AP istatistik kurslarımız tecrübeli AP eğitmenlerimizle birlikte işlenmektedir. Bu hocalarımızın artısı yeni AP müfredatına tam uyumlu olmalarıdır. Sınavda karşınıza çıkacak her zorluğa hazırlayan Test Prep AP istatistik özel ders, hafta içi ve hafta sonu programlarında verilir.

AP İstatistik Ders Müfredatı

AP İstatistikleri dersi, öğrencilere veri toplama, analiz etme ve sonuç çıkarma ile ilgili temel kavram ve araçları tanıtmaktadır. AP İstatistik dersinde dört tema vardır:

1. Verileri, örnekleme
2. Deneyleri keşfetme
3. Kalıpları tahmin etme
4. İstatistiksel çıkarım.

Öğrenciler, kavramsal anlayış geliştirdikçe teknoloji, araştırmaları kullanırlar. Kolejlerde ve üniversitelerde, istatistik dersi alan öğrencilerin sayısı, matematik dersi alan öğrencilerin sayısı kadar fazladır. Bu giriş seviyesindeki istatistikleri dersi, genellikle sosyal bilimler, sağlık bilimleri ve işletme gibi bölümler için gereklidir. Her dönem yaklaşık 236.000 üniversite öğrencisi bir matematik veya istatistik bölümü tarafından sunulan istatistik dersine kayıt oluyor. Bilim, mühendislik ve matematik bölümleri genellikle istatistik dersi olarak üst düzey bir matematik tabanlı ders alırlar. AP İstatistik kursu üniversiteye hazırlıkta ve girişte  etkili derstir.

AP İstatistik Sınav Konuları Nelerdir?

I . Exploring Data: Describing patterns and departures from patterns (20%–30%)

Exploratory analysis of data makes use of graphical and numerical techniques to study patterns and departures from patterns. Emphasis should  be  placed  on interpreting information from graphical and numerical displays and summaries .

A . Constructing and interpreting graphical displays of distributions of univariate data (dotplot, stemplot, histogram, cumulative frequency plot)

1 . Center and spread

2 . Clusters and gaps

3 . Outliers and other unusual features 4 . Shape

B . Summarizing distributions of univariate data

1 . Measuring center: median, mean

2 . Measuring spread: range, interquartile range, standard deviation

3 . Measuring position: quartiles, percentiles, standardized scores (z-scores)

4 . Using boxplots

5 . The effect of changing units on summary measures

C . Comparing distributions of univariate data (dotplots, back-to-back stemplots, parallel boxplots)

1 . Comparing center and spread: within group, between group variation

2 . Comparing clusters and gaps

3 . Comparing outliers and other unusual features 4 . Comparing shapes

D .  Exploring bivariate data

1 . Analyzing patterns in scatterplots

2 . Correlation and linearity

3 . Least-squares regression line

4 . Residual plots, outliers and influential points

5 . Transformations to achieve linearity: logarithmic and power transformations

E .  Exploring  categorical data

1 . Frequency tables and bar charts

2 . Marginal and joint frequencies for two-way tables

3 .  Conditional relative frequencies and association

4 . Comparing distributions using bar charts

II . Sampling and Experimentation: Planning and conducting a study (10%–15%)

Data must be collected according to a well-developed plan if valid information on a conjecture is to be obtained. This plan includes clarifying the question and deciding upon a method of data collection and analysis.

A . Overview of methods of data collection

1 . Census

2 . Sample survey 3 . Experiment

4 .   Observational study

B . Planning and conducting surveys

1 . Characteristics of a well-designed and well-conducted survey

2 . Populations, samples and random selection

3 . Sources of bias in sampling and surveys

4 . Sampling methods, including simple random sampling, stratified random sampling and cluster sampling

C . Planning and conducting experiments

1 . Characteristics of a well-designed and well-conducted experiment

2 . Treatments, control groups, experimental units, random assignments and replication

3 . Sources of bias and confounding, including placebo effect and blinding

4 . Completely randomized design

5 . Randomized block design, including matched pairs design

D . Generalizability of results and types of conclusions that can be drawn from observational studies, experiments and surveys

III . Anticipating Patterns: Exploring random phenomena using probability and simulation (20%–30%)

Probability is the tool used for anticipating what the distribution of data should look like under a given model.

A . Probability

1 . Interpreting probability, including long-run relative frequency interpretation

2 . “Law of Large Numbers” concept

3 . Addition rule, multiplication rule, conditional probability and independence

4 . Discrete random variables and their probability distributions, including binomial and geometric

5 . Simulation of random behavior and probability distributions

6 . Mean (expected value) and standard deviation of a random variable, and linear transformation of a random variable

B . Combining independent random variables

1 . Notion of independence versus dependence

2 . Mean and standard deviation for sums and differences of independent random variables

C . The normal distribution

1 . Properties of the normal distribution

2 . Using tables of the normal distribution

3 . The normal distribution as a model for measurements

D . Sampling distributions

1 . Sampling distribution of a sample proportion 2 . Sampling distribution of a sample mean

3 . Central Limit Theorem

4 . Sampling distribution of a difference between two independent sample proportions

5 . Sampling distribution of a difference between two independent sample means

6 . Simulation of sampling distributions

7 . t-distribution

8 .   Chi-square distribution

IV . Statistical Inference: Estimating population parameters and testing hypotheses  (30%–40%)

Statistical inference guides the selection of appropriate models.

A . Estimation (point estimators and confidence intervals)

1 . Estimating population parameters and margins of error

2 . Properties of point estimators, including unbiasedness and variability

3 . Logic of confidence intervals, meaning of confidence level and confidence intervals, and properties of confidence intervals

4 . Large sample confidence interval for a proportion

5 . Large sample confidence interval for a difference between two proportions

6 . Confidence interval for a mean

7 . Confidence interval for a difference between two means (unpaired and paired)

8 . Confidence interval for the slope of a least-squares regression line

B .  Tests of significance

1 . Logic of significance testing, null and alternative hypotheses; p-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power

2 . Large sample test for a proportion

3 . Large sample test for a difference between two proportions

4 . Test for a mean

5 . Test for a difference between two means (unpaired and paired)

6 . Chi-square test for goodness of fit, homogeneity of proportions, and independence (one- and two-way tables)

7 . Test for the slope of a least-squares regression line