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Statistics chapter 1 review

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The mathematical theory of statistics is easier to learn when you know the language. This module presents important terms that will be used throughout the text. Data are individual items of information that come from a population or sample.

statistics chapter 1 review

Data may be classified as qualitative categoricalquantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample.

Random sampling methods include simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Convenience sampling is a nonrandom method of choosing a sample that often produces biased data. Samples that contain different individuals result in different data.

This is true even when the samples are well-chosen and representative of the population. When properly selected, larger samples model the population more closely than smaller samples.

There are many different potential problems that can affect the reliability of a sample. Statistical data needs to be critically analyzed, not simply accepted. Some calculations generate numbers that are artificially precise. It is not necessary to report a value to eight decimal places when the measures that generated that value were only accurate to the nearest tenth.

Round off your final answer to one more decimal place than was present in the original data. This means that if you have data measured to the nearest tenth of a unit, report the final statistic to the nearest hundredth. In addition to rounding your answers, you can measure your data using the following four levels of measurement.

When organizing data, it is important to know how many times a value appears.

Practice Tests (1-4) and Final Exams

How many statistics students study five hours or more for an exam? What percent of families on our block own two pets?

Frequency, relative frequency, and cumulative relative frequency are measures that answer questions like these. A poorly designed study will not produce reliable data. There are certain key components that must be included in every experiment. To eliminate lurking variables, subjects must be assigned randomly to different treatment groups.

One of the groups must act as a control group, demonstrating what happens when the active treatment is not applied. Participants in the control group receive a placebo treatment that looks exactly like the active treatments but cannot influence the response variable. To preserve the integrity of the placebo, both researchers and subjects may be blinded. When a study is designed properly, the only difference between treatment groups is the one imposed by the researcher.

Therefore, when groups respond differently to different treatments, the difference must be due to the influence of the explanatory variable. Ethical violations in statistics are not always easy to spot. Professional associations and federal agencies post guidelines for proper conduct. It is important that you learn basic statistical procedures so that you can recognize proper data analysis. Want to cite, share, or modify this book? This book is Creative Commons Attribution License 4.

Skip to Content. Introductory Statistics Chapter Review. Table of contents. Nominal scale level: data that cannot be ordered nor can it be used in calculations Ordinal scale level: data that can be ordered; the differences cannot be measured Interval scale level: data with a definite ordering but no starting point; the differences can be measured, but there is no such thing as a ratio.Sign in.

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How to study your flashcards. Play button. Card Range To Study through. What would be a good way to obtain a safety fact on teenage drivers with a supporting chart, or graph? Answers will vary for example: a good way to obtain this information is from the police station. Researchers carried out a study to help figure whether whitening strips or bleaching gels are more effective. On the group they gathered, they put the gel on one side of each volunteers mouth, and the strips on the other.

The results showed that the patients teeth had no greater difference on one side compared to the other.Played times. Print Share Edit Delete.

statistics chapter 1 review

Live Game Live. Finish Editing. This quiz is incomplete! To play this quiz, please finish editing it. Delete Quiz. Question 1. Which is larger? A subset of the population from which data are obtained is called. What is the name of a survey that includes all members of a population being studied? Which of the following is an example of quantitative data? The color of your car.

A person's state of residence. A person's Zip Code. A Person's Height. Categorical or Quantitative? Years of schooling completed. College Major. Hours Studying. Favorite Food. Distance from School. Hair Color. Which step is the Statistical Problem Solving Process involves answering the question? Ask a Question of Interest.

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Interpret Results. Which step is the Statistical Problem Solving Process involves organizing data to make it easier to work with?If you're seeing this message, it means we're having trouble loading external resources on our website.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser. Donate Login Sign up Search for courses, skills, and videos. Watch an introduction video 2 minutes 25 seconds. Course summary. Analyzing categorical data.

Welcome to AP Statistics : Analyzing categorical data Analyzing one categorical variable : Analyzing categorical data Two-way tables : Analyzing categorical data. Distributions in two-way tables : Analyzing categorical data.

Displaying and describing quantitative data. Frequency tables and dot plots : Displaying and describing quantitative data Histograms and stem-and-leaf plots : Displaying and describing quantitative data Describing and comparing distributions : Displaying and describing quantitative data. Summarizing quantitative data. Measuring center in quantitative data : Summarizing quantitative data More on mean and median : Summarizing quantitative data Measuring spread in quantitative data : Summarizing quantitative data.

More on standard deviation optional : Summarizing quantitative data Box and whisker plots : Summarizing quantitative data. Modeling data distributions. Percentiles cumulative relative frequency : Modeling data distributions Z-scores : Modeling data distributions Effects of linear transformations : Modeling data distributions.

Density curves : Modeling data distributions Normal distributions and the empirical rule : Modeling data distributions Normal distribution calculations : Modeling data distributions. Exploring bivariate numerical data. Making and describing scatterplots : Exploring bivariate numerical data Correlation coefficients : Exploring bivariate numerical data Least-squares regression equations : Exploring bivariate numerical data. Assessing the fit in least-squares regression : Exploring bivariate numerical data.

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Study design. Sampling and observational studies : Study design Sampling methods : Study design Types of studies experimental vs.

statistics chapter 1 review

Experiments : Study design. Randomness, probability, and simulation : Probability Addition rule : Probability Multiplication rule : Probability.

Practice Tests (1-4) and Final Exams

Conditional probability : Probability. Random variables. Discrete random variables : Random variables Continuous random variables : Random variables Transforming random variables : Random variables.

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Combining random variables : Random variables Binomial random variables : Random variables Binomial mean and standard deviation formulas : Random variables Geometric random variables : Random variables.

Sampling distributions.

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What is a sampling distribution? Confidence intervals. Introduction to confidence intervals : Confidence intervals Confidence intervals for proportions : Confidence intervals Confidence intervals for means : Confidence intervals.

Significance tests hypothesis testing.Use the following information to answer the next three exercises. A grocery store is interested in how much money, on average, their customers spend each visit in the produce department. Identify the population, sample, parameter, statistic, variable, and data for this example.

This is an example of a:. Use the following information to answer the next two exercises. A health club is interested in knowing how many times a typical member uses the club in a week. They decide to ask every tenth customer on a specified day to complete a short survey including information about how many times they have visited the club in the past week. Describe a situation in which you would calculate a parameter, rather than a statistic.

Chapter 1 - Introduction to Statistics - Review - Review Exercises - Page 36: 1

The U. One question asks whether they are planning to attend a four-year college or university in the following year. Fifty percent answer yes to this question; that fifty percent is a:. Imagine that the U. This 50 percent is an example of a:. A survey of a random sample of nurses working at a large hospital asked how many years they had been working in the profession. Their answers are summarized in the following incomplete table. Fill in the blanks in the table and round your answers to two decimal places for the Relative Frequency and Cumulative Relative Frequency cells.

Describe how you might draw a random sample of 30 students from a lecture class of students. A manager wants to draw a sample, without replacement, of 30 employees from a workforce of Describe how the chance of being selected will change over the course of drawing the sample. The manager of a department store decides to measure employee satisfaction by selecting four departments at random, and conducting interviews with all the employees in those four departments.The Prerequisites Checklist page on the Department of Statistics website lists a number of courses that require a foundation of basic statistical concepts as a prerequisite.

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All of the graduate courses in the Master of Applied Statistics program heavily rely on these concepts and procedures. Therefore, it is imperative — after you study and work through this lesson — that you thoroughly understand all the material presented here. Students that do not possess a firm understanding of these basic concepts will struggle to participate successfully in any of the graduate level courses above STAT These review materials are intended to provide a review of key statistical concepts and procedures.

Specifically, the lesson reviews:. For instance, with regards to hypothesis testing, some of you may have learned only one approach — some the P -value approach, and some the critical value approach. It is important that you understand both approaches.

If the P -value approach is new to you, you might have to spend a little more time on this lesson than if not. Upon completion of this review of basic statistical concepts, you should be able to do the following:. Students are strongly encouraged to take STATthoroughly review the materials that are covered in the sections above or take additional coursework that focuses on these foundations. If you have struggled with the concepts and methods that are presented here, you will indeed struggle in any of the graduate level courses included in the Master of Applied Statistics program above STAT that expect and build on this foundation.

Breadcrumb Home Reviews Statistical concepts. Basic Statistical Concepts. Font size. Font family A A. Content Preview Arcu felis bibendum ut tristique et egestas quis: Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris Duis aute irure dolor in reprehenderit in voluptate Excepteur sint occaecat cupidatat non proident. Lorem ipsum dolor sit amet, consectetur adipisicing elit.

Odit molestiae mollitia laudantium assumenda nam eaque, excepturi, soluta, perspiciatis cupiditate sapiente, adipisci quaerat odio voluptates consectetur nulla eveniet iure vitae quibusdam? Excepturi aliquam in iure, repellat, fugiat illum voluptate repellendus blanditiis veritatis ducimus ad ipsa quisquam, commodi vel necessitatibus, harum quos a dignissimos. Close Save changes. Help F1 or? Review Materials These review materials are intended to provide a review of key statistical concepts and procedures.

Specifically, the lesson reviews: populations and parameters and how they differ from samples and statistics, confidence intervals and their interpretation, hypothesis testing procedures, including the critical value approach and the P -value approach, chi-square analysis, tests of proportion, and power analysis. Understand the general idea of hypothesis testing -- especially how the basic procedure is similar to that followed for criminal trials conducted in the United States.

Be able to distinguish between the two types of errors that can occur whenever a hypothesis test is conducted. Understand the basic procedures for the critical value approach to hypothesis testing. Understand the basic procedures for the P -value approach to hypothesis testing.

Note : These materials are NOT intended to be a complete treatment of the ideas and methods used in basic statistics. These materials and the accompanying self-assessment are simply intended as simply an 'early warning signal' for students. Save changes Close.Iceland is a beautiful country no doubt but with the services of Nordic visitor it became an unforgetable experience. Here i want to specially thank HELMUR for the promptness in resolving any issues and taking care of all our needs as we were travelling with small children aged 4 and 11.

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