfortunately, Eric lost to John and Qingyuan during the tournament, ending up to be the fourth place in the tournament. 


Fun facts
If anyone feels our third place winner's name, Wu Qingyuan, sound familiar, it is the name of one of the most talented go player in the 20th century, known to the Western world as Go Seigen (Japanese pronunciation). 


Award Ceremony
During our award ceremony, the children were informed about the 2013 Canadian Go Open Tournament and its new children's division. Our young go players asked many questions regarding the 2013 Canadian Go Open Tournament from their point of views. 

We are sharing some of the questions here: 
1. What is the age limit for the children's division for the 2013 Canadian Go Open?
The age limit is 12 years old. So you need to be 12 years old or under on the day of the tournament. 

2. Will the children winner be representing Canada at the World's Amateur Go Tournament? 
 Unfortunately, no. If you wish to win and to represent Canada, you should join the adult division. 

3. Will the children allow to join pair go?
Yes! You are welcome to join the pair go section. By the way, it is not very easy for everyone to find a girl partner for the pair go. So if you have someone in mind, you should persuade them to register!

4. Are we going to have the chance to play with professional go players?
Yes! You will have the chance to play with professional go players during one-to-many games. We will ask you to register for it once we have the set schedule. 


]]><![CDATA[School Closed Due to Weather Conditions]]>Fri, 08 Feb 2013 19:17:31 GMThttp://www.goldenkeycc.com/blog-english/school-closed-due-to-weather-conditionsGolden Key is closed on Feb.8, 2013 
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We will be closed today, Feb.8, 2013 due to the weather condition, All classes are scheduled to resume tomorrow, Feb. 9, 2013 unless states otherwise. 
]]><![CDATA[2013 Canadian Go Open Tournament]]>Mon, 24 Dec 2012 04:04:10 GMThttp://www.goldenkeycc.com/blog-english/2013-canadian-go-open-tournament
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2013 Canadian Go Open Tournament Details

Date: June 28 to July 1, 2013 
Location: 2330 Midland Avenue, Toronto, Ontario, M1S 5G5

Organized by: Golden Key Cultural Center Inc.

Division & Fees
  • 6+ Dan: $70
    (Open Division)
  • 1-5 Dan: $50
  • Kyu-level: $40
  • Children: $30 (2 day only)

Format and Schedule

MAIN TOURNAMENT (Adult ) 

  • 3 day competition
  • 6 rounds - Swiss MacMahon
  • 2 games per day – Friday, Saturday and Sunday
  • 1 hour per player
  • Byo-yomi: 20 moves per 10 minutes
Playoffs on Monday
Schedule Details

Friday, Day 1
9:00 - 12:00   Round 1
12:00 -1:00    Lunch
1:00 - 4:00     Round 2

Saturday, Day 2
9:00 - 12:00   Round 1
12:00 -1:00    Lunch
1:00 - 4:00     Round 2

Sunday, Day 3 
9:00 - 12:00   Round 1
12:00 -1:00    Lunch
1:00 - 4:00     Round 2
CHILDREN'S TOURNAMENT
  • 2 day competition afternoon
  • 6 rounds - Swiss MacMahon
  • 3 games per day – Saturday and Sunday afternoon
  • 30 minutes per player
  • Byo-yomi: 25 moves per 10 minutes
  • Interactive Go Puzzles between matches
Schedule Details
Saturday, Day 1

12:30 - 1:45   Round 1
1:45 - 3:00     Round 2
3:00 - 4:15     Round 3

Sunday, Day 2

12:30 - 1:45   Round 1
1:45 - 3:00     Round 2
3:00 - 4:15     Round 3
PAIR GO TOURNAMENT 

  • 3 rounds - Swiss MacMahon
  • Will be held on Monday
  • 45 minutes per player
  • no Byo-yomi
Schedule Details
Monday

9:00 - 11:00   Round 1
11:00 - 1:00   Round 2
2:00 -  4:00    Round 3
AWARD CEREMONY AND BANQUET 

·         Sunday night
·         Announce winners from each division 
·         Opportunity to thank sponsors and volunteers
·         Cultural performances and auction
·         2013 Canadian Go recap slide show
Schedule
Sunday, 5:30 pm  - 8:00 pm
Auction- Go related artwork (ink paintings) and objects.
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