As currently taught in the United States, introductory courses in analytical chemistryemphasize …

As currently taught in the United States, introductory courses in analytical chemistryemphasize quantitative (and sometimes qualitative) methods of analysis along with a heavydose of equilibrium chemistry. Analytical chemistry, however, is much more than a collection ofanalytical methods and an understanding of equilibrium chemistry; it is an approach to solvingchemical problems. Although equilibrium chemistry and analytical methods are important, theircoverage should not come at the expense of other equally important topics.

The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum forexploring topics such as experimental design, sampling, calibration strategies, standardization,optimization, statistics, and the validation of experimental results. Analytical methods comeand go, but best practices for designing and validating analytical methods are universal. Becausechemistry is an experimental science it is essential that all chemistry students understand theimportance of making good measurements.

My goal in preparing this textbook is to find a more appropriate balance between theoryand practice, between “classical” and “modern” analytical methods, between analyzing samplesand collecting samples and preparing them for analysis, and between analytical methods anddata analysis. There is more material here than anyone can cover in one semester; it is myhope that the diversity of topics will meet the needs of different instructors, while, perhaps,suggesting some new topics to cover.

Apply the sampling distribution of the sample mean as summarized by the …

Apply the sampling distribution of the sample mean as summarized by the Central Limit Theorem (when appropriate). In particular, be able to identify unusual samples from a given population.

Systematic error, or 'bias' is of particular importance in any epidemiological investigation, …

Systematic error, or 'bias' is of particular importance in any epidemiological investigation, and should be avoided wherever possible. Biases will reduce the validity of any results obtained, whether it be by overestimating or underestimating the frequency of disease in a population or the association between an exposure and disease. The forms of bias covered here can only be minimised through careful study design and execution - they cannot be accounted for in the analysis. Although confounding is considered by many authors as a form of bias, it can be accounted for during analysis, and so is covered separately.

This video talka about what is easily one of the most fundamental …

This video talka about what is easily one of the most fundamental and profound concepts in statistics and maybe in all of mathematics. And that's the central limit theorem.

Included in the course are introductions to each lesson, lecture slides, videos, and problem questions. Topics include:

Types of Data Sampling Techniques Qualitative Data Frequency Distributions Descriptive Statistics Variation and Position Confidence Intervals Hypothesis Testing Chi-Square Goodness of Fit Linear Regression Variance ANOVA

Learning Objectives: 1).Determine point estimates in simple cases, and make the connection …

Learning Objectives: 1).Determine point estimates in simple cases, and make the connection between the sampling distribution of a statistic, and its properties as a point estimator. 2). Explain what a confidence interval represents and determine how changes in sample size and confidence level affect the precision of the confidence interval. 3). Find confidence intervals for the population mean and the population proportion (when certain conditions are met), and perform sample size calculations.

Introductory Statistics Course covering hypothesis testing, confidence interval, sampling, probability, counting techniques, correlation, linear regression, data collection and more.

This Statistics resource was developed under the guidance and support of experienced …

This Statistics resource was developed under the guidance and support of experienced high school teachers and subject matter experts. It is presented here in multiple formats: PDF, online, and low-cost print. Statistics offers instruction in grade-level appropriate concepts and skills in a logical, engaging progression that begins with sampling and data and covers topics such as probability, random variables, the normal distribution, and hypothesis testing. This content was developed with students in mind, incorporating statistics labs, worked exercises, and additional opportunities for assessment that incorporate real-world statistical applications. For instructors, resources are available to support the implementation of the Statistics textbook, including a Getting Started Guide, direct instruction presentations, and a solutions manual.

Introduction to Statistics is a resource for learning and teaching introductory statistics. …

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic …

The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."

In this video segment adapted from NOVA, scientist Mike Garcia draws lava …

In this video segment adapted from NOVA, scientist Mike Garcia draws lava samples at the foot of the active Kilauea volcano to see if it is related to its neighboring volcano, Mauna Loa.

This is a three-credit course which covers topics that enhance the students’ …

This is a three-credit course which covers topics that enhance the students’ problem solving abilities, knowledge of the basic principles of probability/statistics, and guides students to master critical thinking/logic skills, geometric principles, personal finance skills. This course requires that students apply their knowledge to real-world problems. A TI-84 or comparable calculator is required. The course has four main units: Thinking Algebraically, Thinking Logically and Geometrically, Thinking Statistically, and Making Connections. This course is paired with a course in MyOpenMath which contains the instructor materials (including answer keys) and online homework system with immediate feedback. All course materials are licensed by CC-BY-SA unless otherwise noted.

Topics List for this Lesson: Sampling, Frequency Distributions, and GraphsMeasures of CenterMeasures of …

Topics List for this Lesson: Sampling, Frequency Distributions, and GraphsMeasures of CenterMeasures of VarianceNormal Distributions and Problem SolvingZ-Scores and Unusual ValuesEmpirical Rule and Central Limit TheoremScatterplots, Correlation, and Regression

Four full-year digital course, built from the ground up and fully-aligned to …

Four full-year digital course, built from the ground up and fully-aligned to the Common Core State Standards, for 7th grade Mathematics. Created using research-based approaches to teaching and learning, the Open Access Common Core Course for Mathematics is designed with student-centered learning in mind, including activities for students to develop valuable 21st century skills and academic mindset.

Samples and ProbabilityType of Unit: ConceptualPrior KnowledgeStudents should be able to:Understand the …

Samples and ProbabilityType of Unit: ConceptualPrior KnowledgeStudents should be able to:Understand the concept of a ratio.Write ratios as percents.Describe data using measures of center.Display and interpret data in dot plots, histograms, and box plots.Lesson FlowStudents begin to think about probability by considering the relative likelihood of familiar events on the continuum between impossible and certain. Students begin to formalize this understanding of probability. They are introduced to the concept of probability as a measure of likelihood, and how to calculate probability of equally likely events using a ratio. The terms (impossible, certain, etc.) are given numerical values. Next, students compare expected results to actual results by calculating the probability of an event and conducting an experiment. Students explore the probability of outcomes that are not equally likely. They collect data to estimate the experimental probabilities. They use ratio and proportion to predict results for a large number of trials. Students learn about compound events. They use tree diagrams, tables, and systematic lists as tools to find the sample space. They determine the theoretical probability of first independent, and then dependent events. In Lesson 10 students identify a question to investigate for a unit project and submit a proposal. They then complete a Self Check. In Lesson 11, students review the results of the Self Check, solve a related problem, and take a Quiz.Students are introduced to the concept of sampling as a method of determining characteristics of a population. They consider how a sample can be random or biased, and think about methods for randomly sampling a population to ensure that it is representative. In Lesson 13, students collect and analyze data for their unit project. Students begin to apply their knowledge of statistics learned in sixth grade. They determine the typical class score from a sample of the population, and reason about the representativeness of the sample. Then, students begin to develop intuition about appropriate sample size by conducting an experiment. They compare different sample sizes, and decide whether increasing the sample size improves the results. In Lesson 16 and Lesson 17, students compare two data sets using any tools they wish. Students will be reminded of Mean Average Deviation (MAD), which will be a useful tool in this situation. Students complete another Self Check, review the results of their Self Check, and solve additional problems. The unit ends with three days for students to work on Gallery problems, possibly using one of the days to complete their project or get help on their project if needed, two days for students to present their unit projects to the class, and one day for the End of Unit Assessment.

Students will apply their knowledge of statistics learned in sixth grade. They …

Students will apply their knowledge of statistics learned in sixth grade. They will determine the typical class score from a sample of the population, and reason about the representativeness of the sample.Students analyze test score data from a fictitious seventh grade class and make generalizations about district-wide results. They then compare the data to a second seventh grade class and reason about whether these are random samples. Students will review measures of center and spread as they find evidence to draw conclusions about the data.Key ConceptsSample size will be considered as it affects the conclusions of an analysis of a population.Students will review tools that they used in sixth grade to analyze data, such as measures of center and spread, and different types of graphs.Goals and Learning ObjectivesExplore sample size.Look at the effects of using a nonrandom sample.Review tools used to analyze data.

Students begin to develop intuition about appropriate sample size by conducting an …

Students begin to develop intuition about appropriate sample size by conducting an experiment. They compare different sample sizes and whether increasing the sample size improves the results.Key ConceptsSampling is a way to discover unknown characteristics about a population. The size of the sample is important in determining the accuracy of the results. Ratio and proportion are used to compare the sample to the population.Goals and Learning ObjectivesStudents will use sampling to determine the number of different color marbles in a jar.Students will explore sample size compared to population size.

Students are introduced to the concept of sampling as a method of …

Students are introduced to the concept of sampling as a method of determining characteristics of a population. They consider how a sample can be random or biased, and think of methods for randomly sampling a population to ensure that it is representative.The idea of sampling is connected to probability; a relatively small set of data (a random sample/number of trials) can be used to generalize about a population (or determine probability). A larger sample (more trials) will give more confidence in the conclusions, but how large of a sample is needed?Students also discuss what random means and how to generate a random sample. Random samples are compared to biased samples and give insight into how statistics can be misleading (intentionally or otherwise).Key ConceptsRandom samples are related to probability. In probability, the number of trials is a sample used to generalize about the probability of an event. The results in probability are random if we are looking at equally likely outcomes. If a data sample is not random, the conclusions about the population will not reflect it.Terminology introduced in this lesson:population: the entire set of objects that can be considered when asking a statistical questionsample: a subset of a population; can be random, where each object in the population is equally likely to be in the sample, or biased, where not every object in the population is equally likely to be in the sampleGoals and Learning ObjectivesIntroduce sampling as a method to generalize about a population.Discuss the concept of a random sample versus a biased sample.Determine methods to generate random samples.Understand that biased samples are sometimes used to mislead.SWD: Some students with disabilities will benefit from a preview of the goals in this lesson. Students can highlight the critical features and/or concepts and will help them to pay close attention to salient information.

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