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Person Analyzing Statistics

Introduction to Statistic

Unlock the Power of Data: Master AP Statistics and Excel in Analysis!

20 hours of 1:1 lessons

Course Overview 

AP Statistics is a college-level course that introduces students to the principles and methods of statistical analysis. The course covers a wide range of topics, including data collection and organization, probability theory, hypothesis testing, and data interpretation. Students will develop the skills necessary to analyze and interpret data, make informed decisions, and communicate statistical information effectively.

20 High Quality 



hands on learning

Customized course structure

Flexible payment plan

By the End of the course Student will be able to

Topic Covered






  1. Develop ability to collect, organize, and analyze data using appropriate statistical methods.

  2. Enhance understanding of probability theory and its applications in real-world scenarios.

  3. Skills to conduct hypothesis tests, make inferences, and draw conclusions based on sample data.

  4. To foster critical thinking and problem-solving skills through hands-on data analysis and interpretation.

  5. To prepare students for the AP Statistics exam, including the ability to apply statistical concepts to solve complex problems.

What we will be covering in 20 Lesson :

Lesson 1: Introduction to Statistics

  • Overview of statistics, data types, and measurement scales

  • Descriptive vs. inferential statistics

  • Collecting and organizing data

Lesson 2: Descriptive Statistics

  • Measures of central tendency (mean, median, mode)

  • Measures of variability (range, variance, standard deviation)

  • Displaying data with graphs and charts

Lesson 3: Sampling and Experimental Design

  • Types of sampling techniques (random, stratified, cluster)

  • Experimental design principles (control, randomization, replication)

Lesson 4: Probability

  • Fundamentals of probability theory

  • Probability rules (addition, multiplication, complement)

  • Conditional probability and independence

Lesson 5: Probability Distributions

  • Discrete probability distributions (binomial, geometric, Poisson)

  • Continuous probability distributions (normal, uniform)

Lesson 6: Sampling Distributions

  • Central Limit Theorem and its implications

  • Sampling distribution of the sample mean

  • Sampling distribution of the sample proportion

Lesson 7: Confidence Intervals

  • Confidence interval estimation for means and proportions

  • Interpreting confidence intervals

  • Factors affecting the width of confidence intervals

Lesson 8: Hypothesis Testing

  • Null and alternative hypotheses

  • Type I and Type II errors

  • Hypothesis testing for means and proportions

Lesson 9: Inference for Means

  • One-sample t-test

  • Independent samples t-test

  • Paired samples t-test

Lesson 10: Inference for Proportions

  • Testing proportions and constructing confidence intervals

  • Comparing two proportions

Lesson 11: Chi-Square Tests

  • Chi-square goodness-of-fit test

  • Chi-square test of independence

  • Chi-square test for homogeneity

Lesson 12: Regression Analysis

  • Simple linear regression

  • Fitting the regression line

  • Assessing the model's goodness of fit

Lesson 13: Correlation Analysis

  • Measures of correlation (Pearson's r, Spearman's rho)

  • Interpreting correlation coefficients

  • Testing for the significance of correlation

Lesson 14: Multiple Regression

  • Multiple regression analysis

  • Interpreting regression coefficients

  • Model selection and diagnostic checking

Lesson 15: Analysis of Variance (ANOVA)

  • One-way ANOVA

  • Two-way ANOVA

  • Post-hoc tests and multiple comparisons

Lesson 16: Nonparametric Methods

  • Sign test

  • Wilcoxon signed-rank test

  • Mann-Whitney U test

Lesson 17: Confidence Intervals and Hypothesis Testing for Regression

  • Confidence intervals for regression coefficients

  • Hypothesis testing for regression coefficients

  • Residual analysis and model validation

Lesson 18: Experimental Design and Analysis

  • Randomized complete block design

  • Factorial design

  • Analysis of variance for designed experiments

Lesson 19: Simulation and Probability Models

  • Monte Carlo simulation

  • Probability models and simulations

  • Applications of simulations in statistics

Lesson 20: Review and Exam Preparation

  • Comprehensive review of AP Statistics topics

  • Practice exams and exam strategies

  • Tips for success on the AP Statistics exam


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