This document summarizes the key topics and concepts covered in Chapter 2 of the 9th edition of the business statistics textbook "Presenting Data in Tables and Charts". The chapter discusses guidelines for analyzing data and organizing both numerical and categorical data. It then covers various methods for tabulating and graphing univariate and bivariate data, including tables, histograms, frequency distributions, scatter plots, bar charts, pie charts, and contingency tables.
Basic Business Statistics Chapter 3 Numerical Descriptive Measures
Chapters Objectives:
Learn about Measures of Center.
How to calculate mean, median and midrange
Learn about Measures of Spread
Learn how to calculate Standard Deviation, IQR and Range
Learn about 5 number summaries
Coefficient of Correlation
The Course Aim, Purpose and Learning Outcomes
Course Aim and Purpose:
This course has aims provide a practical and approach to in the use of statistics in order for the students to gain an understanding about: -
Basic statistical theory
Management statistics used in different organizations; and
Statistical techniques used to undertake research.
Learning Outcomes:
It is intended for a student to gain an understanding: -
how to use computers to undertake statistical tasks
how to explore and understand data
How to display data.
how to investigate the relationship between variables.
about statistical confidence intervals
how to use and select basic statistical hypothesis tests
This chapter aims to teach students how to compute and interpret various numerical descriptive measures of data, including measures of central tendency (mean, median, mode), variation (range, variance, standard deviation), and shape (skewness). It covers how to find quartiles and construct box-and-whisker plots. The chapter also discusses population summary measures, rules for describing variation around the mean, and interpreting correlation coefficients.
This document provides an overview of the key topics in Chapter 6 on the normal distribution, including:
1) It introduces continuous probability distributions and defines the normal distribution as the most important continuous probability distribution.
2) It explains how the normal distribution can be standardized to have a mean of 0 and standard deviation of 1, known as the standardized normal distribution.
3) It outlines the types of problems that will be solved using the normal distribution, including finding probabilities and percentiles for both the normal and standardized normal distribution.
This chapter discusses various methods for organizing and presenting data through tables and graphs. It covers techniques for categorical data like summary tables, bar charts, pie charts and Pareto diagrams. For numerical data, it discusses ordered arrays, stem-and-leaf displays, frequency distributions, histograms, frequency polygons and ogives. It also introduces methods for presenting multivariate categorical data using contingency tables and side-by-side bar charts. The goal is to choose the most effective way to summarize and communicate patterns in the data.
Chapter 8 Confidence Interval Estimation
Estimation Process
Point Estimates
Interval Estimates
Confidence Interval Estimation for the Mean ( Known )
Confidence Interval Estimation for the Mean ( Unknown )
Confidence Interval Estimation for the Proportion
This document provides an overview of sampling techniques and procedures. It begins with definitions of key terms like population, sample, and sampling. It then outlines the sampling design process, which includes defining the target population, determining the sampling frame, selecting a sampling technique, determining sample size, and executing sampling. The document categorizes sampling techniques into non-probability and probability methods. It provides examples and descriptions of specific techniques like simple random sampling, stratified sampling, and snowball sampling. Tables summarize and compare the strengths and weaknesses of different sampling approaches.
This chapter discusses various numerical descriptive measures that can be used to describe and analyze data. It covers measures of central tendency like the mean, median, and mode. It also discusses measures of variation such as the range, variance, standard deviation, and coefficient of variation. Other topics covered include quartiles, the empirical rule, box-and-whisker plots, correlation coefficients, and choosing the appropriate descriptive measure based on the characteristics of the data. The goals are to help readers compute and interpret these common statistical measures, and use them together with graphs and charts to describe and analyze data.
Basic Business Statistics Chapter 3 Numerical Descriptive Measures
Chapters Objectives:
Learn about Measures of Center.
How to calculate mean, median and midrange
Learn about Measures of Spread
Learn how to calculate Standard Deviation, IQR and Range
Learn about 5 number summaries
Coefficient of Correlation
The Course Aim, Purpose and Learning Outcomes
Course Aim and Purpose:
This course has aims provide a practical and approach to in the use of statistics in order for the students to gain an understanding about: -
Basic statistical theory
Management statistics used in different organizations; and
Statistical techniques used to undertake research.
Learning Outcomes:
It is intended for a student to gain an understanding: -
how to use computers to undertake statistical tasks
how to explore and understand data
How to display data.
how to investigate the relationship between variables.
about statistical confidence intervals
how to use and select basic statistical hypothesis tests
This chapter aims to teach students how to compute and interpret various numerical descriptive measures of data, including measures of central tendency (mean, median, mode), variation (range, variance, standard deviation), and shape (skewness). It covers how to find quartiles and construct box-and-whisker plots. The chapter also discusses population summary measures, rules for describing variation around the mean, and interpreting correlation coefficients.
This document provides an overview of the key topics in Chapter 6 on the normal distribution, including:
1) It introduces continuous probability distributions and defines the normal distribution as the most important continuous probability distribution.
2) It explains how the normal distribution can be standardized to have a mean of 0 and standard deviation of 1, known as the standardized normal distribution.
3) It outlines the types of problems that will be solved using the normal distribution, including finding probabilities and percentiles for both the normal and standardized normal distribution.
This chapter discusses various methods for organizing and presenting data through tables and graphs. It covers techniques for categorical data like summary tables, bar charts, pie charts and Pareto diagrams. For numerical data, it discusses ordered arrays, stem-and-leaf displays, frequency distributions, histograms, frequency polygons and ogives. It also introduces methods for presenting multivariate categorical data using contingency tables and side-by-side bar charts. The goal is to choose the most effective way to summarize and communicate patterns in the data.
Chapter 8 Confidence Interval Estimation
Estimation Process
Point Estimates
Interval Estimates
Confidence Interval Estimation for the Mean ( Known )
Confidence Interval Estimation for the Mean ( Unknown )
Confidence Interval Estimation for the Proportion
This document provides an overview of sampling techniques and procedures. It begins with definitions of key terms like population, sample, and sampling. It then outlines the sampling design process, which includes defining the target population, determining the sampling frame, selecting a sampling technique, determining sample size, and executing sampling. The document categorizes sampling techniques into non-probability and probability methods. It provides examples and descriptions of specific techniques like simple random sampling, stratified sampling, and snowball sampling. Tables summarize and compare the strengths and weaknesses of different sampling approaches.
This chapter discusses various numerical descriptive measures that can be used to describe and analyze data. It covers measures of central tendency like the mean, median, and mode. It also discusses measures of variation such as the range, variance, standard deviation, and coefficient of variation. Other topics covered include quartiles, the empirical rule, box-and-whisker plots, correlation coefficients, and choosing the appropriate descriptive measure based on the characteristics of the data. The goals are to help readers compute and interpret these common statistical measures, and use them together with graphs and charts to describe and analyze data.
The document provides information on bivariate analysis and cross-tabulation. It discusses how cross-tabulation allows examination of relationships between two variables and calculation of percentages to compare groups. Chi-square is introduced as a test of hypotheses about relationships between nominal or ordinal variables, requiring calculation of expected frequencies. Examples are provided to demonstrate cross-tabulation tables and chi-square calculations.
This chapter introduces the basic concepts and terminology of statistics. It discusses two main branches of statistics - descriptive statistics which involves collecting, organizing and summarizing data, and inferential statistics which allows drawing conclusions about populations from samples. The chapter also covers variables, populations, samples, parameters, statistics and how to organize and visualize data through tables, charts and graphs. It emphasizes that statistics helps turn data into useful information for decision making in business.
This chapter introduces basic concepts in business statistics including how statistics are used in business, types of data and their sources, and popular software programs like Microsoft Excel and Minitab. It discusses descriptive versus inferential statistics and reviews key terminology such as population, sample, parameters, and statistics. The chapter also covers different types of variables, levels of measurement, and considerations for properly using statistical software programs.
This chapter introduces basic probability concepts including sample spaces, events, simple probability, joint probability, and conditional probability. It defines key terms and provides examples of calculating probabilities using contingency tables and decision trees. Probability rules are examined, including the general addition rule and rules for mutually exclusive and collectively exhaustive events. The chapter also covers statistical independence, marginal probability, and Bayes' theorem for calculating conditional probabilities.
This document provides an overview of techniques for presenting numerical data in tables and charts. It discusses ordered arrays, stem-and-leaf displays, frequency distributions, histograms, polygons, ogives, bar charts, pie charts, and scatter diagrams. The chapter goals are to teach how to create and interpret these various data presentation methods using Microsoft Excel. Examples are provided for frequency distributions, histograms, polygons, and ogives to illustrate how to construct and make sense of these graphical representations of quantitative data.
Find best statistics experts to do your Business Statistics Assignments. Just send us an email at support@homeworkguru.com with your homework assignment and we will help you with the best statistics resources available online.
Basic statistics is the science of collecting, organizing, summarizing, and interpreting data. It allows researchers to gain insights from data through graphical or numerical summaries, regardless of the amount of data. Descriptive statistics can be used to describe single variables through frequencies, percentages, means, and standard deviations. Inferential statistics make inferences about phenomena through hypothesis testing, correlations, and predicting relationships between variables.
This document summarizes key concepts from Chapter 1 of an introductory statistics textbook. It defines statistics, distinguishes between populations and samples, parameters and statistics, and descriptive and inferential statistics. It also classifies data types and levels of measurement, and discusses experimental design concepts like data collection methods and sampling techniques.
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
This document provides an overview of Chapter 8 in a statistics textbook. The chapter covers statistical inference for estimating parameters of single populations, including: point and interval estimation, estimating the population mean when the standard deviation is known or unknown, estimating the population proportion, estimating the population variance, and estimating sample size. Key concepts introduced include confidence intervals, the t-distribution, chi-square distribution, and determining necessary sample size. The chapter outline and learning objectives are also summarized.
Applied Business Statistics ,ken black , ch 3 part 1AbdelmonsifFadl
This document provides an overview of descriptive statistics concepts including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation, variance), and how to calculate these measures from both ungrouped and grouped data. It defines key terms, explains how to compute various statistics, and includes example problems and solutions. The learning objectives are to understand and be able to compute different descriptive statistics and apply concepts like the empirical rule and Chebyshev's theorem.
This course introduces students to statistical techniques for business decision making. Students will learn to analyze and present business data using appropriate software and statistical tools. Topics covered include descriptive statistics, probability, sampling, hypothesis testing, regression analysis, and comparing means of two and three groups. Assessments include a midterm, project, and final exam. Statistics are used to organize and analyze information to make it more easily understood, allowing judgments about the world. Descriptive statistics describe characteristics of data sets, while inferential statistics allow inferences about populations from data samples.
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
This document provides examples of calculating relative frequency distributions and percentage frequency distributions from raw data. It defines relative frequency as the frequency divided by the total frequency, and percentage frequency as the relative frequency multiplied by 100. The first example uses data on student weights to calculate these distributions and answer questions about percentages of students within certain weight ranges. The second example calculates distributions from data on children's birthdays and estimates births on a particular day if the total births were 10,000.
This document discusses various aspects of data distributions including their shape, modality, symmetry, and skewness. It provides definitions and examples of key terms such as:
- Modality, which refers to the number of peaks in a distribution. Unimodal distributions have one peak while multimodal distributions have two or more.
- Symmetry, which means a distribution could be split down the middle to form mirror images. Asymmetric or skewed distributions have an off-center peak with a tail on one side.
- Skewness, which is assessed using measures like Pearson's coefficient and Fisher's measure that quantify the degree of asymmetry. Positive skewness indicates a right tail while negative indicates a left tail
Vogel's Approximation Method (VAM) is a method for solving transportation problems that considers penalties (opportunity costs) associated with not shipping to cells with the lowest costs. It works as follows:
1. Compute penalties for each row and column based on the difference between the lowest and second lowest costs.
2. Ship to the cell in the row or column with the largest penalty, removing that row or column.
3. Recompute penalties and repeat until all shipments are allocated. VAM finds an initial solution close to optimal with few iterations.
This chapter discusses numerical descriptive measures used to describe the central tendency, variation, and shape of data. It covers calculating the mean, median, mode, variance, standard deviation, and coefficient of variation for data. The geometric mean is introduced as a measure of the average rate of change over time. Outliers are identified using z-scores. Methods for summarizing and comparing data using these descriptive statistics are presented.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
This document provides an overview of a course on business statistics. It includes 10 chapters that cover topics like descriptive statistics, measures of central tendency, measures of dispersion, probability, and the use of Excel for statistical analysis. The document also provides learning objectives for each chapter. For example, chapter 2 focuses on descriptive statistics and covers collecting, processing, and presenting data. It aims to describe descriptive and inferential statistics and explain how to collect, classify, tabulate, and present data diagrammatically and graphically using various charts and graphs.
This chapter introduces three continuous probability distributions: the uniform, normal, and exponential distributions. It focuses on the normal distribution and how to solve various problems using it, including approximating binomial distributions with the normal. It also covers using the normal distribution to find probabilities, the correction for continuity when approximating binomials, and how to apply the exponential distribution to interarrival time problems. Examples are provided throughout to illustrate how to set up and solve different types of probability problems using these continuous distributions.
The document provides information on bivariate analysis and cross-tabulation. It discusses how cross-tabulation allows examination of relationships between two variables and calculation of percentages to compare groups. Chi-square is introduced as a test of hypotheses about relationships between nominal or ordinal variables, requiring calculation of expected frequencies. Examples are provided to demonstrate cross-tabulation tables and chi-square calculations.
This chapter introduces the basic concepts and terminology of statistics. It discusses two main branches of statistics - descriptive statistics which involves collecting, organizing and summarizing data, and inferential statistics which allows drawing conclusions about populations from samples. The chapter also covers variables, populations, samples, parameters, statistics and how to organize and visualize data through tables, charts and graphs. It emphasizes that statistics helps turn data into useful information for decision making in business.
This chapter introduces basic concepts in business statistics including how statistics are used in business, types of data and their sources, and popular software programs like Microsoft Excel and Minitab. It discusses descriptive versus inferential statistics and reviews key terminology such as population, sample, parameters, and statistics. The chapter also covers different types of variables, levels of measurement, and considerations for properly using statistical software programs.
This chapter introduces basic probability concepts including sample spaces, events, simple probability, joint probability, and conditional probability. It defines key terms and provides examples of calculating probabilities using contingency tables and decision trees. Probability rules are examined, including the general addition rule and rules for mutually exclusive and collectively exhaustive events. The chapter also covers statistical independence, marginal probability, and Bayes' theorem for calculating conditional probabilities.
This document provides an overview of techniques for presenting numerical data in tables and charts. It discusses ordered arrays, stem-and-leaf displays, frequency distributions, histograms, polygons, ogives, bar charts, pie charts, and scatter diagrams. The chapter goals are to teach how to create and interpret these various data presentation methods using Microsoft Excel. Examples are provided for frequency distributions, histograms, polygons, and ogives to illustrate how to construct and make sense of these graphical representations of quantitative data.
Find best statistics experts to do your Business Statistics Assignments. Just send us an email at support@homeworkguru.com with your homework assignment and we will help you with the best statistics resources available online.
Basic statistics is the science of collecting, organizing, summarizing, and interpreting data. It allows researchers to gain insights from data through graphical or numerical summaries, regardless of the amount of data. Descriptive statistics can be used to describe single variables through frequencies, percentages, means, and standard deviations. Inferential statistics make inferences about phenomena through hypothesis testing, correlations, and predicting relationships between variables.
This document summarizes key concepts from Chapter 1 of an introductory statistics textbook. It defines statistics, distinguishes between populations and samples, parameters and statistics, and descriptive and inferential statistics. It also classifies data types and levels of measurement, and discusses experimental design concepts like data collection methods and sampling techniques.
This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
This document provides an overview of Chapter 8 in a statistics textbook. The chapter covers statistical inference for estimating parameters of single populations, including: point and interval estimation, estimating the population mean when the standard deviation is known or unknown, estimating the population proportion, estimating the population variance, and estimating sample size. Key concepts introduced include confidence intervals, the t-distribution, chi-square distribution, and determining necessary sample size. The chapter outline and learning objectives are also summarized.
Applied Business Statistics ,ken black , ch 3 part 1AbdelmonsifFadl
This document provides an overview of descriptive statistics concepts including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation, variance), and how to calculate these measures from both ungrouped and grouped data. It defines key terms, explains how to compute various statistics, and includes example problems and solutions. The learning objectives are to understand and be able to compute different descriptive statistics and apply concepts like the empirical rule and Chebyshev's theorem.
This course introduces students to statistical techniques for business decision making. Students will learn to analyze and present business data using appropriate software and statistical tools. Topics covered include descriptive statistics, probability, sampling, hypothesis testing, regression analysis, and comparing means of two and three groups. Assessments include a midterm, project, and final exam. Statistics are used to organize and analyze information to make it more easily understood, allowing judgments about the world. Descriptive statistics describe characteristics of data sets, while inferential statistics allow inferences about populations from data samples.
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
This document provides examples of calculating relative frequency distributions and percentage frequency distributions from raw data. It defines relative frequency as the frequency divided by the total frequency, and percentage frequency as the relative frequency multiplied by 100. The first example uses data on student weights to calculate these distributions and answer questions about percentages of students within certain weight ranges. The second example calculates distributions from data on children's birthdays and estimates births on a particular day if the total births were 10,000.
This document discusses various aspects of data distributions including their shape, modality, symmetry, and skewness. It provides definitions and examples of key terms such as:
- Modality, which refers to the number of peaks in a distribution. Unimodal distributions have one peak while multimodal distributions have two or more.
- Symmetry, which means a distribution could be split down the middle to form mirror images. Asymmetric or skewed distributions have an off-center peak with a tail on one side.
- Skewness, which is assessed using measures like Pearson's coefficient and Fisher's measure that quantify the degree of asymmetry. Positive skewness indicates a right tail while negative indicates a left tail
Vogel's Approximation Method (VAM) is a method for solving transportation problems that considers penalties (opportunity costs) associated with not shipping to cells with the lowest costs. It works as follows:
1. Compute penalties for each row and column based on the difference between the lowest and second lowest costs.
2. Ship to the cell in the row or column with the largest penalty, removing that row or column.
3. Recompute penalties and repeat until all shipments are allocated. VAM finds an initial solution close to optimal with few iterations.
This chapter discusses numerical descriptive measures used to describe the central tendency, variation, and shape of data. It covers calculating the mean, median, mode, variance, standard deviation, and coefficient of variation for data. The geometric mean is introduced as a measure of the average rate of change over time. Outliers are identified using z-scores. Methods for summarizing and comparing data using these descriptive statistics are presented.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
This document provides an overview of a course on business statistics. It includes 10 chapters that cover topics like descriptive statistics, measures of central tendency, measures of dispersion, probability, and the use of Excel for statistical analysis. The document also provides learning objectives for each chapter. For example, chapter 2 focuses on descriptive statistics and covers collecting, processing, and presenting data. It aims to describe descriptive and inferential statistics and explain how to collect, classify, tabulate, and present data diagrammatically and graphically using various charts and graphs.
This chapter introduces three continuous probability distributions: the uniform, normal, and exponential distributions. It focuses on the normal distribution and how to solve various problems using it, including approximating binomial distributions with the normal. It also covers using the normal distribution to find probabilities, the correction for continuity when approximating binomials, and how to apply the exponential distribution to interarrival time problems. Examples are provided throughout to illustrate how to set up and solve different types of probability problems using these continuous distributions.
This document provides an outline and overview of Chapter 3: Descriptive Statistics from a statistics textbook. It discusses key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), measures of shape (skewness, kurtosis), and correlation. The chapter will cover calculating these statistics for both ungrouped and grouped data, and interpreting them to describe data distributions. It emphasizes that descriptive statistics are used to numerically summarize and characterize data sets.
This chapter discusses descriptive statistics including organizing and graphing qualitative and quantitative data, measures of central tendency, and measures of dispersion. It covers frequency distributions, histograms, polygons, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), skewness, and cumulative frequency distributions. The objectives are to describe and interpret graphical displays of data, compute various statistical measures, and identify shapes of distributions.
This document appears to be from Chapter 2 of the textbook "Basic Engineering Circuit Analysis" by Irwin. It discusses resistive circuits and provides solutions to example problems involving resistors. Each page contains a circuit diagram, the given information, and the calculated solution. This continues for over 150 pages, with the format and content remaining consistent throughout.
This document discusses descriptive and inferential statistics. Descriptive statistics are used to analyze and represent previously collected data through measures like frequency, range, mean, mode, and standard deviation. Variables can be nominal, ordinal, or interval. Inferential statistics are used to draw conclusions and make predictions based on descriptive statistics. Key concepts in inferential statistics include experiments, probability, population, sampling, and hypothesis testing.
Chapter 2: Frequency Distribution and GraphsMong Mara
This document discusses different types of graphs and charts that can be used to represent frequency distributions of data, including histograms, frequency polygons, ogives, bar charts, pie charts, and stem-and-leaf plots. It provides examples of how to construct each graph or chart using sample data sets and discusses key aspects of each type such as class intervals, relative frequencies, and ordering of data. Guidelines are given for determining the optimal number of classes and class widths for grouped data. Exercises at the end provide practice applying these techniques to additional data sets.
Here are the class widths, marks and boundaries for the given class intervals:
a. Class interval (ci): 4 – 8
Class Width: 4
Class Mark: 6
Class Boundary: 3.5 – 8.5
b. Class interval (ci): 35 – 44
Class Width: 9
Class Mark: 39.5
Class Boundary: 34.5 – 43.5
c. Class interval (ci): 17 – 21
Class Width: 4
Class Mark: 19
Class Boundary: 16.5 – 20.5
d. Class interval (ci): 53 – 57
Class Width: 4
Class Mark: 55
Class Boundary: 52.5 –
This document provides examples and explanations of various graphical methods for describing data, including frequency distributions, bar charts, pie charts, stem-and-leaf diagrams, histograms, and cumulative relative frequency plots. It demonstrates how to construct these graphs using sample data on student weights, grades, ages, and other examples. The goal is to help readers understand different ways to visually represent data distributions and patterns.
1. The document discusses various methods for summarizing categorical and quantitative data through tables and graphs, including frequency distributions, relative frequency distributions, bar charts, pie charts, dot plots, histograms, and ogives.
2. An example using data on customer ratings from a hotel illustrates frequency distributions and pie charts.
3. Another example using costs of auto parts demonstrates frequency distributions, histograms, and ogives.
This document provides an overview of introductory statistics concepts including:
- Descriptive statistics such as frequency distributions, histograms, and measures of central tendency are used to summarize and present data.
- Inferential statistics such as estimation and hypothesis testing are used to draw conclusions about populations based on sample data.
- Data can be organized and presented through tables, graphs including bar charts, pie charts, and scatter plots.
Graphs, charts, and tables ppt @ bec domsBabasab Patil
This document discusses various methods for organizing and presenting quantitative data, including frequency distributions, histograms, stem-and-leaf diagrams, pie charts, bar charts, line charts, scatter plots, and strategies for grouping continuous data into classes. Key topics covered include constructing frequency distributions, interpreting relative frequencies, guidelines for determining class widths and intervals, and using graphs and charts to visualize categorical and multivariate data.
This document discusses frequency distributions and how to construct them from raw data. It provides examples of creating stem-and-leaf displays, frequency tables, relative frequency tables, and cumulative frequency tables from various data sets. Key concepts covered include class width, class boundaries, tallying data, and calculating relative frequencies and percentages. Overall, the document serves as a tutorial on how to organize and summarize data using various types of frequency distributions.
This document provides an overview of different methods for displaying and describing data, including graphs, measures of center, and measures of spread. It discusses bar graphs, pie charts, stem-and-leaf plots, histograms, and calculating the mean, median, quartiles, interquartile range, and standard deviation. Examples are provided using data on football scores to demonstrate these concepts. Key terms like outliers, shape, and transformations of data are also introduced.
The document discusses various graphical methods for describing data, including bar charts, pie charts, stem-and-leaf diagrams, histograms, and cumulative relative frequency plots. It provides examples of each using sample student data on vision correction, weights, ages, and GPAs to illustrate how to construct and interpret the different graph types.
The document discusses various graphical methods for describing data, including bar charts, pie charts, stem-and-leaf diagrams, histograms, and cumulative relative frequency plots. It provides examples of each using sample student data on vision correction methods, weights, ages, and GPAs to illustrate how to construct and interpret the different graph types.
The document provides information about descriptive statistics, summarizing qualitative and quantitative data, and various methods for presenting data visually. It discusses frequency distributions, relative and percent frequency distributions, bar graphs, pie charts, dot plots, histograms, and cumulative distributions as ways to summarize data in a clear manner. Guidelines are given for selecting class widths and numbers when creating frequency distributions. Examples using data on hotel ratings and auto repair part costs are presented to illustrate the various statistical and graphical techniques.
This document summarizes techniques for organizing and presenting numerical and categorical data in tables and charts. It covers organizing numerical data using ordered arrays and stem-and-leaf displays. It also discusses tabulating and graphing numerical data using frequency distributions, histograms, polygons, and cumulative distributions tables and ogives. For categorical data, it describes organizing univariate data with summary tables and graphing it with bar charts, pie charts, and Pareto diagrams. It also addresses tabulating bivariate categorical data with contingency tables and side-by-side charts. Finally, it discusses principles of graphical excellence and common errors to avoid in data presentation.
This document contains slides summarizing concepts for summarizing qualitative and quantitative data. For qualitative data, it discusses frequency distributions, relative frequency distributions, bar graphs, and pie charts. For quantitative data, it discusses frequency distributions, histograms, measures of central tendency including mean, median, and mode, and measures of variability. Examples are provided to illustrate these concepts using data on guest ratings at a hotel and costs of car repairs.
This document summarizes techniques for organizing and presenting numerical and categorical data in tables, charts, and graphs. It covers organizing numerical data using ordered arrays and stem-and-leaf displays. It also discusses tabulating and graphing numerical data using frequency distributions, histograms, polygons, and cumulative distributions like tables and ogives. For categorical data, it describes organizing univariate data with summary tables and graphing it with pie charts, bar charts, and Pareto diagrams. Finally, it addresses tabulating bivariate categorical data using contingency tables and side-by-side charts.
The document discusses various measures used to summarize sample data, including measures of central tendency (location) and spread (dispersion). It describes how to calculate the arithmetic mean, mode, and median of raw data and frequency tables. The mean is the average value, the mode is the most frequent observation, and the median is the middle value when data is ordered from lowest to highest. For skewed data, the mode or median may better indicate central tendency than the mean. The document also introduces the interquartile range as a measure of spread and shows how to calculate percentiles from raw and grouped frequency data.
The document discusses various measures of central tendency and spread that can be used to summarize sample data. It describes the arithmetic mean as the average value found by summing all data points and dividing by the sample size. The mode is defined as the most frequent data point. The median is the middle value when data is arranged in order. The interquartile range is introduced as a measure of spread or dispersion in the data. Formulas are provided for calculating these metrics from both raw and grouped frequency data.
This lecture covers techniques for organizing and presenting data graphically, including:
- Constructing frequency distributions and histograms to organize numerical data into class intervals.
- Creating bar charts and pie charts to present categorical data by comparing frequencies or percentages.
- Examples are provided for constructing frequency distributions, histograms, bar charts, and pie charts using sample temperature and candy data sets.
- Techniques like cumulative frequency tables and ogives (cumulative percentage polygons) are also introduced.
Missing Parts I don’t think you understood the assignment.docxannandleola
Missing Parts:
I don’t think you understood the assignment. I am looking at it, all I see is where you entered
SAS codes and then that’s it. These SAS codes you inputted, I’d like to see some results, such as
these things I am about to mention:
Part I)
1. (2 pts.) Import the data into your software. Be sure to check that your data looks
exactly like the original data before proceeding! 2. (2 pts.) For BOTH of your
original quantitative variables, create TWO categorized versions based upon cutoffs
of your choice. One binary version and one multi-level version with 3-5 groups. Use
numbers for the new variables to represent the groups. No group should have less
than 10% of the overall sample. Be sure you define your groups so that they do not
overlap and you do not miss any observations. • In SPSS this can be done using
TRANSFORM and RECODE INTO DIFFERENT VARIABLE. • In SAS you need
to use a DATA step with IF-THEN statements to create the new variables. 3. (2 pts.)
Create translations which provide the range of values for the variables created in
Question 3. • In SPSS this is done in the variable view using the “Values” column. •
In SAS you need to create the formats using PROC FORMAT and then assign those
formats to the appropriate variables using a DATA step. 4. (3 pts.) Label all
variables with descriptive titles. • In SPSS this is done in the variable view using the
“Label” column. • In SAS you need to use a DATA step which includes a LABEL
statement.
All the codes I’m looking at, I didn’t need to see them, I expect to see them in a table. I’ve
similar exercises, and that’s not how they look.
PART II)
Part 2: Descriptive Summary of Each Variable 5. (6 pts.) Calculate the sample size, sample
mean, sample median, sample standard deviation, min, max, Q1, Q3, and 95% confidence
interval for the population mean for your two quantitative variables. Provide the software
output containing these results in your solution. 6. (6 pts.) Construct a histogram, boxplot,
and QQ-plot for your two quantitative variables. Provide only the graphs in your solution.
7. (8 pts.) Construct a frequency table for each of the four variables created in Question 3.
8. (6 pts.) Provide a brief discussion of the distribution of your two main variables using as
much of the information in Questions 5-7 as possible (and yet remain as concise as
possible).
Where did you do all these calculations; I didn’t see anything. I did see a histogram, that’s all I
saw. Where’s the box plot, QQ plot, there was no graph. Also, you didn’t provide any discussion.
PART III)
Part 3: Case QQ - Using the two quantitative variables 9. (2 pts.) Construct a scatterplot.
Provide only this plot in your solution. 10. (2 pts.) Regardless of whether it is appropriate,
calculate Pearson’s correlation coefficient. Provide the output containing the estimate and
the p-value. 11. (3 pts.) Regardless of whether it is a ...
Mathematical Statistics with Applications in R 2nd Edition Ramachandran Solut...Alvaradoree
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Descriptive Statistics and Data VisualizationDouglas Joubert
This document provides an overview of descriptive statistics and data visualization techniques. It discusses levels of measurement, descriptive versus inferential statistics, and univariate analysis. Various graphical methods for displaying data are also described, including frequency distributions, histograms, Pareto charts, boxplots, and scatterplots. The document aims to help readers choose appropriate analysis and visualization methods based on their research questions and data types.
As mentioned earlier, the mid-term will have conceptual and quanti.docxfredharris32
As mentioned earlier, the mid-term will have conceptual and quantitative multiple-choice questions. You need to read all 4 chapters and you need to be able to solve problems in all 4 chapters in order to do well in this test.
The following are for review and learning purposes only. I am not indicating that identical or similar problems will be in the test. As I have indicated in the class syllabus, all the exams in this course will have multiple-choice questions and problems.
Suggestion: treat this review set as you would an actual test. Sit down with your one page of notes and your calculator, and give it a try. That way you will know what areas you still need to study.
ADMN 210
Answers to Review for Midterm #1
1) Classify each of the following as nominal, ordinal, interval, or ratio data.
a. The time required to produce each tire on an assembly line – ratio since it is numeric with a valid 0 point meaning “lack of”
b. The number of quarts of milk a family drinks in a month - ratio since it is numeric with a valid 0 point meaning “lack of”
c. The ranking of four machines in your plant after they have been designated as excellent, good, satisfactory, and poor – ordinal since it is ranking data only
d. The telephone area code of clients in the United States – nominal since it is a label
e. The age of each of your employees - ratio since it is numeric with a valid 0 point meaning “lack of”
f. The dollar sales at the local pizza house each month - ratio since it is numeric with a valid 0 point meaning “lack of”
g. An employee’s identification number – nominal since it is a label
h. The response time of an emergency unit - ratio since it is numeric with a valid 0 point meaning “lack of”
2) True or False: The highest level of data measurement is the ratio-level measurement.
True (you can do the most powerful analysis with this kind of data)
3) True or False: Interval- and ratio-level data are also referred to as categorical data.
False (Interval and ratio level data are numeric and therefore quantitative, NOT qualitative….Nominal is qualitative)
4) A small portion or a subset of the population on which data is collected for conducting statistical analysis is called __________.
A sample! A population is the total group, a census IS the population, and a data set can be either a sample or a population.
5) One of the advantages for taking a sample instead of conducting a census is this:
a sample is more accurate than census
a sample is difficult to take
a sample cannot be trusted
a sample can save money when data collection process is destructive
6) Selection of the winning numbers is a lottery is an example of __________.
convenience sampling
random sampling
nonrandom sampling
regulatory sampling
7) A type of random sampling in which the population is divided into non-overlapping subpopulations is called __________.
stratified random sampling
cluster sampling
systematic random sampling
regulatory sampling
8) A ...
Lecture 3 making data sets into tables and graphsJason Edington
Here is the grouped frequency distribution table for the heights of the men in the Class Data Base, using the specifications provided:
Class Limits Class Boundaries Tally Frequency Relative Frequency
60-63 57.5-63.5 ||| 3 0.05
64-67 63.5-67.5 |||| |||| |||| || 12 0.20
68-71 67.5-71.5 |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| |||| ||||
Learning Objectives
To understand the escalating importance of logistics and supply-chain management as crucial tools for competitiveness.
To learn about materials management and physical distribution.
To learn why international logistics is more complex than domestic logistics.
To see how the transportation infrastructure in host countries often dictates the options open to the manager.
To learn why international inventory management is crucial for success.
Learning Objectives
Describe alternative organizational structures for international operations.
Highlight factors affecting decisions about the structure of international organizations.
Indicate roles for country organizations in the development of strategy and implementation of programs.
Outline the need for and challenges of controls in international operations.
Learning Objectives
Outline the process of strategic planning in the context of the global marketplace.
Examine both the external and internal factors that determine the conditions for development of strategy and resource allocation.
Illustrate how best to utilize the environmental conditions within the competitive challenges and resources of the firm to develop effective programs.
Suggest how to achieve a balance between local and regional/global priorities and concerns in the implementation of strategy.
Learning Objectives
To learn how firms gradually progress through an internationalization process.
To understand the strategic effects of internationalization.
To study the various modes of entering international markets.
To understand the role and functions of international intermediaries.
To learn about the opportunities and challenges of cooperative market development.
Learning Objectives
To gain an understanding of the need for research.
To explore the differences between domestic and international research.
To learn where to find and how to use sources of secondary information.
To gain insight into the gathering of primary data.
To examine the need for international management information systems.
Learning Objectives
To understand the special concerns that must be considered by the international manager dealing with emerging market economies.
To survey the vast opportunities for trade offered by emerging market economies.
To understand why economic change is difficult and requires much adjustment.
To become aware that privatization offers new opportunities for international trade and investment.
Learning Objectives
To review types of economic integration among countries
To examine the costs and benefits of integrative arrangements
To understand the structure of the European Union and its implications for firms within and outside Europe
To explore the emergence of other integration agreements, especially in the Americas and Asia
To suggest corporate response to advancing economic integration
Learning Objectives
To understand how currencies are traded and quoted on world financial markets
To examine the links between interest rates and exchange rates
To understand the similarities and differences between domestic sources of capital and international sources of capital
To examine how the needs of individual borrowers have changed the nature of the instruments traded on world financial markets in the past decade
To understand how the debt crises of the 1980s and 1990s are linked to the international financial markets and exchange rates
Learning Objectives
To understand the fundamental principles of how countries measure international business activity, the balance of payments
To examine the similarities of the current and capital accounts of the balance of payments
To understand the critical differences between trade in merchandise and services and why international investment activity has recently been controversial in the United States
To review the mechanical steps of how exchange rates are transmitted into altered trade prices and eventually trade volumes
To understand how countries with different government policies toward international trade and investments, or different levels of economic development, differ in their balance of payments
Learning Objectives
To learn how firms gradually progress through an internationalization process.
To understand the strategic effects of internationalization.
To study the various modes of entering international markets.
To understand the role and functions of international intermediaries.
To learn about the opportunities and challenges of cooperative market development.
Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests
Chapter Topic:
Hypothesis Testing Methodology
Z Test for the Mean ( Known)
p-Value Approach to Hypothesis Testing
Connection to Confidence Interval Estimation
One-Tail Tests
t Test for the Mean ( Unknown)
Z Test for the Proportion
Potential Hypothesis-Testing Pitfalls and Ethical Issues
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6. Facilitate Strategic Planning
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L'indice de performance des ports à conteneurs de l'année 2023SPATPortToamasina
Une évaluation comparable de la performance basée sur le temps d'escale des navires
L'objectif de l'ICPP est d'identifier les domaines d'amélioration qui peuvent en fin de compte bénéficier à toutes les parties concernées, des compagnies maritimes aux gouvernements nationaux en passant par les consommateurs. Il est conçu pour servir de point de référence aux principaux acteurs de l'économie mondiale, notamment les autorités et les opérateurs portuaires, les gouvernements nationaux, les organisations supranationales, les agences de développement, les divers intérêts maritimes et d'autres acteurs publics et privés du commerce, de la logistique et des services de la chaîne d'approvisionnement.
Le développement de l'ICPP repose sur le temps total passé par les porte-conteneurs dans les ports, de la manière expliquée dans les sections suivantes du rapport, et comme dans les itérations précédentes de l'ICPP. Cette quatrième itération utilise des données pour l'année civile complète 2023. Elle poursuit le changement introduit l'année dernière en n'incluant que les ports qui ont eu un minimum de 24 escales valides au cours de la période de 12 mois de l'étude. Le nombre de ports inclus dans l'ICPP 2023 est de 405.
Comme dans les éditions précédentes de l'ICPP, la production du classement fait appel à deux approches méthodologiques différentes : une approche administrative, ou technique, une méthodologie pragmatique reflétant les connaissances et le jugement des experts ; et une approche statistique, utilisant l'analyse factorielle (AF), ou plus précisément la factorisation matricielle. L'utilisation de ces deux approches vise à garantir que le classement des performances des ports à conteneurs reflète le plus fidèlement possible les performances réelles des ports, tout en étant statistiquement robuste.
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