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AP Stats Spring 2021 Crash Course 📊

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Unit 1 - Exploring One-Variable Data

AP Statistics

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AP Stats Unit 1 Review

An introductory stream to get to know about AP Statistics and what it is all about, as well as a review or preview of the concepts of Unit 1.

📝study guide

1.0 Unit 1 Overview: Exploring One-Variable Data

Statistics is all about data. We explore data, we describe data, we collect data, we test data, and finally, we make inferences about data. This is the first unit, and we begin by exploring data-the descriptive statistics. In this unit, you will distinguish between categorical (a.k.a. qualitative) and quantitative data and learn a variety of tools to display, describe, and compare them.

Normal Distributions

In this stream, we discuss everything about the normal distribution. Starting at the beginning of the course with the standard normal curve and z scores and landing at the necessity for the normal distribution in sampling distributions and inference procedures.

Presentation Slides, Normal Distributions

Browse slides from the stream 'Normal Distributions.'

Normal Curve and Normal Calculations

This video covered the Empirical Rule (68-95-99.7 Rule) and how to apply it to Normal distributions. We then discussed and described the Standard Normal distribution. After we performed normal calculations and finally discussed ways to assess normality of a distribution using graphs and the Empirical Rule.

Describing Location in a Distribution

In this video we went over what a percentile is and how to calculate it along with some examples. We then defined and interpreted a cumulative relative frequency graph. After we went over what happens to shape, center and spread when transforming data. Lastly we defined a density curve and key characteristics about it.

Describing Location in a Distribution - Slides

Browse slides from the stream 'Describing Location in a Distribution,' where we cover topics including Exploring Data.

Displaying Quantitative Data with Graphs

We covered how to create dotplots, stemplots and histograms. We also learned how to describe distributions using the acronym CUSS BSers. We describe distributions by discussing the center, unusual, shape, spread. We then talked about how we compare distributions and how to use histograms wisely. We went through how to create distributions on a TI-84 calculator.

Analyzing Categorical Data - Slides

Browse slides from the stream 'Analyzing Categorical Data,' where we cover topics including Exploring Data.

Analyzing Categorical Data

In this live session we defined some of the essential foundational terms in Statistics like individuals, variables, distribution and inference. We distinguished the difference between categorical data and quantitative data. We constructed graphical displays for categorical data and two way tables. We interpreted and described relationships between two categorical variables.

📝study guide

1.1 Introducing Statistics: What Can We Learn from Data?

Depending on how we use data, the study of statistics is divided into two main areas: descriptive and inferential. In descriptive statistics, we describe a situation by collecting, organizing, summarizing, and presenting the data. In inferential statistics, we try to make an inference from our collected data to populations by generalizing, estimating, testing, and making predictions. We will preserve the inferential statistics for the future and will focus on the descriptive branch of statistics here.

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1.2 The Language of Variation: Variables

Before taking you on the journey of learning, statistics, let's make some sense of data.

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1.3 Representing a Categorical Variable with Tables

Data can be enormous and hard to understand. For this purpose, statistics was created to help us organize and analyze data. The first values are organized in the tables. Then data are graphed in different displays. Tables are a necessary step to start analyzing data, but it may fail to highlight essential features with data. The graphical displays are visually attractive, easy to read, and see important patterns of the distribution. For categorical variables, the choices are limited. Bar graphs and Pie charts are the most common displays.

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1.4 Representing a Categorical Variable with Graphs

To construct a bar graph (bar chart), we mark the frequencies on a vertical axis and the categories on the horizontal axis. Each category represents one bar. The frequency of each category determines the heights of the bars. All bars have the same width and gap between adjacent bars. To keep it short, here is the bar graph of stress on the job. We can also use relative frequencies or percentages to construct the bar graph. You can be creative and color each category with a different color. It will be visually attractive and easier to compare them.

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1.5 Representing a Quantitative Variable with Graphs

This section describes how we organize and display quantitative data. The frequency table is a bit complicated for quantitative data, especially if we deal with vast amounts of data. The good news is that AP doesn’t require you to make one, so we will skip this one. Just know that many computer programs, including our mighty Excel, and your TI series calculator can make it in seconds. The main displays we will discuss are histograms, polygons, ogive, stem-and-leaf plots, and dot-plot.

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1.6 Describing the Distribution of a Quantitative Variable

Once we organized the set of data of our interest into a certain display of our choice, the next task is to describe the data. In other words we should tell what we see. There are three things that we should look for and never forget it. The three things are shape, center and spread. Let's discuss one by one.

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1.7 Summary Statistics for a Quantitative Variable

Statistics is a measure taken from the sample to help us analyze the data. The parameter is the measure taken from the population. In inferential statistics, we will use statistics to make inferences about the parameters. But let’s not jump in there and focus on summary statistics. Mean, median, standard deviation, IQR, range, all are summary statistics for a quantitative variable. The mean median, quartiles, and percentiles measure the center and position for quantitative data, whereas the range IQR, and standard deviation measure the variability for quantitative data. The summary measures change if we convert them to different units.

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1.8 Graphical Representations of Summary Statistics

This section represents summary statistics for quantitative data graphically. Describing summary statistics we can use it to talk about our data in the context.

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1.9 Comparing Distributions of a Quantitative Variable

We talked a lot about distributions and how to describe, summarize and represent them, now it is time to put more into practice by comparing the multiple sets of data. Comparing sets of histograms and boxplots, the above information would come alive to you.

📝study guide

1.10 The Normal Distribution

This section introduces you to z-scores. When I think of statistics, one of the first things that come in my mind is standard deviation and z-scores. So what are the z-scores? z-scores measure the distance of a value from the mean in standard deviations. The formula is simple but very powerful. It is resistant to units, and it can be used to compare any activity.

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Free Reviews 2020

27 resources

Free Response Questions (FRQs)

7 resources

Unit 1 - Exploring One-Variable Data

19 resources

Unit 2 - Exploring Two-Variable Data

18 resources

Unit 3 - Collecting Data

12 resources

Unit 4 - Probability, Random Variables, and Probability Distributions

22 resources

Unit 5 - Sampling Distributions

16 resources

Unit 6 - Inference for Categorical Data: Proportions

16 resources

Unit 7 - Inference for Qualitative Data: Means

19 resources

Unit 8 Inference for Categorical Data: Chi-Square

0 resources

Unit 9 - Inference for Quantitative Data: Slopes

0 resources

Multiple Choice Questions (MCQs)

0 resources

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