This chapter discusses hypothesis testing for comparing means and variances between two populations or samples. It covers testing for the difference between two independent population means, two related (paired) population means, and two independent population variances. The key tests covered are the pooled variance t-test and separate variance t-test for independent samples, and the paired t-test for related samples. Examples are provided to demonstrate how to calculate the test statistic and conduct the hypothesis test to determine if the means or variances are significantly different.
De vry math 221 all discussion+ilbs latest 2016 novemberlenasour
This document provides instructions and questions for weekly discussions and assignments for a statistics course (MATH 221). It includes discussion prompts and questions for 8 weekly topics (probability, regression, normal distributions, confidence intervals, etc.). It also provides the instructions and questions for the Week 2 in-lab assignment, which involves analyzing survey data in Excel and interpreting graphs and descriptive statistics.
The document provides an overview of analysis of variance (ANOVA) techniques, including:
- One-way ANOVA to evaluate differences between three or more group means and the assumptions of one-way ANOVA.
- Partitioning total variation into between-group and within-group components.
- Computing test statistics like the F-ratio to test for differences between group means.
- Interpreting one-way ANOVA results including rejecting the null hypothesis of no difference between means.
- An example one-way ANOVA calculation and interpretation using golf club distance data.
De vry math 221 all ilabs latest 2016 novemberlenasour
This document contains instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab includes creating graphs in Excel, calculating descriptive statistics, finding probabilities and confidence intervals, and comparing distributions. Students are asked to paste graphs, calculate measures like means and standard deviations, and answer questions interpreting their results in short paragraphs. The document provides statistical concepts and formulas to guide the analysis.
This chapter discusses two-sample tests, including tests for the difference between two independent population means, the difference between two related (paired) sample means, the difference between two population proportions, and the difference between two variances. It provides the formulas and procedures for conducting Z tests, t tests, and F tests for these comparisons in situations where the population standard deviations are both known and unknown. The goal is to test hypotheses about differences between parameters of two populations or to construct confidence intervals for these differences.
De vry math221 all ilabs latest 2016 novemberlenasour
This document provides instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab involves creating graphs in Excel, calculating descriptive statistics, and finding confidence intervals. Students are asked to calculate measures like means, standard deviations, and binomial probabilities for variables measuring things like student heights, money, time spent watching TV, and coin flip results. Confidence intervals are found for sleep hours and heights by gender.
This chapter discusses chi-square tests and nonparametric tests. It begins by introducing contingency tables and how they are used to classify sample observations according to multiple characteristics. Examples are provided to demonstrate how to set up contingency tables and calculate expected frequencies. The chapter then explains how to perform chi-square tests to analyze differences between two or more proportions, test independence between categorical variables, and compare population medians using the Wilcoxon rank-sum test. Decision rules for each test are outlined. Worked examples are provided to demonstrate applying these statistical tests and interpreting the results.
This chapter discusses hypothesis testing for comparing means and variances between two populations or samples. It covers testing for the difference between two independent population means, two related (paired) population means, and two independent population variances. The key tests covered are the pooled variance t-test and separate variance t-test for independent samples, and the paired t-test for related samples. Examples are provided to demonstrate how to calculate the test statistic and conduct the hypothesis test to determine if the means or variances are significantly different.
De vry math 221 all discussion+ilbs latest 2016 novemberlenasour
This document provides instructions and questions for weekly discussions and assignments for a statistics course (MATH 221). It includes discussion prompts and questions for 8 weekly topics (probability, regression, normal distributions, confidence intervals, etc.). It also provides the instructions and questions for the Week 2 in-lab assignment, which involves analyzing survey data in Excel and interpreting graphs and descriptive statistics.
The document provides an overview of analysis of variance (ANOVA) techniques, including:
- One-way ANOVA to evaluate differences between three or more group means and the assumptions of one-way ANOVA.
- Partitioning total variation into between-group and within-group components.
- Computing test statistics like the F-ratio to test for differences between group means.
- Interpreting one-way ANOVA results including rejecting the null hypothesis of no difference between means.
- An example one-way ANOVA calculation and interpretation using golf club distance data.
De vry math 221 all ilabs latest 2016 novemberlenasour
This document contains instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab includes creating graphs in Excel, calculating descriptive statistics, finding probabilities and confidence intervals, and comparing distributions. Students are asked to paste graphs, calculate measures like means and standard deviations, and answer questions interpreting their results in short paragraphs. The document provides statistical concepts and formulas to guide the analysis.
This chapter discusses two-sample tests, including tests for the difference between two independent population means, the difference between two related (paired) sample means, the difference between two population proportions, and the difference between two variances. It provides the formulas and procedures for conducting Z tests, t tests, and F tests for these comparisons in situations where the population standard deviations are both known and unknown. The goal is to test hypotheses about differences between parameters of two populations or to construct confidence intervals for these differences.
De vry math221 all ilabs latest 2016 novemberlenasour
This document provides instructions for completing a statistics lab assignment involving analyzing data from a student survey. The lab involves creating graphs in Excel, calculating descriptive statistics, and finding confidence intervals. Students are asked to calculate measures like means, standard deviations, and binomial probabilities for variables measuring things like student heights, money, time spent watching TV, and coin flip results. Confidence intervals are found for sleep hours and heights by gender.
This chapter discusses chi-square tests and nonparametric tests. It begins by introducing contingency tables and how they are used to classify sample observations according to multiple characteristics. Examples are provided to demonstrate how to set up contingency tables and calculate expected frequencies. The chapter then explains how to perform chi-square tests to analyze differences between two or more proportions, test independence between categorical variables, and compare population medians using the Wilcoxon rank-sum test. Decision rules for each test are outlined. Worked examples are provided to demonstrate applying these statistical tests and interpreting the results.
This document provides examples for a statistics lab involving random variables, probability distributions, and confidence intervals.
[1] It asks whether rolling a die is a discrete or continuous random variable and to calculate the mean and standard deviation of rolling a 4-sided die.
[2] The mean of rolling the 4-sided die is calculated as 2.5 and the standard deviation is calculated as 1.118.
[3] It then has the student calculate descriptive statistics like the mean and median of various data samples and compares how centered they are around the true parameter.
This chapter discusses the fundamentals of hypothesis testing for one-sample tests. It introduces the concepts of the null hypothesis (H0), alternative hypothesis (H1), test statistic, critical values, significance level, Type I and Type II errors. It explains the hypothesis testing process and covers the z-test and t-test for comparing a sample mean to a hypothesized population mean. An example demonstrates a two-tailed t-test to determine if there is evidence that the average cost of hotel rooms in New York is different than the claimed mean of $168, finding insufficient evidence based on a sample.
This chapter discusses methods for comparing two population means or proportions using statistical tests. It covers tests for independent samples, including the z-test when population variances are known and the t-test when they are unknown. It also addresses paired or related samples using a z-test when the population difference variance is known, and a t-test when it is unknown. Examples are provided for hypotheses tests and confidence intervals for the difference between two means or proportions.
This document provides an overview of confidence interval estimation. It discusses constructing confidence intervals for the mean and proportion of a population. The chapter outlines how to determine confidence intervals when the population standard deviation is known or unknown. It also covers how to calculate the required sample size. The document uses examples and formulas to demonstrate how to establish point and interval estimates for a population parameter with a given level of confidence based on a random sample.
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 assignment for a statistics course. It contains 6 questions covering topics like descriptive statistics, probability, sampling, hypothesis testing, analysis of variance, and index numbers. Students are asked to answer the questions in approximately 400 words each. They are provided with the evaluation scheme and instructed to submit their answers via email or phone for review and feedback.
This document discusses statistical methods for comparing two independent sample means and two independent sample proportions. It provides steps and examples for conducting significance tests to compare population means and proportions. For means, it describes using a z-test where the test statistic is the difference between sample means divided by the pooled standard error. For proportions, it describes using a z-test where the test statistic is the difference between sample proportions divided by the pooled standard error. Examples provided show conducting these tests to analyze differences in housework hours and attitudes between years.
This document provides information and instructions for students taking the Algebra I (Common Core) Regents High School Examination. It consists of four parts with a total of 37 multiple-choice questions to be answered. Students are instructed to show all work and steps for parts requiring calculations. Formulas that may be needed are provided. The exam covers topics including linear equations, functions, statistics, and graphing. Students are not permitted to use cell phones or other electronic devices during the exam and must sign a declaration at the end.
Three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled input/output data where the desired outputs are provided, allowing the model to map inputs to outputs. Unsupervised learning involves discovering hidden patterns in unlabeled data and grouping similar data points together. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment by receiving rewards or punishments for actions.
Assignment 1 (to be submitted through the assignment submisslicservernoida
The document provides instructions for an assignment with 5 questions analyzing datasets. It includes tasks like generating scatterplots, identifying outliers, computing correlation coefficients, conducting regression analyses and hypothesis tests, and interpreting the results. Students are asked to compile their answers in a Microsoft Word document and show their work. They should pay careful attention to formatting details like rounding decimals and labeling figures.
This document provides an overview of experimental design and analysis of variance (ANOVA). It defines key terms like independent and dependent variables, experimental units, treatments, and blocks. It explains different types of experimental designs like completely randomized designs, randomized block designs, and factorial experiments. It also covers ANOVA computations and assumptions for one-way and randomized block ANOVA models. Multiple comparison procedures like Tukey's HSD are introduced to identify differences between specific treatment means. Examples are provided to demonstrate applications of one-way and randomized block ANOVA.
This document discusses using Lagrange interpolation to estimate missing values in datasets. It begins with an introduction to missing data problems and common techniques for handling missing values like deletion, mean substitution, and more. It then explains Lagrange interpolation, which uses known data points to estimate values at unknown points. The algorithm for Lagrange interpolation is presented. An example using years of experience and salary data to estimate salary for 10 years of experience is shown. The document concludes that Lagrange interpolation can be used to estimate missing values in preprocessing if the relationship between attributes is uniform. Limitations are noted if the relationship is not uniform.
This chapter discusses confidence interval estimation. It covers constructing confidence intervals for a single population mean when the population standard deviation is known or unknown, as well as confidence intervals for a single population proportion. The chapter defines key concepts like point estimates, confidence levels, and degrees of freedom. It provides examples of how to calculate confidence intervals using the normal, t, and binomial distributions and how to interpret the resulting intervals.
De vry math 221 all discussion+ilbs latest 2016 november 1lenasour
This document provides discussion questions and assignments for DeVry MATH 221 Statistics for Decision Making for weeks 1 through 7 from November 2016. It includes weekly discussion prompts on topics like helpful course resources, regression equations, probability, discrete random variables, and normal distributions. It also provides instructions and questions for weekly interactive labs covering statistical concepts like descriptive statistics, probability, binomial distributions, and confidence intervals. Students are asked to find and report means, standard deviations, probabilities, and create graphs to analyze and summarize sample data. The document is a compilation of material to help students learn and be evaluated on key statistics topics.
The document discusses statistical flaws in the Microsoft Excel software. It notes that while Excel is a useful tool for basic statistics and data presentation, it is not intended as a statistical package and has several flaws. Excel's standard deviation and variance functions can produce inaccurate results for data sets where the mean is large compared to the variation. The document demonstrates an example where Excel incorrectly reports the standard deviation as zero due to numerical limitations. It provides an alternative user-defined function for more accurate standard deviation calculations. Overall, the document warns that Excel should be used with caution for statistical analysis and is not recommended for work with serious consequences.
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.
This document provides an overview of different methods for analyzing and summarizing data, including statistics, graphs, and t-tests. It explains concepts like mean, median, range, and the Q-test. It also provides step-by-step instructions for conducting a t-test in Microsoft Excel to determine if the averages of two data sets are statistically different. Key tips are given to improve the reliability of t-test results.
Floating treatment wetlands are proposed to improve water quality in Beira Lake in Colombo, Sri Lanka. Studies have found the lake water to have high levels of nutrients, bacteria, and other pollutants that are causing eutrophication. Floating treatment wetlands would help remove these pollutants from the water naturally through bacterial activity and plant uptake without using chemicals. The wetlands provide habitat for microbes and plants to purify the water by absorbing nutrients and increasing oxygen levels. This natural water treatment approach could help restore the lake's ecosystem functionality over time.
DJ Barcio is a DJ who provides music for schools, clubs and parties. His website DJBarcio.com provides contact information for booking Nick Barcio as a DJ. The document lists DJ, Schools, Clubs, Parties and Nick Barcio, suggesting it is an advertisement for DJ services at various events.
This document provides examples for a statistics lab involving random variables, probability distributions, and confidence intervals.
[1] It asks whether rolling a die is a discrete or continuous random variable and to calculate the mean and standard deviation of rolling a 4-sided die.
[2] The mean of rolling the 4-sided die is calculated as 2.5 and the standard deviation is calculated as 1.118.
[3] It then has the student calculate descriptive statistics like the mean and median of various data samples and compares how centered they are around the true parameter.
This chapter discusses the fundamentals of hypothesis testing for one-sample tests. It introduces the concepts of the null hypothesis (H0), alternative hypothesis (H1), test statistic, critical values, significance level, Type I and Type II errors. It explains the hypothesis testing process and covers the z-test and t-test for comparing a sample mean to a hypothesized population mean. An example demonstrates a two-tailed t-test to determine if there is evidence that the average cost of hotel rooms in New York is different than the claimed mean of $168, finding insufficient evidence based on a sample.
This chapter discusses methods for comparing two population means or proportions using statistical tests. It covers tests for independent samples, including the z-test when population variances are known and the t-test when they are unknown. It also addresses paired or related samples using a z-test when the population difference variance is known, and a t-test when it is unknown. Examples are provided for hypotheses tests and confidence intervals for the difference between two means or proportions.
This document provides an overview of confidence interval estimation. It discusses constructing confidence intervals for the mean and proportion of a population. The chapter outlines how to determine confidence intervals when the population standard deviation is known or unknown. It also covers how to calculate the required sample size. The document uses examples and formulas to demonstrate how to establish point and interval estimates for a population parameter with a given level of confidence based on a random sample.
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 assignment for a statistics course. It contains 6 questions covering topics like descriptive statistics, probability, sampling, hypothesis testing, analysis of variance, and index numbers. Students are asked to answer the questions in approximately 400 words each. They are provided with the evaluation scheme and instructed to submit their answers via email or phone for review and feedback.
This document discusses statistical methods for comparing two independent sample means and two independent sample proportions. It provides steps and examples for conducting significance tests to compare population means and proportions. For means, it describes using a z-test where the test statistic is the difference between sample means divided by the pooled standard error. For proportions, it describes using a z-test where the test statistic is the difference between sample proportions divided by the pooled standard error. Examples provided show conducting these tests to analyze differences in housework hours and attitudes between years.
This document provides information and instructions for students taking the Algebra I (Common Core) Regents High School Examination. It consists of four parts with a total of 37 multiple-choice questions to be answered. Students are instructed to show all work and steps for parts requiring calculations. Formulas that may be needed are provided. The exam covers topics including linear equations, functions, statistics, and graphing. Students are not permitted to use cell phones or other electronic devices during the exam and must sign a declaration at the end.
Three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled input/output data where the desired outputs are provided, allowing the model to map inputs to outputs. Unsupervised learning involves discovering hidden patterns in unlabeled data and grouping similar data points together. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment by receiving rewards or punishments for actions.
Assignment 1 (to be submitted through the assignment submisslicservernoida
The document provides instructions for an assignment with 5 questions analyzing datasets. It includes tasks like generating scatterplots, identifying outliers, computing correlation coefficients, conducting regression analyses and hypothesis tests, and interpreting the results. Students are asked to compile their answers in a Microsoft Word document and show their work. They should pay careful attention to formatting details like rounding decimals and labeling figures.
This document provides an overview of experimental design and analysis of variance (ANOVA). It defines key terms like independent and dependent variables, experimental units, treatments, and blocks. It explains different types of experimental designs like completely randomized designs, randomized block designs, and factorial experiments. It also covers ANOVA computations and assumptions for one-way and randomized block ANOVA models. Multiple comparison procedures like Tukey's HSD are introduced to identify differences between specific treatment means. Examples are provided to demonstrate applications of one-way and randomized block ANOVA.
This document discusses using Lagrange interpolation to estimate missing values in datasets. It begins with an introduction to missing data problems and common techniques for handling missing values like deletion, mean substitution, and more. It then explains Lagrange interpolation, which uses known data points to estimate values at unknown points. The algorithm for Lagrange interpolation is presented. An example using years of experience and salary data to estimate salary for 10 years of experience is shown. The document concludes that Lagrange interpolation can be used to estimate missing values in preprocessing if the relationship between attributes is uniform. Limitations are noted if the relationship is not uniform.
This chapter discusses confidence interval estimation. It covers constructing confidence intervals for a single population mean when the population standard deviation is known or unknown, as well as confidence intervals for a single population proportion. The chapter defines key concepts like point estimates, confidence levels, and degrees of freedom. It provides examples of how to calculate confidence intervals using the normal, t, and binomial distributions and how to interpret the resulting intervals.
De vry math 221 all discussion+ilbs latest 2016 november 1lenasour
This document provides discussion questions and assignments for DeVry MATH 221 Statistics for Decision Making for weeks 1 through 7 from November 2016. It includes weekly discussion prompts on topics like helpful course resources, regression equations, probability, discrete random variables, and normal distributions. It also provides instructions and questions for weekly interactive labs covering statistical concepts like descriptive statistics, probability, binomial distributions, and confidence intervals. Students are asked to find and report means, standard deviations, probabilities, and create graphs to analyze and summarize sample data. The document is a compilation of material to help students learn and be evaluated on key statistics topics.
The document discusses statistical flaws in the Microsoft Excel software. It notes that while Excel is a useful tool for basic statistics and data presentation, it is not intended as a statistical package and has several flaws. Excel's standard deviation and variance functions can produce inaccurate results for data sets where the mean is large compared to the variation. The document demonstrates an example where Excel incorrectly reports the standard deviation as zero due to numerical limitations. It provides an alternative user-defined function for more accurate standard deviation calculations. Overall, the document warns that Excel should be used with caution for statistical analysis and is not recommended for work with serious consequences.
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.
This document provides an overview of different methods for analyzing and summarizing data, including statistics, graphs, and t-tests. It explains concepts like mean, median, range, and the Q-test. It also provides step-by-step instructions for conducting a t-test in Microsoft Excel to determine if the averages of two data sets are statistically different. Key tips are given to improve the reliability of t-test results.
Floating treatment wetlands are proposed to improve water quality in Beira Lake in Colombo, Sri Lanka. Studies have found the lake water to have high levels of nutrients, bacteria, and other pollutants that are causing eutrophication. Floating treatment wetlands would help remove these pollutants from the water naturally through bacterial activity and plant uptake without using chemicals. The wetlands provide habitat for microbes and plants to purify the water by absorbing nutrients and increasing oxygen levels. This natural water treatment approach could help restore the lake's ecosystem functionality over time.
DJ Barcio is a DJ who provides music for schools, clubs and parties. His website DJBarcio.com provides contact information for booking Nick Barcio as a DJ. The document lists DJ, Schools, Clubs, Parties and Nick Barcio, suggesting it is an advertisement for DJ services at various events.
Narayana Swamy Naik B provides his contact information and career objective of pursuing a challenging career where he can enhance his skills and contribute to an organization's success and growth. He has a M.Tech in Digital Electronics and Communication from Sree Vidyanikethan Engineering College with 7.3% and a B.Tech in Electronics and Communication from Narayana Engineering College with 71.6%. He has technical skills in C and MATLAB programming languages. His undergraduate project involved developing a real-time visual inspection system using a high-speed digital camera to detect surface defects on rail heads.
La lección se centrará en la fábula, con los estudiantes participando en dramatizaciones, lecturas y audios en el aula. Luego, se les pedirá a los estudiantes que creen su propia fábula ilustrada con personajes, tiempo y espacio.
Este documento describe diferentes métodos anticonceptivos como preservativos, píldoras, parches y aros anticonceptivos, así como enfermedades de transmisión sexual como gonorrea, sífilis, clamidia, papiloma humano, herpes genital y VIH/SIDA. Explica los síntomas, etapas y tratamientos de estas enfermedades, y destaca que los preservativos son el único método que previene tanto el embarazo como las infecciones.
El documento anuncia una sesión de trabajo organizada por PwC y la Cámara de Comercio de Mallorca sobre la nueva reforma del Código Penal español y cómo evitar la responsabilidad penal de las personas jurídicas y sus administradores. La sesión incluirá presentaciones sobre los cambios introducidos por la reforma en 2010 que estableció por primera vez la responsabilidad penal de las empresas, y sobre cómo los modelos de prevención de delitos pueden eximir a las empresas y administradores de responsabilidad penal. La sesión
Curso de Redes y Telemática para la Universidad Fidélitas (SC-625), 1 cuatrimestre del 2015, presentación de la clase 1
Para mas información visitar el sitio web www.randyvv.com
Código de ética Colegio Bilingüe Nueva GaliciaAra Gastelum
Este documento presenta el código ético de una empresa llamada Colegio Bilingüe Nueva Galicia. Describe los valores que guiarán la conducta de los empleados, incluyendo la honestidad, el respeto, la responsabilidad, la comunicación, la solidaridad, el compromiso, la lealtad, la equidad y el servicio. También explica los métodos para implementar estos valores, como cursos de capacitación y seguimiento por parte de los coordinadores. El objetivo general es promover una cultura de valores que mejore las relaciones entre empleados y
( Espiritismo) # - jose l boberg - o segredo das bem aventurançasAlencar Santana
O documento discute os ensinamentos de Jesus sobre a divindade presente em todas as pessoas. Jesus não se proclamou Deus, mas ensinou que todos têm o mesmo poder divino dentro de si e podem realizar grandes feitos se desenvolverem seu potencial interior. O documento também diferencia entre Jesus, o homem, e Cristo, a chama divina presente em todos os seres humanos independentemente de religião.
A homework assignment for PSYC 354 involves completing several statistical analyses and writing questions. The document provides instructions and data for completing single-sample t-tests, calculating percentiles and effect sizes, and hypothesis testing using z-tests. Students are asked to analyze provided data sets using SPSS and answer conceptual questions related to confidence intervals, statistical power, and descriptive statistics.
PSYC 354Homework 8Single-Sample T-TestWhen submitting this f.docxpotmanandrea
This homework assignment involves analyzing data using SPSS and answering conceptual questions about hypothesis testing, z-tests, percentiles, and effect sizes. It covers four parts: concepts, SPSS analysis using provided data sets, additional questions requiring calculations, and a cumulative section involving descriptive statistics and graphs in SPSS. Students are instructed to complete analyses in SPSS and paste outputs and graphs into their homework document along with answering written questions.
SWOT AnalysisHCS499 Version 32University of Phoenix Mater.docxssuserf9c51d
SWOT Analysis
HCS/499 Version 3
2
University of Phoenix Material
SWOT Analysis
Based on review of the performance analysis of Stevens District Hospital, consider what you perceive to be the strengths, weaknesses, opportunities and threats for this hospital.
· Strengths and weaknesses are traits internal to the hospital, i.e. strong physician loyalty to hospital, aging building, and availability of financial resources.
· Opportunities and threats are external to the hospital, such as a mall facility available for lease or a competitor hospital opening two physician practices in your market.
SWOT Analysis
Review the SWOT Analysis PowerPoint® prior to completing this assignment.
Based on the review of the Stevens District Hospital strategic planning scenario, conduct a SWOT analysis to generate a list of perceived strengths, weaknesses, opportunities, and threats for the hospital.
· Strengths and weaknesses are traits internal to the hospital (i.e., strong physician loyalty to hospital, aging building, and availability of financial resources).
· Opportunities and threats are external to the hospital, such as a mall facility available for lease or a competitor hospital opening two physician practices in your market.
Write a 700- to 1,050-word analysis that incorporates the key components of a SWOT analysis for the scenario described in Week One to generate a list of perceived strengths, weaknesses, opportunities, and threats. The analysis will include the following:
· Analyze the purpose of conducting the analysis in the context of the scenario.
· Analyze the limitations and advantages of conducting a SWOT analysis on your own (vs. with a group of stakeholders).
· Use the table provided to record your analysis of the information from the strategic planning scenario and generate two factors for each of the SWOT categories (strengths, weaknesses, opportunities, and threats).
Cite at least 1 peer-reviewed, scholarly, or similar references to support your assignment.
Click the Assignment Files tab to submit your assignment.
Table 1: SWOT Analysis
Analysis of existing information from Stevens District Hospital strategic planning scenario
Perceived strength (internal)
Analysis of existing information from Stevens District Hospital strategic planning scenario
Perceived strength (internal)
Analysis of existing information from Stevens District Hospital strategic planning scenario
Perceived weakness (internal)
Analysis of existing information from Stevens District Hospital strategic planning scenario
Perceived weakness (internal)
Analysis of existing information from Stevens District Hospital strategic planning scenario
Perceived opportunity (external)
Analysis of existing information from Stevens District Hospital strategic planning scenario
Perceived opportunity (external)
Analysis of existing information from Stevens District Hospital strategic planning scenario
Perceived threat (external)
Analysis of existing informa ...
Btm8107 8 week2 activity understanding and exploring assumptions a+ workcoursesexams1
1. We care about statistical test assumptions because violating assumptions can invalidate results. This activity explores the assumptions of normality and homogeneity of variance using SPSS on hygiene data from a music festival.
2. Histograms and P-P plots were created for hygiene variables each day, showing some non-normality. Descriptive statistics also calculated, with skewness and kurtosis indicating violation of normality assumption.
3. Levene's test showed violation of homogeneity of variance on some variables when split by university, meaning test results could be invalidated by assumption violations. Options when assumptions are violated and cases where violations may not impact intended analyses are discussed.
1. The document discusses using linear regression to analyze relationships between variables in two different datasets.
2. For the first simple linear regression, the analysis examines the relationship between hours worked per week and family income using one dataset.
3. For the second multiple linear regression, the analysis predicts depression scores based on age, education, employment, abuse, and health using a different dataset. The document provides steps to conduct both analyses in SPSS.
Histograms and Descriptive Statistics Scoring GuideCRITERIANON.docxpooleavelina
Histograms and Descriptive Statistics Scoring Guide
CRITERIA
NON-PERFORMANCE
BASIC
PROFICIENT
DISTINGUISHED
Apply the appropriate SPSS procedures for creating histograms to generate relevant output.
Does not provide SPSS output.
Provides SPSS output with errors.
Applies the appropriate SPSS procedures for creating histograms to generate relevant output.
Analyzes the histogram output, demonstrating insight and understanding of relevant data.
Interpret histogram results, including concepts of skew, kurtosis, outliers, symmetry, and modality.
Does not provide an interpretation of histogram results.
Provides an interpretation of histogram results.
Interprets histogram results, including concepts of skew, kurtosis, outliers, symmetry, and modality.
Evaluates histogram results, including concepts of skew, kurtosis, outliers, symmetry, and modality.
Analyze the strengths and limitations of examining a distribution of scores with a histogram.
Does not identify the strengths and limitations of examining a distribution of scores with a histogram.
Identifies the strengths and limitations of examining a distribution of scores with a histogram.
Analyzes the strengths and limitations of examining a distribution of scores with a histogram.
Evaluates the strengths and limitations of examining a distribution of scores with a histogram. Demonstrates insight and understanding of relevant data.
Apply the appropriate SPSS procedure for generating descriptive statistics to generate relevant output.
Does not provide SPSS output.
Includes some, but not all, of the required output. Numerous errors in SPSS output.
Applies the appropriate SPSS procedure for generating descriptive statistics to generate relevant output.
Applies the appropriate SPSS procedure for generating descriptive statistics to generate relevant output. Includes all relevant output; no irrelevant output is included. No errors in SPSS output.
Analyze meaningful versus meaningless variables reported in descriptive statistics.
Does not identify meaningful versus meaningless variables reported in descriptive statistics.
Identifies meaningful versus meaningless variables reported in descriptive statistics.
Analyzes meaningful versus meaningless variables reported in descriptive statistics.
Evaluates meaningful versus meaningless variables reported in descriptive statistics.
Interpret descriptive statistics for meaningful variables.
Does not identify meaningful variables.
Identifies meaningful variables.
Interprets descriptive statistics for meaningful variables.
Evaluates descriptive statistics for meaningful variables.
Apply the appropriate SPSS procedures for creating z scores and descriptive statistics to generate relevant output.
Does not provide SPSS output.
Provides SPSS output with errors.
Applies the appropriate SPSS procedures for creating z scores and descriptive statistics to generate relevant output.
Analyzes the z scores and descriptive statistics output, demonstrating insight and understand ...
This document contains 26 multiple choice questions covering a variety of statistical concepts. These include questions about variables, measures of central tendency, percentiles, standard deviation, sampling, levels of measurement, and experimental design. The questions require calculating statistics like mean, median, mode, and standard deviation from data sets, interpreting graphs like histograms and box plots, and identifying properties of and relationships between statistical concepts.
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docxcargillfilberto
Assessment 3 – Hypothesis, Effect Size, Power, and
t
Tests
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Hypothesis, Effect Size, and Power
Problem Set 3.1: Sampling Distribution of the Mean Exercise
Criterion:
Interpret population mean and variance.
Instructions:
Read the information below and answer the questions.
Suppose a researcher wants to learn more about the mean attention span of individuals in some hypothetical population. The researcher cites that the attention span (the time in minutes attending to some task) in this population is normally distributed with the following characteristics: 20
36
. Based on the parameters given in this example, answer the following questions:
1. What is the population mean (μ)? __________________________
2. What is the population variance
? __________________________
3. Sketch the distribution of this population. Make sure you draw the shape of the distribution and label the mean plus and minus three standard deviations.
Problem Set 3.2: Effect Size and Power
Criterion:
Explain effect size and power.
Instructions:
Read each of the following three scenarios and answer the questions.
Two researchers make a test concerning the effectiveness of a drug use treatment. Researcher A determines that the effect size in the population of males is
d
= 0.36; Researcher B determines that the effect size in the population of females is
d
= 0.20. All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning the levels of marital satisfaction among military families. Researcher A collects a sample of 22 married couples (
n
= 22); Researcher B collects a sample of 40 married couples (
n
= 40). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning standardized exam performance among senior high school students in one of two local communities. Researcher A tests performance from the population in the northern community, where the standard deviation of test scores is 110 (
); Researcher B tests performance from the population in the southern community, where the standard deviation of test scores is 60 (
). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Problem Set 3.3: Hypothesis, Direction, and Population Mean
Criterion:
Explain the relationship between hypothesis, tests, and population mean.
Instructions:
Read the following and answer the questions.
This document demonstrates how to simulate experimental data in Excel and R to gain insights into study design and statistical analysis. It shows how to generate random normal distributions to represent two groups, with and without an effect added, and then perform t-tests on the simulated data. Running many such simulations allows understanding of false positive rates, statistical power for different sample sizes, and other statistical properties before collecting real data. The key benefits of simulation include anticipating study design issues, clarifying optimal analysis methods, and performing power analyses to determine appropriate sample sizes.
STAT 200 Final ExaminationFall 2016 OL3Page 4 of 9Answer all 2.docxwhitneyleman54422
STAT 200 Final ExaminationFall 2016 OL3Page 4 of 9
Answer all 20 questions. Make sure your answers are as complete as possible. Show all of your work and reasoning. In particular, when there are calculations involved, you must show how you come up with your answers with critical work and/or necessary tables. Answers that come straight from calculators, programs or software packages without explanation will not be accepted. If you need to use technology to aid in your calculation, you have to cite the source and explain how you get the results. For example, state the Excel function along with the required parameters when using Excel; describe the detailed steps when using a hand-held calculator; or provide the URL and detailed steps when using an online calculator, and so on.
Show all supporting work and write all answers in the spaces allotted on the following pages. You may type your work using plain-text formatting or an equation editor, or you may hand-write your work and scan it. In either case, show work neatly and correctly, following standard mathematical conventions. Each step should follow clearly and completely from the previous step. If necessary, you may attach extra pages.
Record your answers and work.
Problem Number
Solution
1
Answer:
(a)
(b)
(c)
(d)
(e)
Justification:
2
Answer:
(a)
(b)
(c)
Justification:
3
Answer:
(a)
(b)
Justification:
4
Answer:
(a)
IQ Scores
Frequency
Relative Frequency
50 - 69
23
70 - 89
0.250
90 -109
450
110 - 129
130 - 149
23
Total
1000
(b)
(c)
Work for (a) and (b):
5
Answer:
(a)
(b)
(c)
Justification:
6
Answer:
(a)
(b)
Work for (a) and (b):
7
Answer:
(a)
(b)
Work for (b):
8
Answer:
(a)
(b)
Work for (a) and (b):
9
Answer:
(a)
(b)
Work for (a) and (b):
10
Answer:
Work:
11
Answer:
(a)
(b)
Work for (b) :
12
Answer:
(a)
(b)
Work for (a) and (b):
13
Answer:
(a)
(b)
Work for (a) and (b):
14
Answer:
Work:
15
Answer:
Work:
16
Answer:
(a)
(b)
(c)
(d)
Work for (b) and (c):
17
Answer:
(a)
(b)
(c)
(d)
Work for (b) and (c):
18
Answers:
(a)
(b)
(c)
(d).
Work for (b) and (c):
19
Answer:
(a)
(b)
(c)
(d)
Work for (b) and (c):
20
Answer:
(a)
(b)
Work for (a) and (b):
STAT 200: Introduction to Statistics Final Examination, Fall 2016 OL3 Page 2 of 8
1. True or False. Justify for full credit.
(a) A is an event, and Ac is the complement of A, then P(A AND Ac ) = 0.
(b) If the variance of a data set is 0, then all the observations in this data set must be zero.
(c) If a 95% confidence interval for a population mean contains 1, then the 99% confidence
interval for the same parameter must contain 1
(d) When plotted on the same gra.
TitleABC123 Version X1Time to Practice – Week Three .docxedwardmarivel
Title
ABC/123 Version X
1
Time to Practice – Week Three
PSYCH/625 Version 1
2
University of Phoenix Material
Time to Practice – Week Three
Complete both Part A and Part B below.
Part A
Some questions in Part A require that you access data from Statistics for People Who (Think They) Hate Statistics. This data is available on the student website under the Student Test Resources link.
1. For the following research questions, create one null hypothesis, one directional research hypothesis, and one nondirectional research hypothesis.
a. What are the effects of attention on out-of-seat classroom behavior?
Research Hypothesis: There will be a relationship between the effects of attention on out-of-seat classroom behavior versus in-seat-classroom behavior.
b. What is the relationship between the quality of a marriage and the quality of the spouses’ relationships with their siblings?
Null Hypothesis: There will be no relationship in the relationship between the quality of a marriage and the quality of the spouses’ relationship with their siblings.
c. What is the best way to treat an eating disorder?
One Directional Research Hypothesis:
2. Provide one research hypothesis and an equation for each of the following topics:
a. The amount of money spent on food among undergraduate students and undergraduate student-athletes
b. The average amount of time taken by white and brown rats to get out of a maze
c. The effects of Drug A and Drug B on a disease
d. The time to complete a task in Method 1 and Method 2
3. Why does the null hypothesis presume no relationship between variables?
4. Create a research hypothesis tested using a one-tailed test and a research hypothesis tested using a two-tailed test.
5. What does the critical value represent?
6. Given the following information, would your decision be to reject or fail to reject the null hypothesis? Setting the level of significance at .05 for decision making, provide an explanation for your conclusion.
a. The null hypothesis that there is no relationship between the type of music a person listens to and his crime rate (p < .05).
In Hypothesis Testing, we typically deem a research hypothesis to be significant, if the odds of two means actually being equal are no greater than 1 in 20 or .05 (5%) or less.
b. The null hypothesis that there is no relationship between the amount of coffee consumption and GPA (p = .62).
c. The null hypothesis that there is a negative relationship between the number of hours worked and level of job satisfaction (p = .51).
7. Why is it harder to find a significant outcome (all other things being equal) when the research hypothesis is being tested at the .01 rather than the .05 level of significance?
At the .01 level, there is less room for error because the test is more rigorous.
8. Why should we think in terms of “failing to reject” the null rather than just accepting it?
9. When is it appropriate to use the one-sample z test?
10. What similarity does a z test have ...
Homework 1
Introduction to Statistics
Be sure you have reviewed this module/week’s lesson and presentations before proceeding to the homework exercises. Number all responses. Review the “Homework Instructions: General” document for an example of how homework assignments must look.
Homework 1 does not include any SPSS output and consists only of Part I.
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1. PSYC 354
Page 1 of 18
PSYC354 Homework 5 complete solutions correct answers key
Find the answer at
http://www.coursemerit.com/solution-details/14939/PSYC354-Homework-5-complete-solutions-correct-answers-key
Z-Scores
Complete all analyses in SPSS, then copy and paste your output and graphs into
your homework document file. Answer any written questions (such as the text-
based questions or the APA Participants section) in the appropriate place within
the same file.
Part I: Questions 4-8
Remember to show work to receive partial credit where applicable. For help
working on these problems, refer to the presentation from this module/week on the
normal curve and computing z-scores.
4) Calculating z scores from raw scores: If a population has a mean of m=198 and
a standard deviation of s = 20, calculate z scores for each of the following raw
scores (X) from this population. Show work on the right hand side, put answers on
the left in the space provided.
4a) X = 210; Z = Answer
Work:
4b) X = 231; Z = Answer
Work:
4c) X = 179; Z = Answer
Work:
4d) X = 163; Z = Answer
Work:
2. PSYC 354
Page 2 of 18
5) Calculating raw scores from z scores: If a population has a mean of m=198 and
a standard deviation of s = 20, calculate raw scores (X) for each of the following z
scores from this population. Show work on the right hand side, put answers on the
left in the space provided.
5a) Z = .56; X = Answer
Work:
5b) Z = -2.44; X = Answer
Work:
5c) Z = -1.0; X = Answer
Work:
5d) Z = 1.83; X = Answer
Work:
6) In a normal curve, what percentage of scores falls:
6a) Above the mean? Answer
Work:
6b) Between -1 and +1 standard deviations (SD) from the mean? Answer
Work:
6c) Beyond 2 SD’s away from the mean (in the tails on both sides)? Answer
Work:
6d) Between the mean and 2 SD’s above the mean? Answer
Work:
7) Compute the standard error (sm) for each of the following sample sizes,
assuming a population mean of 125 and a standard deviation of 20.
7a) 40 Answer
3. PSYC 354
Page 3 of 18
Work:
7b) 140 Answer
Work:
7c) 1400 Answer
Work:
8) Compute a z-statistic for each of the following sample means, assuming the
population has a mean of 100 and a standard deviation of 30 (Remember to
compute sM before computing the z statistic!)
8a) A sample of 32 scores has a mean of 113 Answer
Work:
8b) A sample of 80 scores has a mean of 95 Answer
Work:
8c) A sample of 50 scores has a mean of 100. Answer
Work:
4. PSYC 354
Page 4 of 18
Part II: SPSS Analysis
Module 5 Lesson 21 Exercise File 1
Open the “Lesson 21 Exercise File 1” document (found in the course’s Assignment
Instructions folder) in order to complete these exercises.
5. PSYC 354
Page 5 of 18
Part II:
Exercises 1a-1d
Use file: Module 5 Lesson 21 Exercise File 1
Using the data set (answers will be pasted into the blanks below this summary):
· a) Create a histogram of the raw scores
· b) Transform the raw scores to z-scores
o Label the new variable “z_anxiety”
· Paste Descriptive Statistics Table of the raw anxiety scores
o Note that descriptive statistics should describe the original raw scores and not
the new z scores
· c) Identify the z-score that is closest to 0 and farthest from 0.
· d) Evaluate whether the scores are normally distributed.
o Support your answer.
1a)
Create a histogram of the anxiety raw scores and paste it below.
Answer: Histogram
6. PSYC 354
Page 6 of 18
1b)
Using the descriptives method covered in the presentation and chapter, transform
the anxiety raw scores to z-scores, creating a new variable called “z_anxiety.”
Paste the output of descriptive statistics in the cell below.
These descriptive statistics should describe the original raw scores and not the new
z-scores.
Answer: Descriptive Statistics Table
1c)
What is the z-score that is closest to 0 (on either side of the mean) in the data set?
What is the z-score that is the farthest from 0 (on either side of the mean) in the
data set?
7. PSYC 354
Page 7 of 18
Answer
Answer
1d)
Based on the histogram from (1a) and your other answers above, would you
describe the anxiety data as being normally distributed? Why or why not? Support
your answer with information from the chapter and presentations regarding normal
and standard normal z-distributions.
Answer
Justification
9. PSYC 354
Page 9 of 18
Part III: SPSS Data Entry and Analysis
Data provided below.
IQ Scores
79
120
104
145
108
100
115
107
60
122
105
87
10. PSYC 354
Page 10 of 18
98
124
82
93
89
123
117
104
112
96
88
98
105
91
113
123
124
90
Part III:
Questions 1a-1e
The data in the columns to the left represent IQ scores of a sample of 30 high
school students. In the general population, IQ scores have a mean of 100 and a
standard deviation of 15. Enter this data into SPSS. Be sure to save this file, since
you will be using it next week as well.
11. PSYC 354
Page 11 of 18
· Generate descriptive statistics for this variable.
· Generate a histogram for this variable.
· In your data set, standardize the IQ scores by transforming them into z-
scores
o Label the new variable “ZIQ”
· Which z-scores corresponds to a raw IQ score of 115, 79 and 107?
· Does the distribution reflect the distribution in the general population?
o Support your answer.
1-a)
Generate descriptive statistics for this variable.
Answer: Descriptive Statistics Table
12. PSYC 354
Page 12 of 18
1-b)
Generate a histogram for this variable.
Answer: Histogram
1-c)
In your data set, standardize the IQ scores by transforming them into z-scores
under a new variable “ZIQ.”
Using your data set as a reference, what z-score corresponds to a raw IQ score of
115?
To a raw IQ score of 79? To a raw IQ score of 107?
115
Answer
79
Answer
13. PSYC 354
Page 13 of 18
107
Answer
1-d)
Based on what you have been told about IQ scores in the beginning of the problem,
does this sample’s distribution seem to reflect the distribution of IQ scores in the
general population?
Why or why not?
Answer
Justification
ASPD
Diagnosis
15. PSYC 354
Page 15 of 18
Part IV:
Questions 2a & 2b (SPSS)
A forensic psychologist wants to examine the level of narcissistic personality traits
in those who are diagnosed with antisocial personality disorder (ASPD) and those
who do not qualify for ASPD within a local prison population. She administers a
measure of narcissistic personality traits where higher scores indicate higher levels
of narcissism and scores range from 0–35.
· Create a new SPSS data file for these scores.
· Your file must have 2 variables: Diagnosis and Score.
· Your diagnosis variable must be set up as a 1-column grouping variable
with 2 groups (diagnosis, no diagnosis) coded numerically. This will be much like
the gender variable you created in a previous module/week.
o For example, if you code ASPD Diagnosis as 1 and No ASPD Diagnosis as 2,
then the SPSS file will appear somewhat like the following:
Column 1
Column 2
“Diagnosis”
“Score”
1
23
1
11
1
19
16. PSYC 354
Page 16 of 18
· All ASPD Diagnosis scores from the table above will appear in a similar
fashion.
· Then, continuing in the same columns, enter No ASPD Diagnosis
information as:
Column 1
Column 2
2
10
2
8
2
19
[Continue in this fashion to the end of the file]
· a) Compute descriptive statistics by diagnosis (that is, for each of the
two groups in one table) using similar steps to those covered in Green and
Salkind’s Lesson 21 and in the Module/Week 3 presentation (HS GPA scores by
Gender).
· b) Construct a boxplot to show the difference between the mean scores
of the 2 groups
17. PSYC 354
Page 17 of 18
2-a)
Compute descriptive statistics by diagnosis (that is, for each of the two groups in
one table) (2 pts)
Answer: SPSS Table- Descriptive Statistics for Score (level of narcissistic
personality) grouped by Diagnosis (ASPD/No ASPD):
[Paste one table]
2-b)
Construct a boxplot to show the difference between the mean scores of the 2
groups. (3 pts)
Answer: Boxplot