Course Details
AP Statistics Live 1:1 Online Tutoring – Score a 5 with Expert Help
Develop a deep understanding of data analysis, probability, and statistical inference with Refresh Kid’s AP Statistics course. Designed to help high school students excel on the College Board AP exam, our course offers clear lessons, practical problem-solving exercises, and personalized support. Whether you're building a foundation in statistics or aiming for a perfect score, Refresh Kid is here to guide you every step of the way.
Why Choose Refresh Kid’s 1:1 AP Statistics Tutoring Course?
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Tailored Learning: Lessons are customized to each student's pace, covering everything from the basics of data collection to advanced statistical inference.
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Expert Tutors: Learn from experienced AP Statistics educators who simplify complex concepts and provide targeted strategies for exam success.
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Focused Curriculum: Our course covers all key topics, including exploring data, probability, sampling distributions, and statistical inference.
- Weekly Progress Reports: Stay informed with updates that track your improvement and focus on areas that need attention.
Choose the format that best fits your learning style:
1:1 Non-local teacher:
Affordable, focused instruction for personalized support.
Group with local US teacher:
Engaging group sessions led by a local expert.
1:1 with US teacher:
Intensive prep for those aiming for a high score, offering deep focus on challenging concepts.
Practice with Real AP Statistics Exam Questions:
Access practice tests, past AP exam questions, and targeted exercises to build your confidence.
Start Your AP Statistics Tutoring Journey Today!
With Refresh Kid’s AP Statistics course, you’ll gain the skills needed to analyze data effectively and succeed on exam day. Our expert tutors, structured curriculum, and dedication to your success make us the right choice for mastering AP Statistics.
Enroll Now and take the first step towards achieving a 5 on the AP Statistics exam with Refresh Kid!
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LoginTopics Covered
AP Statistics
A. Exploring One-Variable Data
1 Types of data: categorical vs quantitative
2 Displaying categorical data (bar charts, pie charts)
3 Displaying quantitative data (dot plots, stem plots, histograms)
4 Describing distributions (shape, center, spread, outliers)
5 Measuring center: mean, median
6 Measuring spread: range, IQR, standard deviation
7 Comparing distributions (using center and spread)
8 Understanding and using percentiles and z-scores
B. Exploring Two-Variable Data
1 Scatterplots and interpreting relationships
2 Correlation: strength and direction of a linear relationship
3 Introduction to linear regression
4 Least-squares regression line (LSRL)
5 Interpreting slope, y-intercept, and correlation coefficient (r)
6 Residuals and assessing model fit
7 Transformations to achieve linearity (logarithmic, reciprocal)
C. Collecting Data
1 Designing a study: observational study vs experiment
2 Understanding and identifying bias in sampling
3 Random sampling methods (SRS, stratified, cluster)
4 Experimental design: control, randomization, replication
5 Types of experimental designs (completely randomized, blocked, matched pairs)
6 Identifying confounding variables
7 Using simulations to model random events
D. Probability, Random Variables, and Probability Distributions
1 Basics of probability rules (addition, multiplication)
2 Probability models and sample spaces
3 Independent and dependent events
4 Conditional probability
5 Discrete random variables and their expected value
6 Continuous random variables and probability density functions
7 Mean and standard deviation of random variables
8 Combining random variables
E. Sampling Distributions
1 Understanding what a sampling distribution is
2 Sample proportions: distribution and center/spread
3 Sample means: distribution and center/spread
4 Central Limit Theorem (CLT)
5 Conditions for normal approximation (Large Counts and Large Sample Conditions)
6 Using sampling distributions to predict variability
F. Inference for Categorical Data: Proportions
1 Confidence intervals for a population proportion
2 Margin of error and interpreting confidence intervals
3 Hypothesis testing for a proportion
4 Conditions for inference on proportions
5 Power of a test and Type I/Type II errors
G. Inference for Quantitative Data: Means
1 Confidence intervals for a population mean
2 Hypothesis testing for a population mean
3 Conditions for inference on means
4 Using t-distributions when standard deviation is unknown
5 Paired t-tests for matched pairs data
H. Inference for Categorical Data: Two Categories
1 Two-sample z-test for the difference between two proportions
2 Confidence intervals for the difference between two proportions
3 Chi-square tests for goodness of fit
4 Chi-square tests for homogeneity and association
I. Inference for Quantitative Data: Two Samples
1 Two-sample t-tests for the difference between two means
2 Confidence intervals for the difference between two means
3 Using t-distributions for inference
4 Assumptions and conditions for two-sample t-tests
J. Linear Regression Inference
1 Conditions for regression inference (Linearity, Independence, Normality, Equal Variance)
2 Confidence intervals for slope of regression line
3 Hypothesis testing for slope of regression line
4 Using computer output for regression inference
5 Understanding standard error of estimate and r² in context
