1.1: Variation in categorical and quantitative variables
1.2: Representing data using tables or graphs
1.3: Calculating and interpreting statistics
1.4: Describing and comparing distributions of data
1.5: The normal distribution
2.1: Comparing representations of 2 categorical variables
2.2: Calculating statistics for 2 categorical variables
2.3: Representing bivariate quantitative data using scatter plots
2.4: Describing associations in bivariate data and interpreting correlation
2.5: Linear regression models
2.6: Residuals and residual plots
2.7: Departures from linearity
3.1: Planning a study
3.2: Sampling methods
3.3: Sources of bias in sampling methods
3.4: Designing an experiment
3.5: Interpreting the results of an experiment
4.1: Using simulation to estimate probabilities
4.2: Calculating the probability of a random event
4.3: Random variables and probability distributions
4.4: The binomial distribution
4.5: The geometric distribution
5.1: Variation in statistics for samples collected from the same population
5.2: The central limit theorem
5.3: Biased and unbiased point estimates
5.4: Sampling distributions for sample proportions
5.5: Sampling distributions for sample means
6.1: Constructing and interpreting a confidence interval for a population proportion
6.2: Setting up and carrying out a test for a population proportion
6.3: Interpreting a p-value and justifying a claim about a population proportion
6.4: Type I and Type II errors in significance testing
6.5: Confidence intervals and tests for the difference of 2 proportions
7.1: Constructing and interpreting a confidence interval for a population mean
7.2: Setting up and carrying out a test for a population mean
7.3: Interpreting a p-value and justifying a claim about a population mean
7.4: Confidence intervals and tests for the difference of 2 population means
8.1: The chi-square test for goodness of fit
8.2: The chi-square test for homogeneity
8.3: The chi-square test for independence
8.4: Selecting an appropriate inference procedure for categorical data
9.1: Confidence intervals for the slope of a regression model
9.2: Setting up and carrying out a test for the slope of a regression model
9.3: Selecting an appropriate inference procedure
Identify strengths, weaknesses, and needs.
Set academic goals with a clear learning roadmap.
Develop a detailed and structured study plan.
Teachers provide close guidance and adapt flexibly to maximize learning outcomes.
Identify strengths, weaknesses, and needs.
Set academic goals with a clear learning roadmap.
Develop a detailed and structured study plan.
Teachers provide close guidance and adapt flexibly to maximize learning outcomes.
Teaches:
Curriculum:
Teaches:
Curriculum:
Teaches:
Curriculum:
Teaches:
Curriculum:
Teaches:
Curriculum:
Teaches:
Curriculum:
Teaches:
Curriculum:
Teaches:
Curriculum:
| Condition / Feature | Standard | Premium | Platinum |
|---|---|---|---|
| πTarget Score Commitment | β | β | β |
| Worksheets and Lesson Notes | β | β | β |
| In-class Exercises and Solutions | β | β | β |
| Extra Homework | β | β | β |
| Exam-style and Past Papers | β | β | β |
| Question Bank | β | β | β |
| Saturday Morning Homework Support | β | β | β |
| Fixed Teacher | β | β | β |
| Support for IA, EE, TOK | β | β | β |
| After-hours Message Response (until 9:30 PM) | β | β | β |
| Initial Teacher & Student Meeting (Welcome Meeting) | β | β | β |
| Teacher & Parent Conference | β | β | β |
| Periodic Academic Reports | β | β | β |
| Teacher's Feedback and Evaluation After Each Class | β | β | β |
| Rescheduling Policy (Notice within working hours) | 24 hours | 12 hours | 06 hours |
| Exam Pass Commitment | β | β | β |
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