- The document provides guidance on preparing for the Math 533 final exam, including sample questions on hypothesis testing, the binomial distribution, and confidence intervals.
- It also provides an example of a regression analysis conducted to determine if the number of hours studied is associated with final exam grades. The regression equation found a significant positive relationship between hours studied and exam score.
- The document provides an overview of topics that may be covered on the Math 533 final exam, including hypothesis testing, the binomial distribution, descriptive statistics, confidence intervals, and regression analysis.
- It includes examples of sample questions and worked problems for each topic to help students prepare.
The document is a study guide for the MATH533 final exam that provides important information about the exam format and content. It notes that the exam is open book and open notes, will last up to 3 hours and 30 minutes, and students should save their answers frequently. It outlines that the exam will cover all course objectives and content from weeks 1 through 7. Students can expect 10 randomly selected essay questions worth 25 points each, totaling 250 points. Proper citation is required for any borrowed content. Key areas of study include exploratory data analysis, probability, confidence intervals, hypothesis testing, and regression. The guide recommends reviewing assignments and course objectives in preparation.
Okay, here are the steps to convert each score to a z-score:
For history test:
Z = (X - Mean) / Standard Deviation
Z = (78 - 79) / 6
Z = -0.167
For math test:
Z = (X - Mean) / Standard Deviation
Z = (82 - 84) / 5
Z = 0.8
So the z-score for the history test is -0.167 and the z-score for the math test is 0.8.
This document provides a summary of key concepts and formulas for a statistics final exam. It includes examples of hypothesis tests, confidence intervals, descriptive statistics, probability calculations, and interpreting correlation. Multiple choice questions are presented along with step-by-step workings to find probabilities, means, medians, modes, variances, and conduct hypothesis testing. Formulas for regression, the normal distribution, and sampling are also reviewed along with examples of interpreting stem-and-leaf plots, sample sizes, and evaluating data normality.
This document provides examples for homework problems 17, 18, and 20 from Week 6. Example 17 constructs a 95% confidence interval for the proportion of men who wear hats using survey data. Example 18 calculates sample sizes needed for estimating a population proportion within a margin of error. Example 20 constructs 95% confidence intervals for the proportions of adults who report traffic congestion as a problem in different regions, based on survey data.
The document provides a review of topics covered in a statistics course for a final exam. It includes sample problems related to regression analysis, correlation, probability distributions, hypothesis testing, and descriptive statistics. Students are asked to calculate predictions, interpret correlation coefficients, find probabilities using the binomial and Poisson distributions, determine sample sizes, and interpret hypothesis tests, among other tasks.
Solution manual for design and analysis of experiments 9th edition douglas ...Salehkhanovic
Solution Manual for Design and Analysis of Experiments - 9th Edition
Author(s): Douglas C Montgomery
Solution manual for 9th edition include chapters 1 to 15. There is one PDF file for each of chapters.
The document provides a review of key concepts for a statistics final exam, including how to calculate regression equations and lines, probabilities using normal and binomial distributions, hypothesis testing, and other statistical analyses. It includes examples of problems and questions that may appear on the exam.
- The document provides an overview of topics that may be covered on the Math 533 final exam, including hypothesis testing, the binomial distribution, descriptive statistics, confidence intervals, and regression analysis.
- It includes examples of sample questions and worked problems for each topic to help students prepare.
The document is a study guide for the MATH533 final exam that provides important information about the exam format and content. It notes that the exam is open book and open notes, will last up to 3 hours and 30 minutes, and students should save their answers frequently. It outlines that the exam will cover all course objectives and content from weeks 1 through 7. Students can expect 10 randomly selected essay questions worth 25 points each, totaling 250 points. Proper citation is required for any borrowed content. Key areas of study include exploratory data analysis, probability, confidence intervals, hypothesis testing, and regression. The guide recommends reviewing assignments and course objectives in preparation.
Okay, here are the steps to convert each score to a z-score:
For history test:
Z = (X - Mean) / Standard Deviation
Z = (78 - 79) / 6
Z = -0.167
For math test:
Z = (X - Mean) / Standard Deviation
Z = (82 - 84) / 5
Z = 0.8
So the z-score for the history test is -0.167 and the z-score for the math test is 0.8.
This document provides a summary of key concepts and formulas for a statistics final exam. It includes examples of hypothesis tests, confidence intervals, descriptive statistics, probability calculations, and interpreting correlation. Multiple choice questions are presented along with step-by-step workings to find probabilities, means, medians, modes, variances, and conduct hypothesis testing. Formulas for regression, the normal distribution, and sampling are also reviewed along with examples of interpreting stem-and-leaf plots, sample sizes, and evaluating data normality.
This document provides examples for homework problems 17, 18, and 20 from Week 6. Example 17 constructs a 95% confidence interval for the proportion of men who wear hats using survey data. Example 18 calculates sample sizes needed for estimating a population proportion within a margin of error. Example 20 constructs 95% confidence intervals for the proportions of adults who report traffic congestion as a problem in different regions, based on survey data.
The document provides a review of topics covered in a statistics course for a final exam. It includes sample problems related to regression analysis, correlation, probability distributions, hypothesis testing, and descriptive statistics. Students are asked to calculate predictions, interpret correlation coefficients, find probabilities using the binomial and Poisson distributions, determine sample sizes, and interpret hypothesis tests, among other tasks.
Solution manual for design and analysis of experiments 9th edition douglas ...Salehkhanovic
Solution Manual for Design and Analysis of Experiments - 9th Edition
Author(s): Douglas C Montgomery
Solution manual for 9th edition include chapters 1 to 15. There is one PDF file for each of chapters.
The document provides a review of key concepts for a statistics final exam, including how to calculate regression equations and lines, probabilities using normal and binomial distributions, hypothesis testing, and other statistical analyses. It includes examples of problems and questions that may appear on the exam.
The document contains statistics lab report scores for 8 students who spent varying amounts of time preparing. It includes the regression equation relating hours spent to score and predicts a score for someone who spent 1 hour. It also defines the correlation coefficient and explains it measures the strength of the linear relationship between two variables.
This document provides a summary of key concepts and formulas for a statistics final exam. It includes examples of hypothesis tests, confidence intervals, descriptive statistics, probability calculations, and interpreting correlation. Questions cover topics like regression analysis, the normal distribution, sampling, and distinguishing between binomial and Poisson distributions. Formulas and explanations are provided for concepts like variance, standard deviation, z-scores, and determining minimum sample sizes.
The document provides a review for a statistics final exam. It includes definitions students should know, examples of hypothesis tests and probability questions, instructions on how to find probabilities using normal distributions, and other statistical concepts. It emphasizes knowing key terms, being able to perform hypothesis tests and find probabilities using normal distributions, and understanding the differences between binomial and Poisson distributions.
The document contains analyses of various datasets using R code. Key findings include:
1) For a fridge price dataset, the mean price is higher than the median price, indicating a skewed distribution.
2) Cereal fiber data follows a right-skewed distribution. The mean calories is higher than the median.
3) Changing data values by adding or multiplying a constant affects measures of central tendency but not measures of dispersion.
4) Analysis of lottery prize values finds the mean prize exceeds the ticket price, suggesting potential for profit.
5) Probability calculations are shown for events at two locations.
6) Stem-and-leaf plot and measures of central tendency are presented for
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
This document provides information on estimating population characteristics from sample data, including:
- Point estimates are single numbers based on sample data that represent plausible values of population characteristics.
- Confidence intervals provide a range of plausible values for population characteristics with a specified degree of confidence.
- Formulas are given for constructing confidence intervals for population proportions and means using large sample approximations or t-distributions.
- Guidelines for determining necessary sample sizes to estimate population values within a specified margin of error are also outlined.
This document outlines how to perform a chi-square test of independence using a contingency table. It explains that a contingency table displays the observed frequencies of two variables arranged in rows and columns. Expected frequencies are calculated for each cell assuming independence by multiplying the corresponding row and column totals and dividing by the sample size. A chi-square test can then determine if the observed frequencies differ significantly from the expected frequencies under independence.
This document outlines key concepts for constructing confidence intervals for a population mean when sample sizes are large or small. It discusses how to find point estimates and margins of error, and how to construct confidence intervals using z-scores or t-statistics depending on sample size. Examples are provided to demonstrate how to calculate critical values, margins of error, and minimum sample sizes needed to estimate population means within a given level of confidence.
This document section provides an overview of correlation and linear regression analysis. It defines correlation as a relationship between two variables and discusses different types of correlation including positive, negative, nonlinear, and no correlation. Key concepts covered include the correlation coefficient, which measures the strength and direction of linear relationships, and how to calculate and interpret the coefficient. The section also explains how to test if a population correlation is statistically significant using sample data and correlation coefficient values with tables. Examples are provided to demonstrate these techniques.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.2: Testing a Claim About a Proportion
Solutions. Design and Analysis of Experiments. MontgomeryByron CZ
This document summarizes solutions to problems from a chapter on simple comparative experiments. Key points include:
- Hypotheses are tested to compare means and variances of samples from two populations or processes.
- t-tests and F-tests are used to analyze differences in means and variances based on sample data.
- Confidence intervals are constructed to estimate population parameters based on sample statistics.
- Normality assumptions and sample sizes are considered in selecting appropriate statistical tests.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.1: Inferences about Two Proportions
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 document outlines how to perform hypothesis tests to compare the means of two independent samples. It discusses using a two-sample z-test when samples are large and normally distributed, and a two-sample t-test when samples are small. The key steps are to state the null and alternative hypotheses, calculate the test statistic, find the critical value, make a decision to reject or fail to reject the null hypothesis, and interpret the results. Examples are provided to demonstrate these tests.
This document provides examples and explanations for statistical concepts covered on a final exam, including the normal distribution, hypothesis testing, and probability distributions. It includes sample problems calculating probabilities and critical values for hypothesis tests on means and proportions. Excel templates are referenced for finding probabilities based on the standard normal and Poisson distributions. Step-by-step workings are shown for several problems to illustrate statistical calculations and interpretations.
This document provides examples for homework problems assigned in Week 5. It includes step-by-step work and explanations for problems involving normal distributions, z-scores, percentiles, and sampling distributions. The examples demonstrate how to use Minitab to find probabilities and critical values for normally distributed data. Key concepts covered include interpreting left and right tails, shifting to standardized units, and adjusting standard deviations for sampling distributions.
Quantitative Analysis For Management 11th Edition Render Solutions ManualShermanne
The document provides 10 teaching suggestions for instructors on key probability concepts. The suggestions focus on clarifying common misconceptions students have regarding probabilities ranging from 0 to 1, where probabilities come from, mutually exclusive and collectively exhaustive events, adding probabilities of events that are not mutually exclusive, using visual examples to explain dependent events, understanding random variables, expected value, the normal distribution curve, areas under the normal curve, and using normal tables. Alternative examples are also provided to illustrate each concept.
The document contains data on lab report scores of 8 students and the number of hours they spent preparing. It shows the highest possible score was 40. It then provides the regression equation to predict scores based on hours and defines the correlation coefficient. It predicts a score of 28 for someone who spent 1 hour preparing. It also defines the correlation coefficient and what it indicates about the relationship between variables.
Here are the calculations for the summary statistics:
Mean = (100 + 95 + 120 + 190 + 200 + 200 + 280) / 7 = 169.3
Median = 190
Mode = 200 (occurs 3 times)
Variance = (100-169.3)^2 + (95-169.3)^2 + ... + (280-169.3)^2 / 7 = 4553.6
Range = 280 - 100 = 185
This sample is unlikely to have come from a normal population. The mean, median and mode are all different values, which would not occur in a normal distribution. The variance is also quite high relative to the mean. So in summary, the differences between the
Quantitative Methods for Lawyers - Class #15 - R Boot Camp - Part 2 - Profess...Daniel Katz
This document provides an overview of statistical tests and calculations using R. It discusses loading and cleaning a dataset, doing basic calculations, and running statistical tests like the binomial distribution, normal distribution, hypothesis testing, chi-squared test, and F test. Examples are provided for each type of analysis, including the R code and interpretation. The goal is to demonstrate how to use R to analyze datasets and evaluate various statistical hypotheses.
This document outlines steps to solve an example problem by providing boundary conditions at four points, then integrating from each condition to obtain two equations. It attempts to substitute the second boundary condition into the first equation but results in an error.
Peter Kahn: Examples of research-led teaching at the University of Liverpool. Slides from the University of Liverpool Learning and Teaching Conference 2009.
The university is considering ways in which its teaching is led by research. This session will explore three examples of ways in which research-led learning is presently employed within the university, taken from practice within the School of Management, the School of Archaeology, Classics and Egyptology, and elsewhere. The session will begin with a brief introduction to the notion of research-led learning and teaching, and will allow time for discussion amongst participants.
The document contains statistics lab report scores for 8 students who spent varying amounts of time preparing. It includes the regression equation relating hours spent to score and predicts a score for someone who spent 1 hour. It also defines the correlation coefficient and explains it measures the strength of the linear relationship between two variables.
This document provides a summary of key concepts and formulas for a statistics final exam. It includes examples of hypothesis tests, confidence intervals, descriptive statistics, probability calculations, and interpreting correlation. Questions cover topics like regression analysis, the normal distribution, sampling, and distinguishing between binomial and Poisson distributions. Formulas and explanations are provided for concepts like variance, standard deviation, z-scores, and determining minimum sample sizes.
The document provides a review for a statistics final exam. It includes definitions students should know, examples of hypothesis tests and probability questions, instructions on how to find probabilities using normal distributions, and other statistical concepts. It emphasizes knowing key terms, being able to perform hypothesis tests and find probabilities using normal distributions, and understanding the differences between binomial and Poisson distributions.
The document contains analyses of various datasets using R code. Key findings include:
1) For a fridge price dataset, the mean price is higher than the median price, indicating a skewed distribution.
2) Cereal fiber data follows a right-skewed distribution. The mean calories is higher than the median.
3) Changing data values by adding or multiplying a constant affects measures of central tendency but not measures of dispersion.
4) Analysis of lottery prize values finds the mean prize exceeds the ticket price, suggesting potential for profit.
5) Probability calculations are shown for events at two locations.
6) Stem-and-leaf plot and measures of central tendency are presented for
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
This document provides information on estimating population characteristics from sample data, including:
- Point estimates are single numbers based on sample data that represent plausible values of population characteristics.
- Confidence intervals provide a range of plausible values for population characteristics with a specified degree of confidence.
- Formulas are given for constructing confidence intervals for population proportions and means using large sample approximations or t-distributions.
- Guidelines for determining necessary sample sizes to estimate population values within a specified margin of error are also outlined.
This document outlines how to perform a chi-square test of independence using a contingency table. It explains that a contingency table displays the observed frequencies of two variables arranged in rows and columns. Expected frequencies are calculated for each cell assuming independence by multiplying the corresponding row and column totals and dividing by the sample size. A chi-square test can then determine if the observed frequencies differ significantly from the expected frequencies under independence.
This document outlines key concepts for constructing confidence intervals for a population mean when sample sizes are large or small. It discusses how to find point estimates and margins of error, and how to construct confidence intervals using z-scores or t-statistics depending on sample size. Examples are provided to demonstrate how to calculate critical values, margins of error, and minimum sample sizes needed to estimate population means within a given level of confidence.
This document section provides an overview of correlation and linear regression analysis. It defines correlation as a relationship between two variables and discusses different types of correlation including positive, negative, nonlinear, and no correlation. Key concepts covered include the correlation coefficient, which measures the strength and direction of linear relationships, and how to calculate and interpret the coefficient. The section also explains how to test if a population correlation is statistically significant using sample data and correlation coefficient values with tables. Examples are provided to demonstrate these techniques.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.2: Testing a Claim About a Proportion
Solutions. Design and Analysis of Experiments. MontgomeryByron CZ
This document summarizes solutions to problems from a chapter on simple comparative experiments. Key points include:
- Hypotheses are tested to compare means and variances of samples from two populations or processes.
- t-tests and F-tests are used to analyze differences in means and variances based on sample data.
- Confidence intervals are constructed to estimate population parameters based on sample statistics.
- Normality assumptions and sample sizes are considered in selecting appropriate statistical tests.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.1: Inferences about Two Proportions
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 document outlines how to perform hypothesis tests to compare the means of two independent samples. It discusses using a two-sample z-test when samples are large and normally distributed, and a two-sample t-test when samples are small. The key steps are to state the null and alternative hypotheses, calculate the test statistic, find the critical value, make a decision to reject or fail to reject the null hypothesis, and interpret the results. Examples are provided to demonstrate these tests.
This document provides examples and explanations for statistical concepts covered on a final exam, including the normal distribution, hypothesis testing, and probability distributions. It includes sample problems calculating probabilities and critical values for hypothesis tests on means and proportions. Excel templates are referenced for finding probabilities based on the standard normal and Poisson distributions. Step-by-step workings are shown for several problems to illustrate statistical calculations and interpretations.
This document provides examples for homework problems assigned in Week 5. It includes step-by-step work and explanations for problems involving normal distributions, z-scores, percentiles, and sampling distributions. The examples demonstrate how to use Minitab to find probabilities and critical values for normally distributed data. Key concepts covered include interpreting left and right tails, shifting to standardized units, and adjusting standard deviations for sampling distributions.
Quantitative Analysis For Management 11th Edition Render Solutions ManualShermanne
The document provides 10 teaching suggestions for instructors on key probability concepts. The suggestions focus on clarifying common misconceptions students have regarding probabilities ranging from 0 to 1, where probabilities come from, mutually exclusive and collectively exhaustive events, adding probabilities of events that are not mutually exclusive, using visual examples to explain dependent events, understanding random variables, expected value, the normal distribution curve, areas under the normal curve, and using normal tables. Alternative examples are also provided to illustrate each concept.
The document contains data on lab report scores of 8 students and the number of hours they spent preparing. It shows the highest possible score was 40. It then provides the regression equation to predict scores based on hours and defines the correlation coefficient. It predicts a score of 28 for someone who spent 1 hour preparing. It also defines the correlation coefficient and what it indicates about the relationship between variables.
Here are the calculations for the summary statistics:
Mean = (100 + 95 + 120 + 190 + 200 + 200 + 280) / 7 = 169.3
Median = 190
Mode = 200 (occurs 3 times)
Variance = (100-169.3)^2 + (95-169.3)^2 + ... + (280-169.3)^2 / 7 = 4553.6
Range = 280 - 100 = 185
This sample is unlikely to have come from a normal population. The mean, median and mode are all different values, which would not occur in a normal distribution. The variance is also quite high relative to the mean. So in summary, the differences between the
Quantitative Methods for Lawyers - Class #15 - R Boot Camp - Part 2 - Profess...Daniel Katz
This document provides an overview of statistical tests and calculations using R. It discusses loading and cleaning a dataset, doing basic calculations, and running statistical tests like the binomial distribution, normal distribution, hypothesis testing, chi-squared test, and F test. Examples are provided for each type of analysis, including the R code and interpretation. The goal is to demonstrate how to use R to analyze datasets and evaluate various statistical hypotheses.
This document outlines steps to solve an example problem by providing boundary conditions at four points, then integrating from each condition to obtain two equations. It attempts to substitute the second boundary condition into the first equation but results in an error.
Peter Kahn: Examples of research-led teaching at the University of Liverpool. Slides from the University of Liverpool Learning and Teaching Conference 2009.
The university is considering ways in which its teaching is led by research. This session will explore three examples of ways in which research-led learning is presently employed within the university, taken from practice within the School of Management, the School of Archaeology, Classics and Egyptology, and elsewhere. The session will begin with a brief introduction to the notion of research-led learning and teaching, and will allow time for discussion amongst participants.
This document provides a review and preparation guide for the GM 533 Final Exam. It summarizes the topics covered in each week of the course, including descriptive statistics, probability, confidence intervals, hypothesis testing, simple and multiple regression. It provides sample questions and worked examples for key concepts like the binomial distribution, hypothesis testing, and confidence intervals. Students are directed to use Excel templates to solve problems involving the normal distribution.
Examples of research methods h ighschool movies - preschool in three cultur...Ray Brannon
This document provides an overview of key concepts in research methods. It discusses what constitutes "good" research, including validity, reliability, and generalizability. It also outlines quantitative and qualitative research approaches, deductive vs inductive reasoning, the importance of establishing causality rather than just correlation, how to define variables, and how to develop hypotheses. Research methods help social scientists systematically study relationships between social factors.
This document outlines the key steps and components of the research process for a study titled "A Study on Pragmatic Approaches and Quality Initiatives for Enhancing Teachers’ Caliber in Post Graduate Institutes offering MBA Programme under Bangalore University". The research methodology section defines different types of research and the scientific research process. It also provides details on key aspects of research design including objectives, hypotheses, sampling, data collection and analysis. The document concludes by mentioning the final steps of report writing and research reporting.
This document provides an introduction to a master's thesis examining the efficiency of Islamic banking in Malaysia from 2000 to 2009. It begins with background information on the growth of Islamic banking globally and in Malaysia. It then states the problem being examined is that while Islamic banking has grown rapidly, analysis of efficiency at the cross-country level is still limited. The objectives are to measure the efficiency of Islamic banks in Malaysia during this period and compare the efficiency of full-fledged Islamic banks to Islamic windows. The methodology to be used is data envelopment analysis to evaluate input and output variables from Islamic banks.
The document provides an overview of the key elements that should be included in a research proposal. It discusses the purpose of a research proposal is to convince others that the proposed research project is worthwhile and that the investigator has the competence and work plan to complete it. The main elements that should be included in a research proposal are an introduction section outlining the background, problem statement, objectives, literature review, methodology, ethical considerations, time schedule and references.
This document provides an overview and study guide for the GM 533 Final Exam. It summarizes the topics covered in each of the 7 weeks of class, including descriptive statistics, probability, confidence intervals, hypothesis testing, simple linear regression, and multiple regression. It includes sample questions and worked examples for key concepts like the binomial distribution, hypothesis testing, and constructing confidence intervals. The document aims to prepare students for the types of questions that may appear on the final exam through providing relevant content summaries, examples, and step-by-step worked solutions.
The document provides a summary and review of topics covered in the GM 533 course, including descriptive statistics, probability, confidence intervals, hypothesis testing, simple and multiple regression. It includes sample questions and worked examples related to binomial distributions, hypothesis testing, confidence intervals, and the normal distribution. Students are provided Minitab output and guided steps to work through problems involving descriptive statistics, hypothesis testing, and probability distributions.
This document contains 5 questions and their answers. Question 1 analyzes survey data to determine if more than 61% of people sleep 7 or more hours per night on weekends. Question 2 calculates a p-value for a hypothesis test comparing the means of two employment tests. Question 3 performs a hypothesis test to examine if a sample's mean score differs from the expected population mean. Question 4 uses a chi-squared test to determine if there is a preference for certain class times. Question 5 provides commute data and asks to calculate the line of best fit, confidence intervals, and determine if distance can indicate travel time.
This document provides 10 multiple choice questions that are checkpoints for weeks 1-4 of a graduate level statistics course (GM533). The questions cover a range of statistical concepts including measures of central tendency, probability, confidence intervals, hypothesis testing, and more. Sample sizes and other relevant information are provided for each question.
This document provides 10 multiple choice questions that are checkpoints for a student completing weeks 1-4 of a graduate level statistics course (GM533). The questions cover a range of statistical concepts including measures of central tendency, probability, confidence intervals, hypothesis testing, and more. Correct answers are provided for each question.
This document provides 10 multiple choice questions that are checkpoints for a student completing weeks 1-4 of a graduate level statistics course (GM533). The questions cover a range of statistical concepts including measures of central tendency, probability, confidence intervals, hypothesis testing, and more. Correct answers are provided for each question.
This document provides a summary of 10 multiple choice questions from weekly checkpoints for the course GM533. The questions cover topics in statistics including calculating relative frequencies, means, medians, standard deviations, probabilities, z-scores, and interpreting normal distributions. Correct answer options are provided for each question. The document appears to be a study guide or self-assessment for a student in the GM533 course to check their understanding of key statistical concepts covered in the first 4 weeks.
An introduction to the Multivariable analysis.pptvigia41
This document introduces multiple linear regression, which allows for more than one independent variable. It provides an example using data from 100 motor inns to predict operating margin based on characteristics like competition, demand generators, and demographics. Key outputs are assessed, including the standard error, coefficient of determination, ANOVA results, and interpretations of individual coefficients. Diagnostics like normality of errors are also discussed.
This document discusses Chi Square and related procedures for analyzing categorical data. It explains that Chi Square can be used for goodness of fit tests to check if a sample follows a particular distribution, and for tests of association to check if two categorical variables are related. It provides examples of how to conduct and interpret Chi Square goodness of fit and association tests using SPSS. Other related procedures discussed include Fisher's Exact Test for small sample sizes and McNamer's Test for analyzing changes in paired categorical data.
Math 533 ( applied managerial statistics ) final exam answersBrittneDean
This document provides answers to the final exam for the course MATH 533 (Applied Managerial Statistics). It includes answers to 8 questions that involve hypothesis testing, confidence intervals, and other statistical analyses. The questions cover topics like the binomial distribution, confidence intervals for proportions and means, hypothesis tests for proportions and means, and linear regression. For each question, the null and alternative hypotheses are stated and the appropriate statistical test is conducted at a given significance level.
Math 533 ( applied managerial statistics ) final exam answersNathanielZaleski
This document provides answers to the final exam for the MATH 533 (Applied Managerial Statistics) course. It includes answers to multiple choice and free response questions covering a range of statistical topics, such as hypothesis testing, confidence intervals, probability, descriptive statistics, and inference for proportions. For one question, the summary calculates probabilities and interprets results from a contingency table on visitor locations and types of parks. Overall, the document offers fully worked out solutions to exam problems involving common statistical analyses.
Math 533 ( applied managerial statistics ) final exam answersDennisHine
This document provides answers to the final exam for the course MATH 533 (Applied Managerial Statistics). It includes answers to 8 questions that involve hypothesis testing, confidence intervals, and other statistical analyses. The questions cover topics like the binomial distribution, confidence intervals for proportions and means, hypothesis tests for proportions and means, and linear regression. For each question, the null and alternative hypotheses are stated and the appropriate statistical tests are conducted and results interpreted.
Math 533 ( applied managerial statistics ) final exam answersPatrickrasacs
This document provides the answers to the final exam for the MATH 533 (Applied Managerial Statistics) course. It includes answers to 8 multiple choice and calculation questions covering topics like hypothesis testing, confidence intervals, and linear regression. The questions analyze data related to job placement times, customer profiles, credit card usage, refueling times, toothpaste recommendations, paint defects, profit percentages, and land prices to determine probabilities, test claims, and estimate population parameters. The document also provides a link to download the full exam answers in PDF format and contact information for the company providing the exam assistance.
Analytics is the process of examining data to draw conclusions and inform decision making. It involves descriptive, predictive, and prescriptive models. Descriptive models analyze past data to understand what has occurred, predictive models use statistical techniques to forecast future outcomes, and prescriptive models advise on potential actions and outcomes. Common techniques in analytics include statistics, machine learning, and visualization of large datasets.
The internship summary document describes Matthew Schomisch's 2015 summer internship at Fabri-Kal where he conducted finite element analysis (FEA) simulations. The internship involved using Solidworks Simulation to model a lid application process and drop testing of plastic containers. Through iterative physical tests and simulations, Matthew was able to determine the average force required to apply a lid and establish failure thresholds for containers of different thicknesses dropped from specific heights. He provided recommendations to further validate and expand the FEA methods for improved product design at Fabri-Kal.
Esitmates for year 201620162015Sales (units) increase.docxYASHU40
Esitmates for year 2016
2016
2015
Sales (units) increase
10%
115,000
Sale Price (unit) increase
1%
$5.00
Raw material:
Price
DM - Plasitic (lb.)
$2.90
$3.00
DM - Wheel (wheel)
$0.03
$0.02
Labor cost:
wage rate (airplane)
$0.60
$88,775
total
MOH:
Indirect material (per airplane)
$0.005
Indirect labor (per airplane)
$0.003
utility
$850
factory depreciation
$1,000
$27,000
total
Period cost:
S&A expenses - variable (per airplane)
$0.01
S&A expenses - Fixed
$15,000
$130,000
total
Finished Goods:
beginning (units)
?
desired ending (units)
9%
of yearly sales
15,000
Account receivable
25%
23%
Account payable
25%
23%
Tax rate
30%
30%
Minimun bank account
$50,000
$50,000
What is the break-even in sales units for 2016?
What is the target sale in sales units for 2016 with a target profit of $200,000?
Assuming at the beginning of 2015, the company made the plan same as 2016. Find the quantity factors and price factors for 2015:
Prepare income statement using both variable costing method and absorption costing method for 2016
Prepare a flexible budget for 2016, with decrease 10% sales, same, and increase 10% sales
Prepare a Master Budget for 2016:
Sales budget
Production budget
DM purchases budget
DL cost budget
MOH cost budget
COGS budget
S&A budget
Cash budget
Account receivable
Account payable
Does the factory need to borrow money at the end of 2016?
MS1023 Business Statistics with Computer Applications Homework #4
Maho Sonmez [email protected] 1
MS1023 Business Statistics w/Comp Apps I
Homework #4 – Use Red Par Score Form
Chps. 9 & 10: 50 Questions Only
1. The first step in testing a hypothesis is to
establish a true null hypothesis and a false
alternative hypothesis.
a) True
b) False
2. In testing hypotheses, the researcher
initially assumes that the alternative
hypothesis is true and uses the sample data
to reject it.
a) True
b) False
3. The null and the alternative hypotheses
must be mutually exclusive and collectively
exhaustive.
a) True
b) False
4. Generally speaking, the hypotheses that
business researchers want to prove are stated
in the alternative hypothesis.
a) True
b) False
5. When a true null hypothesis is rejected,
the researcher has made a Type I error.
a) True
b) False
6. When a false null hypothesis is rejected,
the researcher has made a Type II error.
a) True
b) False
7. The rejection region for a hypothesis test
becomes smaller if the level of significance
is changed from 0.01 to 0.05.
a) True
b) False
8. Whenever hypotheses are established
such that the alternative hypothesis is "μ>8",
where μ is the population mean, the
hypothesis test would be a two-tailed test.
a) True
b) False
9. Whene ...
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1. (2 points)Two random samples are selected from two indepe.docxSONU61709
1. (2 points)
Two random samples are selected from two independent pop-
ulations. A summary of the samples sizes, sample means, and
sample standard deviations is given below:
n1 = 37, x̄1 = 52.4, s1 = 5.8
n2 = 48, x̄2 = 75, s2 = 10
Find a 92.5% confidence interval for the difference µ1− µ2
of the means, assuming equal population variances.
Confidence Interval =
Answer(s) submitted:
•
(incorrect)
2. (2 points) In order to compare the means of two popu-
lations, independent random samples of 238 observations are
selected from each population, with the following results:
Sample 1 Sample 2
x1 = 1 x2 = 3
s1 = 120 s2 = 200
(a) Use a 97 % confidence interval to estimate the difference
between the population means (µ1−µ2).
≤ (µ1−µ2)≤
(b) Test the null hypothesis: H0 : (µ1− µ2) = 0 versus the al-
ternative hypothesis: Ha : (µ1− µ2) 6= 0. Using α = 0.03, give
the following:
(i) the test statistic z =
(ii) the positive critical z score
(iii) the negative critical z score
The final conclustion is
• A. We can reject the null hypothesis that (µ1−µ2) = 0
and accept that (µ1−µ2) 6= 0.
• B. There is not sufficient evidence to reject the null hy-
pothesis that (µ1−µ2) = 0.
(c) Test the null hypothesis: H0 : (µ1−µ2) = 26 versus the al-
ternative hypothesis: Ha : (µ1−µ2) 6= 26. Using α = 0.03, give
the following:
(i) the test statistic z =
(ii) the positive critical z score
(iii) the negative critical z score
The final conclustion is
• A. We can reject the null hypothesis that (µ1−µ2) = 26
and accept that (µ1−µ2) 6= 26.
• B. There is not sufficient evidence to reject the null hy-
pothesis that (µ1−µ2) = 26.
Answer(s) submitted:
•
•
•
•
•
•
•
•
•
•
(incorrect)
3. (2 points) Two independent samples have been selected,
70 observations from population 1 and 83 observations from
population 2. The sample means have been calculated to be
x1 = 14.9 and x2 = 10.5. From previous experience with these
populations, it is known that the variances are σ21 = 20 and
σ22 = 21.
(a) Find σ(x1−x2).
answer:
(b) Determine the rejection region for the test of H0 :
(µ1−µ2) = 2.92 and Ha : (µ1−µ2)> 2.92 Use α = 0.05.
z >
(c) Compute the test statistic.
z =
The final conclustion is
• A. We can reject the null hypothesis that (µ1− µ2) =
2.92 and accept that (µ1−µ2)> 2.92.
• B. There is not sufficient evidence to reject the null hy-
pothesis that (µ1−µ2) = 2.92.
(d) Construct a 95 % confidence interval for (µ1−µ2).
≤ (µ1−µ2)≤
Answer(s) submitted:
•
•
•
•
•
•
(incorrect)
4. (2 points) Randomly selected 100 student cars have ages
with a mean of 7.2 years and a standard deviation of 3.4 years,
while randomly selected 85 faculty cars have ages with a mean
of 5.4 years and a standard deviation of 3.3 years.
1
1. Use a 0.01 significance level to test the claim that student
cars are older than faculty cars.
The test statistic is
The critical value is
Is there sufficient evidence to support the claim that student
cars are older than faculty cars?
• A. Yes
• ...
The document provides examples and step-by-step instructions for conducting linear regression analyses in Minitab. It discusses how to find confidence intervals for slopes, interpret regression results, make predictions based on regression equations, and conduct hypothesis tests regarding the significance of regression slopes. For example 4, the null hypothesis is that the slope β1 equals 0, indicating crossword puzzle success and jelly beans consumed are not linearly related, while the alternative is that β1 does not equal 0, meaning they are linearly related. The t-statistic is 1.93490422105 and the p-value is 0.075, so the null cannot be rejected at the 0.10 significance level.
This document provides examples for 3 homework problems from a statistics class. Problem 18 demonstrates how to calculate the range, mean, variance, and standard deviation of a data set using Minitab software. Problem 22 shows how to identify the minimum, maximum, quartiles, and interquartile range from a box and whisker plot. Problem 24 matches z-scores to a histogram by considering their relationship to the mean and standard deviation.
This document provides instructions for finding recorded lectures in iConnect Live for Math 221 and Math 533 courses. It explains that the user should click the Week 1 button, then the iConnect Live link, be patient as the recording loads, select the proper course and click show, then launch the desired lecture. It notes that the lecture slides may not be visible initially but will load during the lecture. It also provides instructions for downloading and navigating the lecture recording.
This presentation provides help on numbers 13, 15 and 19 on the Week 7 Homework. This contains hypothesis testing examples for 1 Sample z, 1 Sample t and 1 proportion.
This document provides step-by-step instructions for completing homework problems related to hypothesis testing using z-tests. It includes instructions for finding critical values, performing left-tailed, right-tailed, and two-tailed z-tests using Minitab software. Examples are provided for problems testing claims about population means, finding test statistics, determining p-values, and interpreting results to either reject or fail to reject the null hypothesis. Guidance is given to carefully consider the wording of claims and hypotheses and set up tests accordingly.
Help on funky proportion confidence interval questionsBrent Heard
This presentation provides an alternate way of getting confidence intervals for proportions. We have at least one problem in Week 6 where this applies. Rather than using Minitab, I have an Excel template that will help. Instructions on obtaining the file are at the end of the presentation.
Using minitab instead of tables for z values probabilities etcBrent Heard
This document discusses using Minitab instead of tables to find probabilities and z-values for the standard normal distribution. It provides examples of finding probabilities for given z-values using both tables and Minitab, and shows that Minitab makes the calculations faster and easier. The document also demonstrates how to use Minitab to find z-values for given probabilities, as well as find the z-values that define a symmetric probability between them. Overall, the document promotes using Minitab over tables for standard normal distribution calculations.
This presentation describes choosing the right options in Minitab for distributions related to the "tail" of the distribution. I cover Binomial, Poisson and the Geometric Distributions.
Help on binomial problems using minitabBrent Heard
The document provides help on solving binomial probability problems using Minitab software. It explains how to calculate the probabilities of exactly 8 successes, at least 8 successes, and less than 8 successes when randomly sampling 10 men and the probability of any one man being a basketball fan is 49%. The key steps are to use Minitab's binomial distribution function, enter the number of trials (10), probability of success (0.49), and use the shaded area tab to calculate the probabilities by selecting the left, right, or middle tail as appropriate. The probabilities calculated are 0.03890 for exactly 8, 0.04800 for at least 8, and 0.9520 for less than 8.
This document provides a summary of key concepts and example problems to help students prepare for their undergraduate statistics final exam. It covers topics like levels of measurement, types of sampling, descriptive statistics, populations and samples, qualitative vs. quantitative data, pivot tables, normal distributions, Poisson distributions, and confidence intervals. The examples are worked out step-by-step to demonstrate the calculations and show the reasoning behind each answer. The goal is to help refresh students' memories on what they learned and to feel more prepared for their upcoming final.
This document provides examples and solutions for statistics homework problems using binomial, geometric, and Poisson distributions in Minitab software. It addresses three homework problems on finding probabilities for the number of households reporting they feel secure, the number of sales calls required, and the number of hurricanes hitting an island. Step-by-step instructions are given for setting up each problem in Minitab and calculating the requested probabilities. None of the probabilities calculated are described as unusual.
This document contains step-by-step instructions from a statistics professor on solving various probability and counting problems that commonly appear on homework assignments. The professor demonstrates how to calculate combinations, probabilities, means, variances, and standard deviations using both calculators and Excel. Examples include finding the number of combinations of letters in words, the probability of certain race outcomes, and describing the properties of probability distributions.
This document provides examples for additional homework problems in a statistics course. It discusses problems similar to numbers 11, 13, and 14 from the homework. For problem 11, it explains how to match a regression equation to the correct graph by examining the slope and y-intercept. For problem 13, it demonstrates how to calculate the coefficient of determination from the linear correlation coefficient. Finally, for problem 14 it works through an example of using a multiple regression equation to predict GPA based on given high school GPA and college board scores.
Week 1 Statistics for Decision (3x9 on Wednesday)Brent Heard
This document provides examples and explanations for 3 extra homework problems from a statistics class.
(1) The first problem asks students to find the range, mean, variance and standard deviation for a sample data set using Minitab software. Step-by-step instructions are given.
(2) The second problem involves interpreting parts of a box-and-whisker plot like minimum, maximum, and quartiles.
(3) The third problem has students match z-scores to points on a histogram by considering where values above, below, or at the mean would fall. An unusual z-score is identified as well.
Math 533 week7_homework_feb_2013_num_6_g_hBrent Heard
This homework document provides instructions for parts g and h of number 6 from Math 533 week 7 homework. It reminds the reader that previous parts of the question removed variables x1 and x3, leaving only x2. It then instructs the reader to use the Regression tool in Stat, modeling y as the response variable, x2 as the predictor, and inputting 3 in the "Prediction intervals for new observations" box to get a prediction interval with 95% confidence.
Week 6 homework help feb 11 2013_number_10pptxBrent Heard
This document provides examples for week 6 homework in Math 533. It repeats the phrase "Week 6 Homework Examples" multiple times, suggesting it contains sample problems or explanations for the week 6 assignments in a graduate level math course numbered 533 related to lecture material from that week.
Week 6 homework help feb 11 2013_numbers_7_8Brent Heard
This document provides examples for week 6 homework in a math 533 course. It contains repetitive text on "Week 6 Homework Examples" that seems to be placeholder or template text for homework problems or questions that were not fully populated. The document focuses on providing examples for the sixth week of homework assignments but does not include the specific questions or problems.
Week 6 homework help feb 11 2013_number_5Brent Heard
This document contains 6 repetitions of the phrase "Week 6 Homework Examples" with no other context or details provided. It appears to be listing or labeling something related to week 6 homework examples, but no other information is given to summarize.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
3. Math 533 Final Exam Prep
• Sample Question on Hypothesis Testing
– Pepito’s Pizza Work is putting pizzas out by delivery as fast as they
can. Pepito’s claims they can deliver pizzas within their delivery area
in less than 29 minutes. You are given the following data from a
sample.
Sample size: 120 Deliveries
Population standard deviation: 1.4
Sample mean: 28.3
Formulate a hypothesis test to evaluate the claim.
4. Math 533 Final Exam Prep
• Sample Question on Hypothesis Testing
– Pepito’s Pizza Work is putting pizzas out by delivery as fast as they
can. Pepito’s claims they can deliver pizzas within their delivery area
in less than 29 minutes. You are given the following data from a
sample.
Sample size: 120 Deliveries
Population standard deviation: 1.4
Sample mean: 28.3
Formulate a hypothesis test to evaluate the claim.
– Answer: Ho: µ ≥ 29, Ha : µ < 29
– (In this case, the claim was Ha)
– Remember Ho always contains equality (It will either be =, ≤ or ≥)
– Ha will be either ≠, < or >
5. Math 533 Final Exam Prep
• Sample Question on Binomial Distribution
– Assume that a study was done finding that 70 percent of males in Georgia are
football fans. If a researcher asks 8 Georgia Males if they are fans, the
following binomial distribution would be applicable. What is the probability
that at least 5 will be football fans?
n p
8 0.7
x P( x) Cumulative
0 0.0001 0.0001
1 0.0012 0.0013
2 0.0100 0.0113
3 0.0467 0.0580
4 0.1361 0.1941
5 0.2541 0.4482
6 0.2965 0.7447
7 0.1977 0.9424
8 0.0576 1.0000
6. Math 533 Final Exam Prep
• Sample Question on Binomial Distribution
– Assume that a study was done finding that 70 percent of males in Georgia are
football fans. If a researcher asks 8 Georgia Males if they are fans, the
following binomial distribution would be applicable. What is the probability
that at least 5 will be football fans?
n p “At least 5” is the probability that 5, 6,
8 0.7
7 or 8 will be fans. Simply add those
x P( x) Cumulative probabilities.
0 0.0001 0.0001
1 0.0012 0.0013 0.2541
2 0.0100 0.0113 0.2965
3 0.0467 0.0580 0.1977
4 0.1361 0.1941 0.0576 My total is 0.8059
5 0.2541 0.4482 or about 81% which
6 0.2965 0.7447 is the probability of
7 0.1977 0.9424 0.8059
at least 5 being
8 0.0576 1.0000
football fans.
7. Math 533 Final Exam Prep
• Analysis Example
– 9 members of the local college baseball team had the following number for extra base hits for
the year. Using the Minitab output given, determine:
A. Mean
B. Standard Deviation
C. Range
D. Median
E. The range of the data that would contain 68% of the results .
Raw Data
7
9
4
24
15
17
15
6
29
– Minitab Follows
8. Math 533 Final Exam Prep
Descriptive Statistics: Extra Base Hits
Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3
Extra Base Hits 9 0 14.00 2.82 8.47 4.00 6.50 15.00 20.50
Variable Maximum
Extra Base Hits 29.00
Stem-and-Leaf Display: Extra Base Hits
Stem-and-leaf of Extra Base Hits N = 9
Leaf Unit = 1.0
The range of the data that would contain 68%
4 0 4679 of the results. (Mean – Std Dev, Mean +
(3) 1 557 Std Dev) which is (14 – 8.47, 14 + 8.47) or
2 2 49
(5.53,22.47)
9. Math 533 Final Exam Prep
• Confidence interval Example
– Acme computers needs to find a new vendor for
their hard drives. They are considering using
Howie’s Hard Drives as a vendor. Acme’s
requirement is that 95% of the hard drives last
24000 hours ± 2000 hours. The following data is
from an independent source who evaluated
Howie’s. Should Acme buy from Howie’s? Explain
your answer. (Follows on next page)
10. Math 533 Final Exam Prep
• Mean = 24500
• Sample Standard Deviation 2250
• Min 21402
• Max 29463
• Margin of Error 4500
• Answer Follows
11. Math 533 Final Exam Prep
• No, Acme shouldn’t buy from Howie’s looking
at their requirements (24000 – 2000, 24000 +
2000) which is (22000, 26,000). Based on the
results given, Howie’s would yield a tolerance
of (24500 – 2*2250, 24500+2*2250) which is
(20000, 29000). This does not meet Acme’s
requirement. You could also see this by
looking at the margin of error.
12. Math 533 Final Exam Prep
• Example on Pivot/Contingency Tables
• The table below gives the number of cars of
various colors and the state tag on the car for
a parking lot in a mall close to DC.
VA MD DC Other State Total
Blue 4 8 9 3 24
Black 9 7 11 8 35
White 12 14 21 15 62
Other 37 10 29 35 111
Total 62 39 70 61 232
13. Math 533 Final Exam Prep
• Based on the table, find the probability that a
car is from VA or MD.
• Based on the table, given that a car is from
DC, find the probability it is black.
14. Math 533 Final Exam Prep
VA MD DC Other State Total find the probability that a car is
Blue 4 8 9 3 24 from VA or MD. Add 62 + 39 to get
Black 9 7 11 8 35 So the answer would be 91/232 or it’s
White 12 14 21 15 62 decimal form.
Other 37 10 29 35 111
Total 62 39 70 61 232
62 + 39 = 91
15. Math 533 Final Exam Prep
VA MD DC Other State Total
Given that a car is from DC, find
Blue 4 8 9 3 24
the probability it is black.
Black 9 7 11 8 35
Given that it is from DC means we are only
White 12 14 21 15 62
dealing with the 70 cars from DC.
Other 37 10 29 35 111 There are 11 of those that are black, so the
Total 62 39 70 61 232 probability is 11/70
16. Math 533 Final Exam Prep
• Normal Distribution Example
– The number of students who use the dining hall at
an urban college on a given day is normally
distributed with a mean of 1578 students and a
standard deviation of 274 students.
– Use Minitab for these and shouldn’t have any
issues.
17. Math 533 Final Exam Prep
• Another Confidence Interval Example
– I randomly sampled 18 engineers where I work and asked
them how many projects they have worked on in the last
five years. The sample mean was 21, with a standard
deviation of 5. What is the mean number of projects of all
engineers at my research center? Why? What is the 95%
confidence interval for the population mean? You are
given the information below from Minitab.
One-Sample T
N Mean StDev SE Mean 95% CI
18 21.00 5.00 1.18 (18.51, 23.49)
18. Math 533 Final Exam Prep
• Another Confidence Interval Example
– I randomly sampled 18 engineers where I work and asked them
how many projects they have worked on in the last five years.
The sample mean was 21, with a standard deviation of 5. What
is the mean number of projects of all engineers at my research
center? Why? What is the 95% confidence interval for the
population mean? You are given the information below from
Minitab.
Answer:
21 projects would be the best estimate for the mean. I
would expect 95% of the population mean to fall between
18.51 and 23.49 projects. The t is used because of the
sample size.
19. Math 533 Final Exam Prep
• Regression Example
– I did an analysis to determine if the number of
hours studied for a final exam related to the Final
Exam grade for students. On the sheets that
follow you will see what my Minitab results were.
20. General Regression Analysis: Final Grade versus Hours of Analysis of Variance
Study
Source DF Seq SS Adj SS Adj MS F P
Regression Equation Regression 1 8090.71 8090.71 8090.71 171.274
0.000000
Final Grade = 34.2845 + 1.45508 Hours of Study Hours of Study 1 8090.71 8090.71 8090.71 171.274
0.000000
Error 22 1039.25 1039.25 47.24
Coefficients Lack-of-Fit 18 936.58 936.58 52.03 2.027 0.259215
Pure Error 4 102.67 102.67 25.67
Term Coef SE Coef T P Total 23 9129.96
Constant 34.2845 3.38091 10.1406 0.000
Hours of Study 1.4551 0.11118 13.0872 0.000
Fits and Diagnostics for Unusual Observations
Summary of Model Final
Obs Grade Fit SE Fit Residual St Resid
S = 6.87303 R-Sq = 88.62% R-Sq(adj) = 88.10% 24 19 37.1947 3.17993 -18.1947 -2.98609 R
PRESS = 1426.21 R-Sq(pred) = 84.38%
R denotes an observation with a large standardized residual.
21. Math 533 Final Exam Prep
Predicted Values for New Observations
New Obs Fit SE Fit 95% CI 95% PI
1 48.8353 2.41382 (43.8294, 53.8413) (33.7280, 63.9426)
Values of Predictors for New Observations
Hours
of
New Obs Study
1 10
22. Math 533 Final Exam Prep
I did an analysis to determine if the number of hours studied for a final exam related to the
Final Exam grade for students. On the sheets that follow you will see what my Minitab
results were.
Answer the following questions.
Determine the regression equation.
What conclusions are possible using the meaning of bo (intercept) and b1 (regression coefficient) in this problem?
What does the coefficient of determination (r-squared) mean?
Calculate the coefficient of correlation and explain what it means.
Does this data provide significant evidence (a=0.05) that the final exam grade is associated with the hours
studied? Find the p-value and interpret.
Determine the predicted grade for someone who spends 10 hours studying for the final exam.
What is the 95% confidence interval for the score for spending 10 hours studying on the test? What conclusion is
possible using this interval?
23. Math 533 Final Exam Prep
I did an analysis to determine if the number of hours studied for a
final exam related to the Final Exam grade for students. On the
sheets that follow you will see what my Minitab results were.
Answer the following questions.
Determine the regression equation. y= 34.2845 + 1.45508x
What conclusions are possible using the meaning of b o (intercept) and b1
(regression coefficient) in this problem? For each hour of study the final grade
is increased by about 1.5 points (1.45508). bo represents the y intercept or
34.2845 in our case. It is the score that a student could expect to get without
studying.
What does the coefficient of determination (r-squared) mean? The .886 means
that 88.6 percent of the variability of the final grade can be explained by the
number of study hours. The other 11.4% would be due to something else or
be unexplained.
24. Math 533 Final Exam Prep
Calculate the coefficient of correlation and explain what it means. Square Root of
(0.886) is 0.942 which is r, the correlation coefficient. With a value this close to
one, we could say there is strong positive correlation.
Does this data provide significant evidence (a=0.05) that the final exam grade is
associated with the hours studied? Find the p-value and interpret. Yes, the p value
was 0. If it were above 0.05, I would have said “no.”
Determine the predicted grade for someone who spends 10 hours studying for
the final exam. 48.8353
What is the 95% confidence interval for the score for spending 10 hours studying
on the test? What conclusion is possible using this interval? (43.8294, 53.8413)
We would be 95% confident that if someone studied for 10 hours they would
score on average between those two values.