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Jerry Linch

Jerry Linch

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Each lesson also comes with a downloadable word document of course notes to help you learn the material as you watch the video lessons.

Although our course is catered towards high school students taking the AP test, college students in a first year statistics course will also find this class life-saving.

Did we mention you'll also have an awesome teacher?

Jerry Linch obtained his B.S. in Mathematics from the University of Nebraska and M.S. in Statistics from the University of Houston Clear Lake. With several years of practice in the actuarial field, he has an excellent understanding of the material and can explain the concepts at a level which any entry level student can understand. If you want a comprehensive course of all the AP Statistics topics and most all elementary statistics topics covered in a college course and explained with ease, then this course is for you.

Register for this course and see more details by visiting:
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- Understand the concepts of most elementary college and advanced placement statistics courses.
- Describe patterns and departures from patterns using descriptive statistics.
- Interpret information from graphical and numerical displays and summaries.
- Plan and conduct statistical studies by looking at data collection and analysis. observational studies and experiments are both considered as well as proper sampling techniques and possible biases that can occur.

- Explore random phenomena using probability and simulation. both discrete and continuous probability models are considered and sampling distributions are introduced.
- Estimate population parameters using statistics and testing hypothesis. the student will be able to construct a confidence interval and hypothesis test for numerical and categorical data.
- Successfully complete a college entry level statistics class and achieve success on the advanced placement statistics exam.

- Understand the concepts of most elementary college and advanced placement statistics courses.
- Describe patterns and departures from patterns using descriptive statistics.
- Interpret information from graphical and numerical displays and summaries.
- Plan and conduct statistical studies by looking at data collection and analysis. observational studies and experiments are both considered as well as proper sampling techniques and possible biases that can occur.
- Explore random phenomena using probability and simulation. both discrete and continuous probability models are considered and sampling distributions are introduced.
- Estimate population parameters using statistics and testing hypothesis. the student will be able to construct a confidence interval and hypothesis test for numerical and categorical data.
- Successfully complete a college entry level statistics class and achieve success on the advanced placement statistics exam.

Here is a quick intro of your instructor, Jerry Linch! I'm so glad you are here! Prepare to embark on a journey of statistical understanding and success!

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Here is a quick intro to AP Statistics and Elementary Statistics Video Series and your instructor, Jerry Linch.

Students will be able to describe patterns and departures from patterns using descriptive statistics and will be able to interpret information from graphical and numerical displays and summaries.

An introduction to the the topic of statistics. The two main branches of statistics are discussed: descriptive statistics and inferential statistics. The definitions of population and sample are discussed.

We talk about discrete and continuous variables in this section and classify data by number of variables.

Frequency and Relative Frequency are discussed, We constructing Bar Charts and Pie Graphs based on our categorical data, Comparative Displays are used to look at differences in distributions.

Graphing small to medium sized data sets. Construction of dotplots and stem-and-leaf plots with comparative displays.

Graphing medium to large sized data sets. Construction of Histograms with density scales included. Construction of Ogives (Cumulative Relative Frequency Graphs).

Graphing medium to large sized data sets. Construction of Histograms with density scales included. Construction of Ogives (Cumulative Relative Frequency Graphs).

In this section we discuss the construction of modified boxplots using the 5 number summary statistics of our data. We discuss the calculation of outliers based on the location of fences using the IQR. Multiple boxplots are used in comparative displays to discuss the differences in the features of distributions.

Describing Distributions and Graphical Displays. Features of a graph including Center, Shape, Spread and Unusual Occurrences. Each category is discussed.

Measures of Center including: mean, median and mode. Relationships of each and their use in graphical displays. Basic calculations of all measures of center. Resistant measures and trimmed mean are also discussed in this section.

Measures of Spread. Range, IQR (Inner Quartile Range), Standard Deviation, Variance and Deviations are all introduced in this section with examples and calculations. Each measure is discussed in its use to describe data.

Density curves and Z-Scores are discuessed with formulas and examples. The emperical rule is investigated along with Chebychevs lower bound inequality. Introduction to Normal Bell Shaped Curves. Transition points are introduced as well.

Introduction to Correlation and scatterplots. Pearsons correlation coefficent is developed and investigated. The rules for correlation and examples are given.

Investigating the Least Squares Regression Line. This lesson will show us the LSRL is the line of best fit. We will look at calculating the LSRL and its use as a model for linear data. We will also look at the concept of extrapolation.

Investigating the Least Squares Regression Line. This lesson will show us the LSRL is the line of best fit. We will look at calculating the LSRL and its use as a model for linear data. We will also look at the concept of extrapolation.

Residuals and error components are studied in a least squares regression setting. Coefficient of determination is discussed, defined and interpreted. Influential points and outliers are discussed in length in a least squares regression setting.

In this section we investigate residual plots to determine if data is linear. If the data is nonlinear, we transform the variables to achieve a linear model. Logarithmic, exponential, power, quadratic and reciprocal models are considered.

In this section we look at data collection and analysis. Observational studies and experiments are both considered as well as proper sampling techniques.

Types of Sampling Designs. Advantages and disadvantages of each design with important definitions and concepts in sampling. We discuss a simple random sample, stratified sampling, systematic sampling, cluster sampling and multistage sampling. Definitions of sample design and sampling frame are introduced. The importance of proper sampling is also discussed.

We introduce types of bias in sampling design and experimentation. Random digit tables introduced. Examples with biased results. Examples of types of bias are introduced in problems and designs.

Observational Study versus Experimentation. Definitions of experimental components are introduced.

Examples of Experimental Design.

Completely randomized designs versus block designed experiments. Matched pairs experiments. Randomization, replication and control of extraneous variables. Concept of confounding variables introduced.

Completely randomized designs versus block designed experiments. Matched pairs experiments. Randomization, replication and control of extraneous variables. Concept of confounding variables introduced.

Exploring random phenomena using probability and simulation. Both discrete and continuous probability models are considered and sampling distributions are introduced.

Fundamental Principle of Counting is introduced. Combinations and Permutations are introduced. Examples of counting questions with and without imposed conditions.

Sample Space, Event Space, Complement, Union, Intersection, Venn Diagrams, Mutually Exclusive Events, Disjoint Events considered.

Sample Space, Event Space, Complement, Union, Intersection, Venn Diagrams, Mutually Exclusive Events, Disjoint Events considered.

Experimental probability, law of large numbers, basic rules of probability, independence, dependence are investigated through examples. The use of complements is considered in calculating probabilities.

Conditional probability introduced. Two way and contingency tables introduced with conditional probability as well as tree diagrams.Basic rules of probability, independence, dependence are investigated through examples.

What is a simulation? The steps of a simulation are considered in this video. Introduction to random digit tables and sources of random numbers are considered. Examples of probabilities conducted with simulations. Experimental versus theoretical probability is investigated.

The concept of a random variable is introduced. Discrete probability distributions are explored. Linear transformations and linear combinations are introduced with the calculation of the mean and standard deviations for discrete distributions.

The concept of discrete distributions is discussed and the characteristics of binomial probabilities are presented. Binomial Distributions are investigated and several problems are addressed. The mean and standard deviation of binomial distributions are presented and used in context of problems.

The concept of discrete distributions is discussed and the characteristics of binomial probabilities are presented. Binomial Distributions are investigated and several problems are addressed. The mean and standard deviation of binomial distributions are presented and used in context of problems.

Geometric Probability Distributions are discussed and examples solved. Understanding the probabilities of a first success. The binomial and geometric distrubutions are compared with similarities and differences. The mean and standard deviation for geometric distrubutions are considered as well.

The Poisson probability distribution is discussed. Analyzing the probability of rare occurrences. Discrete probability distributions are compared. The mean and standard deviation of the poisson distribution and the probability density function are discussed. Several examples of Poisson distributions are solved.

Unusual Density Curves are discussed with basic geometric shapes. Probability density functions are discussed for generic continuous distributions with unusual density curves. The concept of continuous probabilities and random variables are explored. Many examples are given and solved with continuous probabilities.

We explore the continuous uniform distribution and its properties. The mean and standard deviation are explored as well as the probability distribution function. Many examples are presented and solved.

The normal distribution is discussed. Emperical rule is discussed with examples. Normal bell shaped curves are graphed and discussed. Many problems are explained and solved with normal probabilities. The concept of z scores are discussed and normal probability tables are presented.

In this section, we assess the normality of data through central limit theorem and graphical displays. Calculator functions are introduced to determine normal continuous probabilities and graphically displaying normal curves. Functions such as normalpdf, normalcdf, invnorm are discussed.

Normal approximations to binomial distributions is considered in this lesson. Approximating binomial distributions with a normal bell shaped curve is addressed with the continuity correction based on the discrete histogram. Several problems are addressed and solved.

Sampling distributions are introduced and discussed. The role of the sampling distribution is introduced to begin inferential statistics. The central limit theorem is discussed. The mean and standard deviation are discussed for sampling distributions. The concept of the mean as an unbiased estimator is presented. Z-scores for sampling distributions are introduced. Examples are presented and solved.

Estimating population parameters using statistics and testing hypothesis. In this section we introduce the construction of a confidence interval and the hypothesis test for numerical and categorical

We begin the inferential section of statistics discussing the confidence interval for a one sample mean procedure. Both z-intervals and t-intervals are discussed and the student’s t-distribution is introduced. Conditions for inference with confidence intervals are explored with Simple Random Sampling. The conditions for normality are evaluated and the calculation of the interval is broken down into its most basic form including the point estimate and the margin of error, made up of the critical value and the standard deviation of the statistic we use to estimate the population parameter value of the mean. Several examples are presented in the construction of a confidence interval. We find the value of the sample size to produce a certain value for our margin of error.

We begin the inferential section of statistics discussing the confidence interval for a one sample mean procedure. Both z-intervals and t-intervals are discussed and the student’s t-distribution is introduced. Conditions for inference with confidence intervals are explored with Simple Random Sampling. The conditions for normality are evaluated and the calculation of the interval is broken down into its most basic form including the point estimate and the margin of error, made up of the critical value and the standard deviation of the statistic we use to estimate the population parameter value of the mean. Several examples are presented in the construction of a confidence interval. We find the value of the sample size to produce a certain value for our margin of error.

We continue the inferential section of statistics discussing hypothesis tests for a one sample mean procedure. Both z-tests and t-tests are discussed the robustness of the t-distribution is introduced. We examine right tail, left tail and two tailed hypothesis tests. Conditions for inference with hypothesis tests are explored with Simple Random Sampling. The conditions for normality are evaluated and the hypothesis statements for both the null and alternative hypothesis are discussed. Calculation of the test statistic value is addressed as well as the calculation of the p-value associated with the test statistic value. Several examples are presented in the one sample hypothesis test procedures. We also discuss the matched pairs t-test using one sample hypothesis test procedures. The confidence interval is compared to a two-tailed hypothesis test.

Errors are introduced based on the decisions of hypothesis tests. Type I and Type II errors are explored and we discuss the decisions made leading to these errors and the consequences associated with making these errors. The relationship between the two errors is investigated as well as the relationship to our level of significance and the probability of a type I error. Many examples are given and we discuss the nature and consequences associated with both type I and type II errors.

In this lesson, we look at the different errors that are possible in hypothesis testing, their consequences and assess probabilities based on a hypothetical alternate mean. The power of the test is addressed and its relationship to a type II error. We also consider the values of power and probabilities associated with Type I and Type II errors and discuss what is acceptable in practice.

In this lesson, we look at sampling distributions for one sample proportions. We discuss the rules for normality and independence based on sample size and value of parameter. Several problems are presented and solved based on sample data involving proportions.

In this lesson, we look at one sample inference with proportions. Confidence Intervals and Hypothesis Tests are discussed in this lesson for one sample proportion inference. Conditions for inference are also discussed. We look at the sample size required to achieve a certain margin of error. We discuss the rules for normality and independence based on sample size and value of parameter. Several problems are presented and solved based on sample data involving proportions, using confidence intervals and hypothesis testing.

In this lesson, we look at two sample inference with means. Confidence Intervals and Hypothesis Tests are discussed in this lesson for two sample mean inference. Conditions for inference are also discussed. We look at the differences between mean difference and difference of means, from matched pairs to two independent samples. Several problems are presented and solved based on sample data involving two sample procedures, using confidence intervals and hypothesis testing. We discuss the robustness of t-inference in particular with two sample procedures.

In this lesson we discuss two sample inference with proportions. We begin by looking at the sampling distribution of the difference in population proportions. Confidence Intervals and Hypothesis Tests are conducted for the difference in population proportions. The conditions for inference are addressed.

In this lesson we discuss inference procedures for categorical data. We begin with Chi Square Goodness of Fit tests. Actual data is compared to expected data. Both Chi Square Tests for Independence and Homogeneity are then discussed. We look at two way tables for both tests and find expected counts. The Chi Square Test Statistic is studied as well as the conditions for Chi Square Inference. Examples of Hypothesis Tests are given.

In this lesson we discuss inference procedures for categorical data. We begin with Chi Square Goodness of Fit tests. Actual data is compared to expected data. Both Chi Square Tests for Independence and Homogeneity are then discussed. We look at two way tables for both tests and find expected counts. The Chi Square Test Statistic is studied as well as the conditions for Chi Square Inference. Examples of Hypothesis Tests are given.

In this lesson, we look at linear regression inference with the construction of both confidence intervals and hypothesis tests. The conditions for inference are addressed. We look at the standard error component and summarized statistical values found in tables. Regression concepts are revisted.

In this lesson, we look at linear regression inference with the construction of both confidence intervals and hypothesis tests. The conditions for inference are addressed. We look at the standard error component and summarized statistical values found in tables. Regression concepts are revisted.

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Know what's good ,
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possible dealbreakers

Develops foundational knowledge and critical thinking skills in statistics relevant to both AP Statistics exam and college-level coursework

Covers a broad range of statistics topics from descriptive statistics and graphical displays to probability and hypothesis testing

Features concise and well-structured lessons with downloadable notes for enhanced learning

Provides a comprehensive overview of elementary statistics concepts and methods essential for success in subsequent courses

Taught by an experienced instructor with a strong background in statistics and actuarial science

Save ACE the AP Statistics Exam and MASTER Elementary Statistics! to your list so you can find it easily later:

According to students, this course is engaging and helpful for preparing for the AP Statistics Exam and learning elementary statistics. The course is well-paced and features engaging assignments and clear lectures. However, some students found the exams to be difficult.

Students appreciate the course's engaging nature.

"This course is amazing"

The course's exams may be challenging for some students

Learners who complete ACE the AP Statistics Exam and MASTER Elementary Statistics! will develop knowledge and skills
that may be useful to these careers:

Statistician

A Statistician applies mathematical and statistical knowledge to collect, analyze, interpret, and present data to help businesses and organizations make informed decisions. This course helps build a foundation for a Statistician by teaching the concepts of descriptive statistics and probability, as well as the planning and conducting of statistical studies. Additionally, this course covers topics such as sampling distributions, hypothesis testing, and confidence intervals, which are all essential for success as a Statistician.

Data Analyst

A Data Analyst uses statistical and analytical methods to interpret and communicate data to help businesses and organizations make informed decisions. This course helps build a foundation for a Data Analyst by teaching the concepts of statistics, including descriptive statistics, probability, and sampling distributions. Additionally, this course covers topics such as hypothesis testing, confidence intervals, and regression analysis, which are all essential skills for success as a Data Analyst.

Market Researcher

A Market Researcher conducts research and analyzes data to help businesses and organizations understand their target market and make informed decisions. This course helps build a foundation for a Market Researcher by teaching the concepts of statistics, including descriptive statistics, probability, and sampling distributions. Additionally, this course covers topics such as hypothesis testing, confidence intervals, and regression analysis, which are all essential skills for success as a Market Researcher.

Actuary

An Actuary uses mathematical and statistical techniques to assess risk and uncertainty for insurance companies and other financial institutions. This course helps build a foundation for an Actuary by teaching the concepts of probability, statistics, and risk management. Additionally, this course covers topics such as financial mathematics and insurance principles, which are all essential skills for success as an Actuary.

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical methods to solve complex problems in business and industry. This course helps build a foundation for an Operations Research Analyst by teaching the concepts of statistics and optimization. Additionally, this course covers topics such as linear programming, simulation, and queuing theory, which are all essential skills for success as an Operations Research Analyst.

Financial Analyst

A Financial Analyst uses financial data and statistical techniques to evaluate and make recommendations on investments. This course helps build a foundation for a Financial Analyst by teaching the concepts of statistics and financial analysis. Additionally, this course covers topics such as portfolio management, risk management, and investment analysis, which are all essential skills for success as a Financial Analyst.

Business Analyst

A Business Analyst uses analytical and problem-solving skills to help businesses and organizations improve their performance. This course helps build a foundation for a Business Analyst by teaching the concepts of statistics and data analysis. Additionally, this course covers topics such as business process improvement, project management, and communication, which are all essential skills for success as a Business Analyst.

Software Engineer

A Software Engineer designs, develops, and maintains software systems. While Statistics is not a primary requirement for this role, a strong understanding of statistics may be helpful in certain areas of software engineering, such as data mining and machine learning. This course may provide a helpful overview of statistical concepts for Software Engineers interested in these areas.

Data Scientist

A Data Scientist uses statistical and analytical methods to extract insights from data to help businesses and organizations make informed decisions. This course helps build a foundation for a Data Scientist by teaching the concepts of statistics, machine learning, and data mining. Additionally, this course covers topics such as big data, cloud computing, and data visualization, which are all essential skills for success as a Data Scientist.

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical techniques to analyze and make recommendations on financial investments. This course helps build a foundation for a Quantitative Analyst by teaching the concepts of statistics, financial mathematics, and risk management. Additionally, this course covers topics such as portfolio optimization, trading strategies, and data analysis, which are all essential skills for success as a Quantitative Analyst.

Biostatistician

A Biostatistician applies statistical methods to medical and health-related data to help researchers and healthcare professionals make informed decisions. This course helps build a foundation for a Biostatistician by teaching the concepts of statistics, epidemiology, and bioinformatics. Additionally, this course covers topics such as clinical trials, data management, and statistical modeling, which are all essential skills for success as a Biostatistician.

Economist

An Economist studies the production, distribution, and consumption of goods and services to understand how economies work. While Statistics is not a primary requirement for this role, a strong understanding of statistics may be helpful in certain areas of economics, such as econometrics and economic forecasting. This course may provide a helpful overview of statistical concepts for Economists interested in these areas.

Teacher

A Teacher develops, implements, and evaluates instructional plans to help students learn. While Statistics is not a primary requirement for this role, a strong understanding of statistics may be helpful in certain areas of teaching, such as educational research and assessment. This course may provide a helpful overview of statistical concepts for Teachers interested in these areas.

Journalist

A Journalist researches, writes, and presents news stories and other content for various media outlets. While Statistics is not a primary requirement for this role, a strong understanding of statistics may be helpful in certain areas of journalism, such as data journalism and investigative reporting. This course may provide a helpful overview of statistical concepts for Journalists interested in these areas.

Lawyer

A Lawyer advises and represents clients in legal matters. While Statistics is not a primary requirement for this role, a strong understanding of statistics may be helpful in certain areas of law, such as intellectual property law and antitrust law. This course may provide a helpful overview of statistical concepts for Lawyers interested in these areas.

For more career information including salaries, visit:
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that we think will supplement your
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ACE the AP Statistics Exam and MASTER Elementary Statistics!.

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