Hypothesis testing is an essential procedure in statistics. A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. When we say that a finding is statistically significant, it’s thanks to a hypothesis test. How do these tests really work and what does statistical significance actually mean?
Solution to the practice test ch 8 hypothesis testing ch 9 two populationsLong Beach City College
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Elementary Statistics Practice Test 4
Module 4:
Chapter 8, Hypothesis Testing
Chapter 9: Two Populations
Chapter 8 Confidence Interval Estimation
Estimation Process
Point Estimates
Interval Estimates
Confidence Interval Estimation for the Mean ( Known )
Confidence Interval Estimation for the Mean ( Unknown )
Confidence Interval Estimation for the Proportion
The document discusses (Q,R) inventory systems which generalize the economic order quantity (EOQ) model to allow for stochastic demand and a reorder point R. Key aspects include:
1) The system uses continuous review with fixed lead times, stochastic demand, ordering costs, holding costs, and shortage costs.
2) Decision variables are the order quantity Q and reorder point R. The total cost considers holding, ordering, and shortage costs.
3) Service level can be measured in two ways - type 1 is the probability of no shortage in the lead time, type 2 is the proportion of demands met from stock.
4) The optimal solution balances minimizing total costs while meeting a specified service level
1. The students conducted a hypothesis test to determine if the average cost of textbooks reported by the college bookstore was accurate. Based on a sample of 100 students with a mean cost of $52.80, the students failed to reject the null hypothesis that the average cost is $52.
2. Environmentalists tested the claim of factories that they lowered the average pollutant levels in a river. Based on a sample of 50 with a mean of 32.5 ppm, the environmentalists failed to reject the null hypothesis that the average is 34 ppm.
3. A dental association tested if the estimated average family dental expenses of $1135 was accurate for their region. Based on a sample of 22 families with
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.
inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier,
This document provides an overview of sampling theory and statistical analysis. It discusses different sampling methods, important sampling terms, and statistical tests. The key points are:
1) There are two ways to collect statistical data - a complete enumeration (census) or a sample survey. A sample is a portion of a population that is examined to estimate population characteristics.
2) Common sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, quota sampling, and purposive sampling.
3) Important terms include parameters, statistics, sampling distributions, and statistical inferences about populations based on sample data.
4) Statistical tests covered include hypothesis testing, types of errors, test statistics, critical values,
This document provides an overview of probability distributions and related concepts. It defines key probability distributions like the binomial, beta, multinomial, and Dirichlet distributions. It also describes probability distribution equations like the cumulative distribution function and probability density function. Additionally, it outlines descriptive parameters for distributions like mean, variance, skewness and kurtosis. Finally, it briefly discusses probability theorems such as the law of large numbers, central limit theorem, and Bayes' theorem.
Solution to the practice test ch 8 hypothesis testing ch 9 two populationsLong Beach City College
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Elementary Statistics Practice Test 4
Module 4:
Chapter 8, Hypothesis Testing
Chapter 9: Two Populations
Chapter 8 Confidence Interval Estimation
Estimation Process
Point Estimates
Interval Estimates
Confidence Interval Estimation for the Mean ( Known )
Confidence Interval Estimation for the Mean ( Unknown )
Confidence Interval Estimation for the Proportion
The document discusses (Q,R) inventory systems which generalize the economic order quantity (EOQ) model to allow for stochastic demand and a reorder point R. Key aspects include:
1) The system uses continuous review with fixed lead times, stochastic demand, ordering costs, holding costs, and shortage costs.
2) Decision variables are the order quantity Q and reorder point R. The total cost considers holding, ordering, and shortage costs.
3) Service level can be measured in two ways - type 1 is the probability of no shortage in the lead time, type 2 is the proportion of demands met from stock.
4) The optimal solution balances minimizing total costs while meeting a specified service level
1. The students conducted a hypothesis test to determine if the average cost of textbooks reported by the college bookstore was accurate. Based on a sample of 100 students with a mean cost of $52.80, the students failed to reject the null hypothesis that the average cost is $52.
2. Environmentalists tested the claim of factories that they lowered the average pollutant levels in a river. Based on a sample of 50 with a mean of 32.5 ppm, the environmentalists failed to reject the null hypothesis that the average is 34 ppm.
3. A dental association tested if the estimated average family dental expenses of $1135 was accurate for their region. Based on a sample of 22 families with
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.
inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier,
This document provides an overview of sampling theory and statistical analysis. It discusses different sampling methods, important sampling terms, and statistical tests. The key points are:
1) There are two ways to collect statistical data - a complete enumeration (census) or a sample survey. A sample is a portion of a population that is examined to estimate population characteristics.
2) Common sampling methods include simple random sampling, systematic sampling, stratified sampling, cluster sampling, quota sampling, and purposive sampling.
3) Important terms include parameters, statistics, sampling distributions, and statistical inferences about populations based on sample data.
4) Statistical tests covered include hypothesis testing, types of errors, test statistics, critical values,
This document provides an overview of probability distributions and related concepts. It defines key probability distributions like the binomial, beta, multinomial, and Dirichlet distributions. It also describes probability distribution equations like the cumulative distribution function and probability density function. Additionally, it outlines descriptive parameters for distributions like mean, variance, skewness and kurtosis. Finally, it briefly discusses probability theorems such as the law of large numbers, central limit theorem, and Bayes' theorem.
This document discusses statistical concepts such as parameters, statistics, descriptive statistics, estimation, and hypothesis testing. It provides examples of:
- Point estimates and interval estimates used to estimate population parameters from sample statistics. Point estimates provide a single value while interval estimates provide a range of values.
- Confidence intervals which specify a range of values that is expected to contain the population parameter a certain percentage of times, known as the confidence level. Common confidence levels are 90%, 95%, and 99%.
- Formulas for constructing confidence intervals for the population mean, proportion, and variance based on the sample statistic, sample size, confidence level, and whether the population standard deviation is known.
Solution to the practice test ch 10 correlation reg ch 11 gof ch12 anovaLong Beach City College
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Elementary Statistics Practice Test 5
Module 5
Chapter 10: Correlation and Regression
Chapter 11: Goodness of Fit and Contingency Tables
Chapter 12: Analysis of Variance
Research application in business decisionAbhinav Kp
Research is vital to the marketing function and is conducted on a wide variety of topics, both internally and externally. Market-based accounting research analyzes corporate financial reporting, valuations, and how investors use accounting information. Auditing and accountability research examines both private and public sector auditing as well as regulations and methodologies. Production and operations research has significant cost and process implications when implemented and is highly focused on specific problems. Key areas of research in decision making include operations planning, demand forecasting, process planning, project management, logistics, and quality assurance.
This document provides 10 teaching suggestions for instructors to help students better understand key concepts in decision analysis. Suggestions include having students describe personal decisions they made and which steps of the decision-making process they used; role playing to define problems and alternatives; discussing types of decisions under certainty, risk, and uncertainty; and using decision trees and Bayesian analysis to solve problems. The goal is for students to recognize how decision theory applies to important real-life decisions. Alternative examples provided apply concepts like expected monetary value to problems involving purchasing industrial robots.
The document discusses different measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure using various examples. The mean is the average value, the median is the middle value when data is arranged in order, and the mode is the value that occurs most frequently in a data set. Formulas are given for calculating the measures using both grouped and ungrouped data.
This document provides an overview of statistical inference. It discusses descriptive statistics, which summarize data, and inferential statistics, which are used to generalize from samples to populations. Key concepts covered include estimation, hypothesis testing, parameters, statistics, confidence intervals, significance levels, types of errors. Examples are given of how to calculate confidence intervals for means and proportions and how to perform hypothesis tests using z-tests and t-tests. Steps for conducting hypothesis tests are outlined.
Estimating standard error of measurementCarlo Magno
The document discusses sources of error in measurement including random error and systematic error. Random error affects individual scores but not the overall mean, while systematic error consistently biases scores in one direction. The document provides an example of how to calculate the confidence interval for a population mean using a sample's mean, standard deviation, sample size, and desired confidence level (e.g. 95%). Ways to reduce standard error include pilot testing instruments, thorough examiner training, and double checking data entry.
This document discusses transportation models and methods for finding an initial basic feasible solution and testing for optimality in transportation problems. It describes three methods - northwest corner, least cost, and Vogel's approximation - for obtaining an initial solution. It then explains how to test if the initial solution is optimal using the MODI or u-v method by calculating opportunity costs for unoccupied cells and finding a closed path if any cells have negative opportunity costs to obtain an improved solution. The process repeats until all opportunity costs are non-negative, indicating an optimal solution.
1) The document provides information about statistics homework help and tutoring services offered by Homework Guru. It discusses various types of statistics help available, including online tutoring, homework help, and exam preparation.
2) Key aspects of their tutoring services are highlighted, including the qualifications of tutors, availability, and interactive online classrooms. Confidence intervals and how to calculate them are also explained in detail.
3) Examples are given to demonstrate how to calculate 95% and 99% confidence intervals for a population mean when the population standard deviation is known or unknown. Interval estimation procedures and when to use z-tests or t-tests are summarized.
The document discusses transportation problems and their solutions. It defines transportation problems as dealing with assigning origins to destinations to maximize effectiveness. It outlines the history of transportation models and some common applications. It then describes the standard process of formulating a transportation problem and several algorithms for solving transportation problems, including the North West Corner Rule, Row Minima Method, Column Minima Method, Least Cost Method, and Vogel's Approximation Method.
This document discusses different types of t-tests, including one sample t-tests and independent sample t-tests. A one sample t-test compares the mean of a sample to a known population mean. An independent sample t-test compares the means of two independent samples to determine if they are significantly different. Both tests assume the data is continuous and normally distributed. Examples are provided of when each type of t-test would be used.
The document introduces econometrics and its methodology. Econometrics is defined as the quantitative analysis of economic phenomena based on concurrent development of economic theory and observation. It differs from economic theory, mathematics economics, and economic statistics by empirically testing economic theories. The methodology of econometrics involves: (1) stating an economic theory or hypothesis, (2) specifying its mathematical model, (3) specifying the econometric model, (4) obtaining data, (5) estimating the model, (6) testing hypotheses, (7) forecasting, and (8) using the model for policy purposes.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
The document provides additional information on correlation analysis. It discusses various examples of correlation between variables like sugar consumption and activity level. It explains the characteristics of a relationship such as the direction, form, and degree of correlation. Correlations can be used for prediction, validity, and reliability. The document also discusses the difference between correlation and causation. It then provides examples to test the reader's understanding of correlation through multiple choice questions. Finally, it covers topics like probable error, coefficient of correlation, coefficient of determination, Spearman's rank correlation method, and concurrent deviation method for calculating correlation.
This document provides an overview of correlation coefficients and how to interpret them. It discusses the difference between correlation strength and significance. The key points covered are:
ONE: Correlation coefficients measure the strength and direction of association between two variables but do not imply causation. Strength is evaluated on a scale from -1 to 1 while significance is determined by comparing the p-value to the significance level alpha.
TWO: There are two parts to interpreting a correlation - the coefficient indicates strength (weak, moderate, strong) while the p-value determines if the correlation is statistically significant or could be due to chance.
THREE: Examples are provided to demonstrate how to interpret correlation output and determine the most strongly correlated variables
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
This presentation is made to represent the basic transportation model. The aim of this presentation is to implement the transportation model in solving transportation problem.
This document provides solutions to practice problems for hypothesis testing. It tests claims about population parameters such as drug failure rates and textbook prices against sample data. For each problem it states the null and alternative hypotheses, calculates the test statistic, finds the critical value, and makes a decision to reject or fail to reject the null hypothesis. It defines type I and type II errors and explains how lower variation in test scores does not necessarily indicate students are performing better.
The document contains 17 multiple choice questions related to hypothesis testing concepts such as null and alternative hypotheses, Type I and Type II errors, critical values, significance levels, t-tests, z-tests, chi-square tests, and degrees of freedom. The questions cover topics like identifying the hypothesis being tested, defining Type I errors, identifying critical values and significance levels, specifying alternative hypotheses, determining acceptance regions, and calculating degrees of freedom for different statistical tests. The document also provides the answers to each question.
This document discusses statistical concepts such as parameters, statistics, descriptive statistics, estimation, and hypothesis testing. It provides examples of:
- Point estimates and interval estimates used to estimate population parameters from sample statistics. Point estimates provide a single value while interval estimates provide a range of values.
- Confidence intervals which specify a range of values that is expected to contain the population parameter a certain percentage of times, known as the confidence level. Common confidence levels are 90%, 95%, and 99%.
- Formulas for constructing confidence intervals for the population mean, proportion, and variance based on the sample statistic, sample size, confidence level, and whether the population standard deviation is known.
Solution to the practice test ch 10 correlation reg ch 11 gof ch12 anovaLong Beach City College
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Elementary Statistics Practice Test 5
Module 5
Chapter 10: Correlation and Regression
Chapter 11: Goodness of Fit and Contingency Tables
Chapter 12: Analysis of Variance
Research application in business decisionAbhinav Kp
Research is vital to the marketing function and is conducted on a wide variety of topics, both internally and externally. Market-based accounting research analyzes corporate financial reporting, valuations, and how investors use accounting information. Auditing and accountability research examines both private and public sector auditing as well as regulations and methodologies. Production and operations research has significant cost and process implications when implemented and is highly focused on specific problems. Key areas of research in decision making include operations planning, demand forecasting, process planning, project management, logistics, and quality assurance.
This document provides 10 teaching suggestions for instructors to help students better understand key concepts in decision analysis. Suggestions include having students describe personal decisions they made and which steps of the decision-making process they used; role playing to define problems and alternatives; discussing types of decisions under certainty, risk, and uncertainty; and using decision trees and Bayesian analysis to solve problems. The goal is for students to recognize how decision theory applies to important real-life decisions. Alternative examples provided apply concepts like expected monetary value to problems involving purchasing industrial robots.
The document discusses different measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure using various examples. The mean is the average value, the median is the middle value when data is arranged in order, and the mode is the value that occurs most frequently in a data set. Formulas are given for calculating the measures using both grouped and ungrouped data.
This document provides an overview of statistical inference. It discusses descriptive statistics, which summarize data, and inferential statistics, which are used to generalize from samples to populations. Key concepts covered include estimation, hypothesis testing, parameters, statistics, confidence intervals, significance levels, types of errors. Examples are given of how to calculate confidence intervals for means and proportions and how to perform hypothesis tests using z-tests and t-tests. Steps for conducting hypothesis tests are outlined.
Estimating standard error of measurementCarlo Magno
The document discusses sources of error in measurement including random error and systematic error. Random error affects individual scores but not the overall mean, while systematic error consistently biases scores in one direction. The document provides an example of how to calculate the confidence interval for a population mean using a sample's mean, standard deviation, sample size, and desired confidence level (e.g. 95%). Ways to reduce standard error include pilot testing instruments, thorough examiner training, and double checking data entry.
This document discusses transportation models and methods for finding an initial basic feasible solution and testing for optimality in transportation problems. It describes three methods - northwest corner, least cost, and Vogel's approximation - for obtaining an initial solution. It then explains how to test if the initial solution is optimal using the MODI or u-v method by calculating opportunity costs for unoccupied cells and finding a closed path if any cells have negative opportunity costs to obtain an improved solution. The process repeats until all opportunity costs are non-negative, indicating an optimal solution.
1) The document provides information about statistics homework help and tutoring services offered by Homework Guru. It discusses various types of statistics help available, including online tutoring, homework help, and exam preparation.
2) Key aspects of their tutoring services are highlighted, including the qualifications of tutors, availability, and interactive online classrooms. Confidence intervals and how to calculate them are also explained in detail.
3) Examples are given to demonstrate how to calculate 95% and 99% confidence intervals for a population mean when the population standard deviation is known or unknown. Interval estimation procedures and when to use z-tests or t-tests are summarized.
The document discusses transportation problems and their solutions. It defines transportation problems as dealing with assigning origins to destinations to maximize effectiveness. It outlines the history of transportation models and some common applications. It then describes the standard process of formulating a transportation problem and several algorithms for solving transportation problems, including the North West Corner Rule, Row Minima Method, Column Minima Method, Least Cost Method, and Vogel's Approximation Method.
This document discusses different types of t-tests, including one sample t-tests and independent sample t-tests. A one sample t-test compares the mean of a sample to a known population mean. An independent sample t-test compares the means of two independent samples to determine if they are significantly different. Both tests assume the data is continuous and normally distributed. Examples are provided of when each type of t-test would be used.
The document introduces econometrics and its methodology. Econometrics is defined as the quantitative analysis of economic phenomena based on concurrent development of economic theory and observation. It differs from economic theory, mathematics economics, and economic statistics by empirically testing economic theories. The methodology of econometrics involves: (1) stating an economic theory or hypothesis, (2) specifying its mathematical model, (3) specifying the econometric model, (4) obtaining data, (5) estimating the model, (6) testing hypotheses, (7) forecasting, and (8) using the model for policy purposes.
Chap19 time series-analysis_and_forecastingVishal Kukreja
Trend + Seasonality + Cyclical + Irregular
Multiplicative Model
X t = Trend × Seasonality × Cyclical × Irregular
This chapter discusses time-series analysis and forecasting methods. It covers computing and interpreting index numbers, testing for randomness, and identifying trend, seasonality, cyclical and irregular components in a time series. It also describes smoothing-based forecasting models like moving averages and exponential smoothing, as well as autoregressive and autoregressive integrated moving average models. The chapter aims to help readers analyze time-series data and develop forecasts.
The document provides additional information on correlation analysis. It discusses various examples of correlation between variables like sugar consumption and activity level. It explains the characteristics of a relationship such as the direction, form, and degree of correlation. Correlations can be used for prediction, validity, and reliability. The document also discusses the difference between correlation and causation. It then provides examples to test the reader's understanding of correlation through multiple choice questions. Finally, it covers topics like probable error, coefficient of correlation, coefficient of determination, Spearman's rank correlation method, and concurrent deviation method for calculating correlation.
This document provides an overview of correlation coefficients and how to interpret them. It discusses the difference between correlation strength and significance. The key points covered are:
ONE: Correlation coefficients measure the strength and direction of association between two variables but do not imply causation. Strength is evaluated on a scale from -1 to 1 while significance is determined by comparing the p-value to the significance level alpha.
TWO: There are two parts to interpreting a correlation - the coefficient indicates strength (weak, moderate, strong) while the p-value determines if the correlation is statistically significant or could be due to chance.
THREE: Examples are provided to demonstrate how to interpret correlation output and determine the most strongly correlated variables
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
This presentation is made to represent the basic transportation model. The aim of this presentation is to implement the transportation model in solving transportation problem.
This document provides solutions to practice problems for hypothesis testing. It tests claims about population parameters such as drug failure rates and textbook prices against sample data. For each problem it states the null and alternative hypotheses, calculates the test statistic, finds the critical value, and makes a decision to reject or fail to reject the null hypothesis. It defines type I and type II errors and explains how lower variation in test scores does not necessarily indicate students are performing better.
The document contains 17 multiple choice questions related to hypothesis testing concepts such as null and alternative hypotheses, Type I and Type II errors, critical values, significance levels, t-tests, z-tests, chi-square tests, and degrees of freedom. The questions cover topics like identifying the hypothesis being tested, defining Type I errors, identifying critical values and significance levels, specifying alternative hypotheses, determining acceptance regions, and calculating degrees of freedom for different statistical tests. The document also provides the answers to each question.
The document summarizes hypothesis testing for claims about population proportions. It provides the steps to conduct a hypothesis test on a proportion using the traditional critical value method, P-value method, and confidence interval method. An example is included to demonstrate conducting a hypothesis test to examine the claim that most consumers are uncomfortable with drone deliveries using the traditional and P-value methods. Both methods lead to the same conclusion - that there is sufficient evidence to reject the null hypothesis and support the claim.
The document contains 73 multiple choice questions related to statistical concepts such as hypothesis testing, measures of central tendency, correlation, regression, sampling, and chi-square tests. The questions cover topics like the difference between parameters and statistics, types of errors in hypothesis testing, measures of dispersion, levels of significance, and definitions of key statistical terms.
Quiz 51. The mean weight of the adults in a certain region is .docxcatheryncouper
Quiz 5
1. The mean weight of the adults in a certain region is believed to be . An agency thinks that this
mean may be higher than the above belief. They are going to take a simple random sample of 100 adults for a hypothesis test at 5% level of significance.
a) State the null and the alternate hypothesis for this test.
b) If the mean weight of the adults in the above simple random sample is and we know that the population standard deviation is , compute the test statistic and the P_value of the test.
c) Will you reject or not reject the null hypothesis at 5% level of significance?
2. An expensive medication from a particular company states 100 ml for the contents on the bottles for this medication. An agency would like to test if the bottles are being under filled, in the sense that the mean contents of all bottles is less than 100 ml.
a) State the null hypothesis and the alternative hypothesis.
b) The contents in 10 randomly selected bottles were measured, and the results are below in ml.
contents in ml
96.0 102.0 99.4 100.5 97.5 98.7 103.4 97.6 98.5 99.4
Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3
contents 10 0 99.300 0.696 2.201 96.000 97.575 99.050 100.875
Variable Maximum
contents 103.400
State the test statistic
State the P_value.
Does this sample show a good evidence at 5% level of significance that the bottles are being under filled?
3. The electricity supply in a certain region appears to show a lot of voltage fluctuation from the required value of 240 V.
To check if the mean voltage is different from 240 V, the voltage will be measured at 15 randomly selected times.
a) State the null and the alternative hypothesis for this test.
b) The following are the readings shown by the 15 measurements.
voltage
222.2 217.9 211.9 219.7 208.8 229.5 219.4 208.1 209.0 208.2 196.2 211.7 199.9 201.1 218.0
Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3
voltage 15 0 212.11 2.35 9.10 196.20 208.10 211.70 219.40
Variable Maximum
voltage 229.50
a) Write the test statistic.
b) Will you reject or not reject the null hypothesis at 5% level of significance?
c) Compute a 95% confidence interval for the overall mean voltage based on the results from the above sample.
d) Does this interval contain the value 240 Volt?
e) Explain the usage of a 95% confidence interval for a two sided hypothesis test for a population mean at a 5% level of significance.
4.
In the past, 44% of those taking a public accounting qualifying exam have passed the exam on their first try. Lately, the availability of exam preparation books and tutoring sessions may have improved the likelihood of an individual’s passing on his or her first try. To test if the likelihood of passing in the first try in now higher,
a simple random sample of recent applicants will be examined.
a) State the null hypothesis and the alternative ...
1. The document discusses hypothesis testing of claims about population parameters such as proportions, means, standard deviations, and variances from one or two samples.
2. Key concepts include hypothesis tests using z-tests, t-tests, and chi-square tests. Confidence intervals are also constructed for parameters.
3. Two examples are provided to demonstrate hypothesis testing of claims about two population proportions using z-tests. The null hypothesis is rejected in one example but not the other.
This document provides an introduction to hypothesis testing. It discusses key concepts such as the null and alternative hypotheses, types of errors, levels of significance, test statistics, p-values, and decision rules. Examples are provided to demonstrate how to state hypotheses, identify the type of test, find critical values and rejection regions, calculate test statistics and p-values, and make decisions to reject or fail to reject the null hypothesis based on these concepts. The steps outlined include stating the hypotheses, specifying the significance level, determining the test statistic and sampling distribution, finding the p-value or using rejection regions to make a decision, and interpreting what the decision means for the original claim.
This document provides a quiz on statistical hypothesis testing concepts. It contains 10 multiple choice questions testing understanding of key terms like hypotheses, type I and II errors, and identifying alternative hypotheses for one-tailed and two-tailed tests. The questions cover topics like identifying the appropriate null hypothesis, determining when type I and II errors occur, and interpreting test statistics in relation to critical values and rejection regions. An answer key is provided for self-checking.
This document discusses hypothesis testing of claims about population standard deviations and variances. It provides the steps to conduct hypothesis tests of such claims using the chi-square distribution. The chi-square test can be used to test if a sample variance or standard deviation is significantly different from a claimed population variance or standard deviation. Examples show how to identify the null and alternative hypotheses, calculate the test statistic, find the critical value, and make a decision to reject or fail to reject the null hypothesis based on the test statistic and critical value.
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Chapter 8: Hypothesis Testing
8.1: Basics of Hypothesis Testing
This document discusses hypothesis testing for claims about population proportions and the difference between two population proportions. It provides information on type I and type II errors. Examples are provided to demonstrate hypothesis testing for a single proportion claim and the difference between two proportions. The examples show setting up the null and alternative hypotheses, checking assumptions, calculating the test statistic, determining the p-value or comparing to the critical value, and making a conclusion. Confidence intervals are also discussed as a way to estimate population proportions and differences between proportions. The examples provide step-by-step workings to test claims about spending behaviors with different denominations of money.
The document discusses confidence intervals and hypothesis testing. It provides examples of constructing 95% confidence intervals for a population mean using a sample mean and standard deviation. It also demonstrates how to identify the null and alternative hypotheses, determine if a test is right-tailed, left-tailed, or two-tailed, and calculate p-values to conclude whether to reject or fail to reject the null hypothesis based on a significance level of 0.05. Examples include testing claims about population proportions and means.
The document discusses confidence intervals and hypothesis testing. It provides examples of constructing 95% confidence intervals for a population mean using a sample mean and standard deviation. It also demonstrates how to identify the null and alternative hypotheses, determine if a test is right-tailed, left-tailed, or two-tailed, and calculate p-values to conclude whether to reject or fail to reject the null hypothesis based on a significance level of 0.05. Examples include testing claims about population proportions and means.
The document discusses confidence intervals and hypothesis testing. It provides examples of constructing 95% confidence intervals for a population mean and proportion. It also demonstrates identifying the null and alternative hypotheses and interpreting the results of hypothesis tests, including calculating p-values.
This document provides an introduction to hypothesis testing and significance levels. It discusses developing tests for null hypotheses and the types of errors that can occur. It describes using critical regions and significance levels like α=0.05 to determine whether to reject the null hypothesis. The document presents methods for testing hypotheses about the mean of a normal distribution when the variance is known and unknown. It also discusses testing the equality of means of two normal populations. Examples are provided to illustrate hypothesis testing methods.
This document provides an overview of hypothesis testing concepts and procedures. It discusses the introduction to hypothesis testing including null and alternative hypotheses. It describes significance levels and types of errors. It covers tests for the mean of a normal population including cases of known and unknown variances. It discusses tests for the equality of means of two normal populations. It also covers paired t-tests, tests concerning the variance of a normal population, and hypothesis tests in binomial populations. Examples are provided to illustrate key concepts and procedures for conducting hypothesis tests.
This document provides an overview of hypothesis testing. It begins by defining hypothesis testing and listing the typical steps: 1) formulating the null and alternative hypotheses, 2) computing the test statistic, 3) determining the p-value and interpretation, and 4) specifying the significance level. It then discusses different types of hypothesis tests for claims about a mean when the population standard deviation is known or unknown, as well as tests for claims about a population proportion. Examples are provided for each type of test to demonstrate how to apply the steps. The document aims to explain the concept and process of hypothesis testing for making data-driven decisions about statistical claims.
a) Null hypothesis and alternative hypothesis.
b) Type I and type II error
c) Acceptance region and rejection region
d) Define level of significance.
e) power of a hypothesis test and its measurement.
This document provides an overview of hypothesis testing, including:
- The two types of statistical hypotheses: the null hypothesis (H0) and alternative hypothesis (H1)
- How to define the hypotheses for different study designs, such as one-tailed, two-tailed, or directional alternatives
- How to determine critical values and critical regions based on the significance level (α)
- How to calculate a test statistic and make a decision to reject or not reject the null hypothesis
- Examples of hypothesis testing for different research scenarios involving means
The document discusses various statistical concepts related to hypothesis testing, including:
- Types I and II errors that can occur when testing hypotheses
- How the probability of committing errors depends on factors like the sample size and how far the population parameter is from the hypothesized value
- The concept of critical regions and how they are used to determine if a null hypothesis can be rejected
- The difference between discrete and continuous probability distributions and examples of each
- How an observed test statistic is calculated and compared to a critical value to determine whether to reject or not reject the null hypothesis
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1. CHAPTER 9—HYPOTHESIS TESTS
MULTIPLE CHOICE
1. The sum of the values of α and β
a. always add up to 1.0
b. always add up to 0.5
c. is the probability of Type II error
d. None of these alternatives is correct.
ANS: D PTS: 1 TOP: Hypothesis Tests
2. What type of error occurs if you fail to reject H0 when, in fact, it is not true?
a. Type II
b. Type I
c. either Type I or Type II, depending on the level of significance
d. either Type I or Type II, depending on whether the test is one tail or two tail
ANS: A PTS: 1 TOP: Hypothesis Tests
3. An assumption made about the value of a population parameter is called a
a. hypothesis
b. conclusion
c. confidence
d. significance
ANS: A PTS: 1 TOP: Hypothesis Tests
4. The probability of committing a Type I error when the null hypothesis is true is
a. the confidence level
b. β
c. greater than 1
d. the Level of Significance
ANS: D PTS: 1 TOP: Hypothesis Tests
5. In hypothesis testing,
a. the smaller the Type I error, the smaller the Type II error will be
b. the smaller the Type I error, the larger the Type II error will be
c. Type II error will not be effected by Type I error
d. the sum of Type I and Ttype II errors must equal to 1
ANS: B PTS: 1 TOP: Hypothesis Tests
6. In hypothesis testing, the tentative assumption about the population parameter is
a. the alternative hypothesis
b. the null hypothesis
c. either the null or the alternative
d. None of these alternatives is correct.
ANS: B PTS: 1 TOP: Hypothesis Tests
7. For a lower tail test, the p-value is the probability of obtaining a value for the test statistic
a. at least as small as that provided by the sample
2. b. at least as large as that provided by the sample
c. at least as small as that provided by the population
d. at least as large as that provided by the population.
ANS: A PTS: 1 TOP: Hypothesis Tests
8. The p-value is a probability that measures the support (or lack of support) for the
a. null hypothesis
b. alternative hypothesis
c. either the null or the alternative hypothesis
d. sample statistic
ANS: A PTS: 1 TOP: Hypothesis Tests
9. The p-value
a. is the same as the Z statistic
b. measures the number of standard deviations from the mean
c. is a distance
d. is a probability
ANS: D PTS: 1 TOP: Hypothesis Tests
10. For a two-tail test, the p-value is the probability of obtaining a value for the test statistic as
a. likely as that provided by the sample
b. unlikely as that provided by the sample
c. likely as that provided by the population
d. unlikely as that provided by the population
ANS: B PTS: 1 TOP: Hypothesis Tests
11. In hypothesis testing if the null hypothesis is rejected,
a. no conclusions can be drawn from the test
b. the alternative hypothesis is true
c. the data must have been accumulated incorrectly
d. the sample size has been too small
ANS: B PTS: 1 TOP: Hypothesis Tests
12. The level of significance is the
a. maximum allowable probability of Type II error
b. maximum allowable probability of Type I error
c. same as the confidence coefficient
d. same as the p-value
ANS: B PTS: 1 TOP: Hypothesis Tests
13. The power curve provides the probability of
a. correctly accepting the null hypothesis
b. incorrectly accepting the null hypothesis
c. correctly rejecting the alternative hypothesis
d. correctly rejecting the null hypothesis
ANS: D PTS: 1 TOP: Hypothesis Tests
14. A Type II error is committed when
a. a true alternative hypothesis is mistakenly rejected
3. b. a true null hypothesis is mistakenly rejected
c. the sample size has been too small
d. not enough information has been available
ANS: A PTS: 1 TOP: Hypothesis Tests
15. The error of rejecting a true null hypothesis is
a. a Type I error
b. a Type II error
c. is the same as β
d. committed when not enough information is available
ANS: A PTS: 1 TOP: Hypothesis Tests
16. The level of significance in hypothesis testing is the probability of
a. accepting a true null hypothesis
b. accepting a false null hypothesis
c. rejecting a true null hypothesis
d. None of these alternatives is correct.
ANS: C PTS: 1 TOP: Hypothesis Tests
17. The level of significance
a. can be any positive value
b. can be any value
c. is (1 - confidence level)
d. can be any value between -1.96 to 1.96
ANS: C PTS: 1 TOP: Hypothesis Tests
18. In hypothesis testing if the null hypothesis has been rejected when the alternative hypothesis has been
true,
a. a Type I error has been committed
b. a Type II error has been committed
c. either a Type I or Type II error has been committed
d. the correct decision has been made
ANS: D PTS: 1 TOP: Hypothesis Tests
19. The probability of making a Type I error is denoted by
a. α
b. β
c. 1 - α
d. 1 - β
ANS: A PTS: 1 TOP: Hypothesis Tests
20. The probability of making a Type II error is denoted by
a. α
b. β
c. 1 - α
d. 1 - β
ANS: B PTS: 1 TOP: Hypothesis Tests
4. 21. When the following hypotheses are being tested at a level of significance of α
H0: µ 500
Ha: µ < 500
the null hypothesis will be rejected if the p-value is
a. α
b. > α
c. > α/2
d. 1 - α/2
ANS: A PTS: 1 TOP: Hypothesis Tests
22. When the p-value is used for hypothesis testing, the null hypothesis is rejected if
a. p-value α
b. α < p-value
c. p-value α
d. p-value = 1 - α
ANS: A PTS: 1 TOP: Hypothesis Tests
23. In order to test the following hypotheses at an α level of significance
H0: µ 800
Ha: µ > 800
the null hypothesis will be rejected if the test statistic Z is
a. Zα
b. < Zα
c. < -Zα
d. = α
ANS: A PTS: 1 TOP: Hypothesis Tests
24. Which of the following does not need to be known in order to compute the p-value?
a. knowledge of whether the test is one-tailed or two-tailed
b. the value of the test statistic
c. the level of significance
d. None of these alternatives is correct.
ANS: C PTS: 1 TOP: Hypothesis Tests
25. In the hypothesis testing procedure, α is
a. the level of significance
b. the critical value
c. the confidence level
d. 1 - level of significance
ANS: A PTS: 1 TOP: Hypothesis Tests
26. If a hypothesis test leads to the rejection of the null hypothesis,
a. a Type II error must have been committed
b. a Type II error may have been committed
5. c. a Type I error must have been committed
d. a Type I error may have been committed
ANS: D PTS: 1 TOP: Hypothesis Tests
27. As the test statistic becomes larger, the p-value
a. gets smaller
b. becomes larger
c. stays the same, since the sample size has not been changed
d. becomes negative
ANS: A PTS: 1 TOP: Hypothesis Tests
28. The p-value ranges between
a. zero and infinity
b. minus infinity to plus infinity
c. zero to one
d. -1 to +1
ANS: C PTS: 1 TOP: Hypothesis Tests
29. For a lower bounds one-tailed test, the test statistic z is determined to be zero. The p-value for this test
is
a. zero
b. -0.5
c. +0.5
d. 1.00
ANS: C PTS: 1 TOP: Hypothesis Tests
30. In a two-tailed hypothesis test situation, the test statistic is determined to be t = -2.692. The sample
size has been 45. The p-value for this test is
a. -0.005
b. +0.005
c. -0.01
d. +0.01
ANS: D PTS: 1 TOP: Hypothesis Tests
31. In a lower one-tail hypothesis test situation, the p-value is determined to be 0.2. If the sample size for
this test is 51, the t statistic has a value of
a. 0.849
b. -0.849
c. 1.299
d. -1.299
ANS: B PTS: 1 TOP: Hypothesis Tests
32. If a hypothesis is rejected at the 5% level of significance, it
a. will always be rejected at the 1% level
b. will always be accepted at the 1% level
c. will never be tested at the 1% level
d. may be rejected or not rejected at the 1% level
ANS: D PTS: 1 TOP: Hypothesis Tests
6. 33. If a hypothesis is not rejected at the 5% level of significance, it
a. will also not be rejected at the 1% level
b. will always be rejected at the 1% level
c. will sometimes be rejected at the 1% level
d. None of these alternatives is correct.
ANS: A PTS: 1 TOP: Hypothesis Tests
34. If the probability of a Type I error (α) is 0.05, then the probability of a Type II error (β) must be
a. 0.05
b. 0.95
c. 0.025
d. None of these alternatives is correct.
ANS: D PTS: 1 TOP: Hypothesis Tests
35. If the level of significance of a hypothesis test is raised from .01 to .05, the probability of a Type II
error
a. will also increase from .01 to .05
b. will not change
c. will decrease
d. will increase
ANS: C PTS: 1 TOP: Hypothesis Tests
36. If a hypothesis is rejected at 95% confidence, it
a. will always be accepted at 90% confidence
b. will always be rejected at 90% confidence
c. will sometimes be rejected at 90% confidence
d. None of these alternatives is correct.
ANS: B PTS: 1 TOP: Hypothesis Tests
37. For a two-tailed test at 86.12% confidence, Z =
a. 1.96
b. 1.48
c. 1.09
d. 0.86
ANS: B PTS: 1 TOP: Hypothesis Tests
38. For a one-tailed test (lower tail) at 93.7% confidence, Z =
a. -1.86
b. -1.53
c. -1.96
d. -1.645
ANS: B PTS: 1 TOP: Hypothesis Tests
39. Read the Z statistic from the normal distribution table and circle the correct answer. A one-tailed test
(upper tail) at 87.7% confidence; Z =
a. 1.54
b. 1.96
c. 1.645
d. 1.16
7. ANS: D PTS: 1 TOP: Hypothesis Tests
40. For a two-tailed test, a sample of 20 at 80% confidence, t =
a. 1.328
b. 2.539
c. 1.325
d. 2.528
ANS: A PTS: 1 TOP: Hypothesis Tests
41. For a one-tailed test (upper tail), a sample size of 18 at 95% confidence, t =
a. 2.12
b. -2.12
c. -1.740
d. 1.740
ANS: D PTS: 1 TOP: Hypothesis Tests
42. For a one-tailed test (lower tail), a sample size of 10 at 90% confidence, t =
a. 1.383
b. 2.821
c. -1.383
d. -2.821
ANS: C PTS: 1 TOP: Hypothesis Tests
43. A two-tailed test is performed at 95% confidence. The p-value is determined to be 0.09. The null
hypothesis
a. must be rejected
b. should not be rejected
c. could be rejected, depending on the sample size
d. has been designed incorrectly
ANS: B PTS: 1 TOP: Hypothesis Tests
44. For a two-tailed test at 98.4% confidence, Z =
a. 1.96
b. 1.14
c. 2.41
d. 0.8612
ANS: C PTS: 1 TOP: Hypothesis Tests
45. For a one-tailed test (lower tail) at 89.8% confidence, Z =
a. -1.27
b. -1.53
c. -1.96
d. -1.64
ANS: A PTS: 1 TOP: Hypothesis Tests
46. For a one-tailed test (upper tail) at 93.7% confidence, Z =
a. 1.50
b. 1.96
c. 1.645
d. 1.53
8. ANS: D PTS: 1 TOP: Hypothesis Tests
47. For a one-tailed test (upper tail), a sample size of 26 at 90% confidence, t =
a. 1.316
b. -1.316
c. -1.740
d. 1.740
ANS: A PTS: 1 TOP: Hypothesis Tests
48. For a one-tailed test (lower tail) with 22 degrees of freedom at 95% confidence, the value of t =
a. -1.383
b. 1.383
c. -1.717
d. -1.721
ANS: C PTS: 1 TOP: Hypothesis Tests
49. For a one-tailed hypothesis test (upper tail) the p-value is computed to be 0.034. If the test is being
conducted at 95% confidence, the null hypothesis
a. could be rejected or not rejected depending on the sample size
b. could be rejected or not rejected depending on the value of the mean of the sample
c. is not rejected
d. is rejected
ANS: D PTS: 1 TOP: Hypothesis Tests
50. In a two-tailed hypothesis test the test statistic is determined to be Z = -2.5. The p-value for this test is
a. -1.25
b. 0.4938
c. 0.0062
d. 0.0124
ANS: D PTS: 1 TOP: Hypothesis Tests
51. In a one-tailed hypothesis test (lower tail) the test statistic is determined to be -2. The p-value for this
test is
a. 0.4772
b. 0.0228
c. 0.0056
d. 0.5228
ANS: B PTS: 1 TOP: Hypothesis Tests
52. The average manufacturing work week in metropolitan Chattanooga was 40.1 hours last year. It is
believed that the recession has led to a reduction in the average work week. To test the validity of this
belief, the hypotheses are
a. H0: µ < 40.1 Ha: µ ≥ 40.1
b. H0: µ ≥ 40.1 Ha: µ < 40.1
c. H0: µ > 40.1 Ha: µ ≤ 40.1
d. H0: µ = 40.1 Ha: µ ≠ 40.1
ANS: B PTS: 1 TOP: Hypothesis Tests
9. 53. The average monthly rent for one-bedroom apartments in Chattanooga has been $700. Because of the
downturn in the real estate market, it is believed that there has been a decrease in the average rental.
The correct hypotheses to be tested are
a. H0: µ ≥ 700 Ha: µ < 700
b. H0: µ = 700 Ha: µ ≠ 700
c. H0: µ > 700 Ha: µ ≤700
d. H0: µ < 700 Ha: µ ≥ 700
ANS: A PTS: 1 TOP: Hypothesis Tests
54. A machine is designed to fill toothpaste tubes with 5.8 ounces of toothpaste. The manufacturer does
not want any underfilling or overfilling. The correct hypotheses to be tested are
a. H0: µ ≠ 5.8 Ha: µ = 5.8
b. H0: µ = 5.8 Ha: µ ≠ 5.8
c. H0: µ > 5.8 Ha: µ ≤ 5.8
d. H0: µ ≥ 5.8 Ha: µ < 5.8
ANS: B PTS: 1 TOP: Hypothesis Tests
55. The average hourly wage of computer programmers with 2 years of experience has been $21.80.
Because of high demand for computer programmers, it is believed there has been a significant
increase in the average wage of computer programmers. To test whether or not there has been an
increase, the correct hypotheses to be tested are
a. H0: µ < 21.80 Ha: µ ≥ 21.80
b. H0: µ = 21.80 Ha: µ ≠ 21.80
c. H0: µ > 21.80 Ha: µ ≤ 21.80
d. H0: µ ≤ 21.80 Ha: µ > 21.80
ANS: D PTS: 1 TOP: Hypothesis Tests
56. A student believes that the average grade on the final examination in statistics is at least 85. She plans
on taking a sample to test her belief. The correct set of hypotheses is
a. H0: µ < 85 Ha: µ 85
b. H0: µ 85 Ha: µ > 85
c. H0: µ 85 Ha: µ < 85
d. H0: µ > 85 Ha: µ 85
ANS: C PTS: 1 TOP: Hypothesis Tests
57. In the past, 75% of the tourists who visited Chattanooga went to see Rock City. The management of
Rock City recently undertook an extensive promotional campaign. They are interested in determining
whether the promotional campaign actually increased the proportion of tourists visiting Rock City.
The correct set of hypotheses is
a. H0: P > 0.75 Ha: P 0.75
b. H0: P < 0.75 Ha: P 0.75
c. H0: P 0.75 Ha: P < 0.75
d. H0: P 0.75 Ha: P > 0.75
ANS: D PTS: 1 TOP: Hypothesis Tests
58. The average life expectancy of tires produced by the Whitney Tire Company has been 40,000 miles.
Management believes that due to a new production process, the life expectancy of their tires has
increased. In order to test the validity of their belief, the correct set of hypotheses is
10. a. H0: µ < 40,000 Ha: µ 40,000
b. H0: µ 40,000 Ha: µ > 40,000
c. H0: µ > 40,000 Ha: µ 40,000
d. H0: µ 40,000 Ha: µ < 40,000
ANS: B PTS: 1 TOP: Hypothesis Tests
59. A soft drink filling machine, when in perfect adjustment, fills the bottles with 12 ounces of soft drink.
Any over filling or under filling results in the shutdown and readjustment of the machine. To
determine whether or not the machine is properly adjusted, the correct set of hypotheses is
a. H0: µ < 12 Ha: µ 12
b. H0: µ 12 Ha: µ > 12
c. H0: µ ≠ 12 Ha: µ = 12
d. H0: µ = 12 Ha: µ ≠ 12
ANS: D PTS: 1 TOP: Hypothesis Tests
60. The academic planner of a university thinks that at least 35% of the entire student body attends
summer school. The correct set of hypotheses to test his belief is
a. H0: P > 0.35 Ha: P 0.35
b. H0: P 0.35 Ha: P > 0.35
c. H0: P 0.35 Ha: P < 0.35
d. H0: P > 0.35 Ha: P 0.35
ANS: C PTS: 1 TOP: Hypothesis Tests
61. The manager of an automobile dealership is considering a new bonus plan in order to increase sales.
Currently, the mean sales rate per salesperson is five automobiles per month. The correct set of
hypotheses for testing the effect of the bonus plan is
a. H0: µ < 5 Ha: µ 5
b. H0: µ 5 Ha: µ > 5
c. H0: µ > 5 Ha: µ 5
d. H0: µ 5 Ha: µ < 5
ANS: B PTS: 1 TOP: Hypothesis Tests
62. Your investment executive claims that the average yearly rate of return on the stocks she recommends
is at least 10.0%. You plan on taking a sample to test her claim. The correct set of hypotheses is
a. H0: µ < 10.0% Ha: µ 10.0%
b. H0: µ 10.0% Ha: µ > 10.0%
c. H0: µ > 10.0% Ha: µ 10.0%
d. H0: µ 10.0% Ha: µ < 10.0%
ANS: D PTS: 1 TOP: Hypothesis Tests
63. A weatherman stated that the average temperature during July in Chattanooga is 80 degrees or less. A
sample of 32 Julys is taken. The correct set of hypotheses is
a. H0: µ 80 Ha: µ < 80
b. H0: µ 80 Ha: µ > 80
c. H0: µ ≠ 80 Ha: µ = 80
d. H0: µ < 80 Ha: µ > 80
ANS: B PTS: 1 TOP: Hypothesis Tests
11. 64. The school's newspaper reported that the proportion of students majoring in business is at least 30%.
You plan on taking a sample to test the newspaper's claim. The correct set of hypotheses is
a. H0: P < 0.30 Ha: P 0.30
b. H0: P 0.30 Ha: P > 0.30
c. H0: P 0.30 Ha: P < 0.30
d. H0: P > 0.30 Ha: P 0.30
ANS: C PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-01
Exhibit 9-1
n = 36 = 24.6 S = 12 H0: µ 20
Ha: µ > 20
NARREND
65. Refer to Exhibit 9-1. The test statistic is
a. 2.3
b. 0.38
c. -2.3
d. -0.38
ANS: A PTS: 1 TOP: Hypothesis Tests
66. Refer to Exhibit 9-1. The p-value is between
a. 0.005 to 0.01
b. 0.01 to 0.025
c. 0.025 to 0.05
d. 0.05 to 0.10
ANS: B PTS: 1 TOP: Hypothesis Tests
67. Refer to Exhibit 9-1. If the test is done at 95% confidence, the null hypothesis should
a. not be rejected
b. be rejected
c. Not enough information is given to answer this question.
d. None of these alternatives is correct.
ANS: B PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-02
Exhibit 9-2
n = 64 = 50 s = 16 H0: µ 54
Ha: µ < 54
NARREND
68. Refer to Exhibit 9-2. The test statistic equals
a. -4
b. -3
c. -2
12. d. -1
ANS: C PTS: 1 TOP: Hypothesis Tests
69. Refer to Exhibit 9-2. The p-value is between
a. .005 to .01
b. .01 to .025
c. .025 to .05
d. .05 to .01
ANS: B PTS: 1 TOP: Hypothesis Tests
70. Refer to Exhibit 9-2. If the test is done at 95% confidence, the null hypothesis should
a. not be rejected
b. be rejected
c. Not enough information is given to answer this question.
d. None of these alternatives is correct.
ANS: B PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-03
Exhibit 9-3
n = 49 = 54.8 s = 28 H0: µ 50
Ha: µ > 50
NARREND
71. Refer to Exhibit 9-3. The test statistic is
a. 0.1714
b. 0.3849
c. -1.2
d. 1.2
ANS: D PTS: 1 TOP: Hypothesis Tests
72. Refer to Exhibit 9-3. The p-value is between
a. 0.01 to 0.025
b. 0.025 to 0.05
c. .05 to 0.1
d. 0.1 to 0.2
ANS: D PTS: 1 TOP: Hypothesis Tests
73. Refer to Exhibit 9-3. If the test is done at the 5% level of significance, the null hypothesis should
a. not be rejected
b. be rejected
c. Not enough information given to answer this question.
d. None of these alternatives is correct.
ANS: A PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-04
Exhibit 9-4
13. The manager of a grocery store has taken a random sample of 100 customers. The average length of
time it took the customers in the sample to check out was 3.1 minutes with a standard deviation of 0.5
minutes. We want to test to determine whether or not the mean waiting time of all customers is
significantly more than 3 minutes.
NARREND
74. Refer to Exhibit 9-4. The test statistic is
a. 1.96
b. 1.64
c. 2.00
d. 0.056
ANS: C PTS: 1 TOP: Hypothesis Tests
75. Refer to Exhibit 9-4. The p-value is between
a. .005 to .01
b. .01 to .025
c. .025 to .05
d. .05 to .10
ANS: B PTS: 1 TOP: Hypothesis Tests
76. Refer to Exhibit 9-4. At 95% confidence, it can be concluded that the mean of the population is
a. significantly greater than 3
b. not significantly greater than 3
c. significantly less than 3
d. significantly greater then 3.18
ANS: A PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-05
Exhibit 9-5
A random sample of 100 people was taken. Eighty-five of the people in the sample favored Candidate
A. We are interested in determining whether or not the proportion of the population in favor of
Candidate A is significantly more than 80%.
NARREND
77. Refer to Exhibit 9-5. The test statistic is
a. 0.80
b. 0.05
c. 1.25
d. 2.00
ANS: C PTS: 1 TOP: Hypothesis Tests
78. Refer to Exhibit 9-5. The p-value is
a. 0.2112
b. 0.05
c. 0.025
d. 0.1056
ANS: D PTS: 1 TOP: Hypothesis Tests
79. Refer to Exhibit 9-5. At 95% confidence, it can be concluded that the proportion of the population in
favor of candidate A
14. a. is significantly greater than 80%
b. is not significantly greater than 80%
c. is significantly greater than 85%
d. is not significantly greater than 85%
ANS: B PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-06
Exhibit 9-6
A random sample of 16 students selected from the student body of a large university had an average
age of 25 years and a standard deviation of 2 years. We want to determine if the average age of all the
students at the university is significantly more than 24. Assume the distribution of the population of
ages is normal.
NARREND
80. Refer to Exhibit 9-6. The test statistic is
a. 1.96
b. 2.00
c. 1.645
d. 0.05
ANS: B PTS: 1 TOP: Hypothesis Tests
81. Refer to Exhibit 9-6. The p-value is between
a. .005 to .01
b. .01 to .025
c. .025 to .05
d. .05 to .10
ANS: C PTS: 1 TOP: Hypothesis Tests
82. Refer to Exhibit 9-6. At 95% confidence, it can be concluded that the mean age is
a. not significantly different from 24
b. significantly different from 24
c. significantly less than 24
d. significantly more than 24
ANS: D PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-07
Exhibit 9-7
A random sample of 16 statistics examinations from a large population was taken. The average score
in the sample was 78.6 with a variance of 64. We are interested in determining whether the average
grade of the population is significantly more than 75. Assume the distribution of the population of
grades is normal.
NARREND
83. Refer to Exhibit 9-7. The test statistic is
a. 0.45
b. 1.80
c. 3.6
d. 8
ANS: B PTS: 1 TOP: Hypothesis Tests
15. 84. Refer to Exhibit 9-7. The p-value is between
a. .005 to .01
b. .01 to .025
c. .025 to .05
d. .05 to 0.1
ANS: C PTS: 1 TOP: Hypothesis Tests
85. Refer to Exhibit 9-7. At 95% confidence, it can be concluded that the average grade of the population
a. is not significantly greater than 75
b. is significantly greater than 75
c. is not significantly greater than 78.6
d. is significantly greater than 78.6
ANS: B PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-08
Exhibit 9-8
The average gasoline price of one of the major oil companies in Europe has been $1.25 per liter.
Recently, the company has undertaken several efficiency measures in order to reduce prices.
Management is interested in determining whether their efficiency measures have actually reduced
prices. A random sample of 49 of their gas stations is selected and the average price is determined to
be $1.20 per liter. Furthermore, assume that the standard deviation of the population ( ) is $0.14.
NARREND
86. Refer to Exhibit 9-8. The standard error has a value of
a. 0.14
b. 7
c. 2.5
d. 0.02
ANS: D PTS: 1 TOP: Hypothesis Tests
87. Refer to Exhibit 9-8. The value of the test statistic for this hypothesis test is
a. 1.96
b. 1.645
c. -2.5
d. -1.645
ANS: C PTS: 1 TOP: Hypothesis Tests
88. Refer to Exhibit 9-8. The p-value for this problem is
a. 0.4938
b. 0.0062
c. 0.0124
d. 0.05
ANS: B PTS: 1 TOP: Hypothesis Tests
NARRBEGIN: Exhibit 09-09
Exhibit 9-9
16. The sales of a grocery store had an average of $8,000 per day. The store introduced several
advertising campaigns in order to increase sales. To determine whether or not the advertising
campaigns have been effective in increasing sales, a sample of 64 days of sales was selected. It was
found that the average was $8,300 per day. From past information, it is known that the standard
deviation of the population is $1,200.
NARREND
89. Refer to Exhibit 9-9. The correct null hypothesis for this problem is
a. µ < 8000
b. µ > 8000
c. µ = 8000
d. µ > 8250
ANS: A PTS: 1 TOP: Hypothesis Tests
90. Refer to Exhibit 9-9. The value of the test statistic is
a. 250
b. 8000
c. 8250
d. 2.0
ANS: D PTS: 1 TOP: Hypothesis Tests
91. Refer to Exhibit 9-9. The p-value is
a. 2.00
b. 0.9772
c. 0.0228
d. 0.5475
ANS: C PTS: 1 TOP: Hypothesis Tests
PROBLEM
1. The Department of Economic and Community Development (DECD) reported that in 2009 the
average number of new jobs created per county was 450. The department also provided the following
information regarding a sample of 5 counties in 2010.
County New Jobs Created
In 2010
Bradley 410
Rhea 480
Marion 407
Grundy 428
Sequatchie 400
a. Compute the sample average and the standard deviation for 2010.
b. We want to determine whether there has been a significant decrease in the average number
of jobs created. Provide the null and the alternative hypotheses.
c. Compute the test statistic.
d. Compute the p-value; and at 95% confidence, test the hypotheses. Assume the population is
normally distributed.
ANS:
17. a. = 425 and s = 32.44 (rounded)
b. Ho: µ > 450
Ha: µ < 450
c. Test statistic t = -1.724
d. P-value is between 0.05 and 0.1; do not reject Ho. There is no evidence of a significant
decrease.
PTS: 1 TOP: Hypothesis Tests
2. The Bureau of Labor Statistics reported that the average yearly income of dentists in the year 2009 was
$110,000. A sample of 81 dentists, which was taken in 2010, showed an average yearly income of
$120,000. Assume the standard deviation of the population of dentists in 2010 is $36,000.
a. We want to test to determine if there has been a significant increase in the average yearly
income of dentists. Provide the null and the alternative hypotheses.
b. Compute the test statistic.
c. Determine the p-value; and at 95% confidence, test the hypotheses.
ANS:
a. Ho: µ ≤ $110,000
Ha: µ > $110,000
b. Z = 2.5
c. p-value = 0.0062
Since the p-value = 0.0062 < 0.05, reject Ho. Therefore, there has been a significant increase.
PTS: 1 TOP: Hypothesis Tests
3. A tire manufacturer has been producing tires with an average life expectancy of 26,000 miles. Now
the company is advertising that its new tires' life expectancy has increased. In order to test the
legitimacy of the advertising campaign, an independent testing agency tested a sample of 6 of their
tires and has provided the following data.
Life Expectancy
(In Thousands of Miles)
28
27
25
28
29
25
a. Determine the mean and the standard deviation.
b. At 99% confidence using the critical value approach, test to determine whether or not the tire
company is using legitimate advertising. Assume the population is normally distributed.
c. Repeat the test using the p-value approach.
ANS:
a. = 27, s = 1.67
b. Ho: µ < 26000
Ha: µ > 26000
Since t = 1.47 < 3.365, do not reject Ho and conclude that there is insufficient evidence to
support the manufacturer's claim.
18. c. p-value > 0.1; do not reject Ho
PTS: 1 TOP: Hypothesis Tests
4. A producer of various kinds of batteries has been producing "D" size batteries with a life expectancy
of 87 hours. Due to an improved production process, management believes that there has been an
increase in the life expectancy of their "D" size batteries. A sample of 36 batteries showed an average
life of 88.5 hours. Assume from past information that it is known that the standard deviation of the
population is 9 hours.
a. Give the null and the alternative hypotheses.
b. Compute the test statistic.
c. At 99% confidence using the critical value approach, test management's belief.
d. What is the p-value associated with the sample results? What is your conclusion based on
the p-value?
ANS:
a. Ho: µ < 87
Ha: µ > 87
b. 1.00
c. Since Z = 1 < 2.33, do not reject Ho and conclude that there is insufficient evidence to
support the corporation's claim.
d. p-value > 0.1587; therefore do not reject Ho
PTS: 1 TOP: Hypothesis Tests
5. Some people who bought X-Game gaming systems complained about having received defective
systems. The industry standard for such systems has been ninety-eight percent non-defective systems.
In a sample of 120 units sold, 6 units were defective.
a. Compute the proportion of defective items in the sample.
b. Compute the standard error of .
c. At 95% confidence using the critical value approach, test to see if the percentage of defective
systems produced by X-Game has exceeded the industry standard.
d. Show that the p-value approach results in the same conclusion as that of part b.
ANS:
a. 0.05
b. 0.0128
c. Test statistic Z = 2.35 > 1.645; reject Ho; the number of defects has exceeded the industry
standard.
d. p-value (.0094) < 0.05; reject Ho.
PTS: 1 TOP: Hypothesis Tests
6. Choo Choo Paper Company makes various types of paper products. One of their products is a 30 mils
thick paper. In order to ensure that the thickness of the paper meets the 30 mils specification, random
cuts of paper are selected and the thickness of each cut is measured. A sample of 256 cuts had a mean
thickness of 30.3 mils with a standard deviation of 4 mils.
19. a. Compute the standard error of the mean.
b. At 95% confidence using the critical value approach, test to see if the mean thickness is
significantly more than 30 mils.
c. Show that the p-value approach results in the same conclusion as that of part b.
ANS:
a. 0.25
b. Test statistics t = 1.2 < 1.645; do not reject Ho.
c. p-value (.1151) is between 0.1 and 0.2; do not reject Ho.
PTS: 1 TOP: Hypothesis Tests
7. Last year, 50% of MNM, Inc. employees were female. It is believed that there has been a reduction in
the percentage of females in the company. This year, in a random sample of 400 employees, 180 were
female.
a. Give the null and the alternative hypotheses.
b. At 95% confidence using the critical value approach, determine if there has been a significant
reduction in the proportion of females.
c. Show that the p-value approach results in the same conclusion as that of Part b.
ANS:
a. Ho: p ≥ 0.5
Ha: p < 0.5
b. Test statistic Z = -2.0 < -1.645; reject Ho; the proportion of female employees is significantly
less than 50%.
c. p-value = 0.0228 < 0.05; reject Ho.
PTS: 1 TOP: Hypothesis Tests
8. Last year, a soft drink manufacturer had 21% of the market. In order to increase their portion of the
market, the manufacturer has introduced a new flavor in their soft drinks. A sample of 400 individuals
participated in the taste test and 100 indicated that they like the taste. We are interested in determining
if more than 21% of the population will like the new soft drink.
a. Set up the null and the alternative hypotheses.
b. Determine the test statistic.
c. Determine the p-value.
d. At 95% confidence, test to determine if more than 21% of the population will like the new soft
drink.
ANS:
a. H0: p 0.21
Ha: p > 0.21
b. Test statistic Z = 1.96
c. p-value = 0.025
d. p-value = 0.025 < .05; therefore, reject Ho; more than 21% like the new drink.
PTS: 1 TOP: Hypothesis Tests
20. 9. In the past, the average age of employees of a large corporation has been 40 years. Recently, the
company has been hiring older individuals. In order to determine whether there has been an increase
in the average age of all the employees, a sample of 64 employees was selected. The average age in the
sample was 45 years with a standard deviation of 16 years. Let α = .05.
a. State the null and the alternative hypotheses.
b. Compute the test statistic.
c. Using the p-value approach, test to determine whether or not the mean age of all employees is
significantly more than 40 years.
ANS:
a. H0: µ 40
Ha: µ > 40
b. t = 2.5
c. p-value (.007518) is between .005 and .01; reject H0
PTS: 1 TOP: Hypothesis Tests
10. The average gasoline price of one of the major oil companies has been $2.20 per gallon. Because of
cost reduction measures, it is believed that there has been a significant reduction in the average price.
In order to test this belief, we randomly selected a sample of 36 of the company's gas stations and
determined that the average price for the stations in the sample was $2.14. Assume that the standard
deviation of the population (σ) is $0.12.
a. State the null and the alternative hypotheses.
b. Compute the test statistic.
c. What is the p-value associated with the above sample results?
d. At 95% confidence, test the company's claim.
ANS:
a. H0: µ 2.20
Ha: µ < 2.20
b. Z = -3
c. p-value = almost zero (0.0013)
d. p-value < .05; reject H0; the average price has been reduced.
PTS: 1 TOP: Hypothesis Tests
11. A sample of 81 account balances of a credit company showed an average balance of $1,200 with a
standard deviation of $126.
a. Formulate the hypotheses that can be used to determine whether the mean of all account
balances is significantly different from $1,150.
b. Compute the test statistic.
c. Using the p-value approach, what is your conclusion? Let α = .05.
ANS:
a. H0: µ = 1150
Ha: µ ≠ 1150
21. b. t = 3.57
c. p-value (almost zero) <.005; therefore, reject H0
PTS: 1 TOP: Hypothesis Tests
12. From a population of cans of coffee marked "12 ounces," a sample of 50 cans was selected and the
contents of each can were weighed. The sample revealed a mean of 11.8 ounces with a standard
deviation of 0.5 ounces.
a. Formulate the hypotheses to test to see if the mean of the population is at least 12 ounces.
b. Compute the test statistic.
c. Using the p-value approach, what is your conclusion? Let α = .05.
ANS:
a. H0: µ 12
Ha: µ < 12
b. t = -2.83
c. p-value (.0034) < .005; therefore, reject H0
PTS: 1 TOP: Hypothesis Tests
13. A lathe is set to cut bars of steel into lengths of 6 centimeters. The lathe is considered to be in perfect
adjustment if the average length of the bars it cuts is 6 centimeters. A sample of 121 bars is selected
randomly and measured. It is determined that the average length of the bars in the sample is 6.08
centimeters with a standard deviation of 0.44 centimeters.
a. Formulate the hypotheses to determine whether or not the lathe is in perfect adjustment.
b. Compute the test statistic.
c. Using the p-value approach, what is your conclusion? Let α = .05.
ANS:
a. H0: µ = 6
Ha: µ ≠ 6
b. t = 2
c. p-value (.0456) is between 0.02 and 0.05; therefore, reject H 0
PTS: 1 TOP: Hypothesis Tests
14. Ahmadi, Inc. has been manufacturing small automobiles that have averaged 50 miles per gallon of
gasoline in highway driving. The company has developed a more efficient engine for its small cars and
now advertises that its new small cars average more than 50 miles per gallon in highway driving. An
independent testing service road-tested 64 of the automobiles. The sample showed an average of 51.5
miles per gallon with a standard deviation of 4 miles per gallon.
a. Formulate the hypotheses to determine whether or not the manufacturer's advertising campaign
is legitimate.
b. Compute the test statistic.
c. What is the p-value associated with the sample results and what is your conclusion? Let α = .
05.
22. ANS:
a. H0: µ 50
Ha: µ > 50
b. t = 3
c. p-value (.0019) is less than .005; reject H0
PTS: 1 TOP: Hypothesis Tests
15. A soft drink filling machine, when in perfect adjustment, fills the bottles with 12 ounces of soft drink.
A random sample of 49 bottles is selected, and the contents are measured. The sample yielded a mean
content of 11.88 ounces with a standard deviation of 0.35 ounces.
a. Formulate the hypotheses to test to determine if the machine is in perfect adjustment.
b. Compute the value of the test statistic.
c. Compute the p-value and give your conclusion regarding the adjustment of the machine. Let α
= .05.
ANS:
a. H0: µ = 12
Ha: µ ≠ 12
b. t = -2.4
c. p-value is between 0.01 and 0.025; therefore, reject H0
PTS: 1 TOP: Hypothesis Tests
16. "D" size batteries produced by MNM Corporation have had a life expectancy of 87 hours. Because of
an improved production process, it is believed that there has been an increase in the life expectancy of
its "D" size batteries. A sample of 36 batteries showed an average life of 88.5 hours. Assume from past
information that it is known that the standard deviation of the population is 9 hours.
a. Formulate the hypotheses for this problem.
b. Compute the test statistic.
c. What is the p-value associated with the sample results? What is your conclusion based on the
p-value? Let α = .05.
ANS:
a. H0: µ 87
Ha: µ > 87
b. Z = 1
c. p-value = 0.1587; therefore, do not reject H0
PTS: 1 TOP: Hypothesis Tests
17. At a local university, a sample of 49 evening students was selected in order to determine whether the
average age of the evening students is significantly different from 21. The average age of the students
in the sample was 23 with a standard deviation of 3.5.
a. Formulate the hypotheses for this problem.
b. Compute the test statistic.
23. c. Determine the p-value and test these hypotheses. Let α = .05.
ANS:
a. H0: µ = 21
Ha: µ ≠ 21
b. t = 4
c. p-value is almost zero; therefore, reject H0
PTS: 1 TOP: Hypothesis Tests
18. In order to determine the average price of hotel rooms in Atlanta, a sample of 64 hotels was selected. It
was determined that the average price of the rooms in the sample was $108.50 with a standard
deviation of $16.
a. Formulate the hypotheses to determine whether or not the average room price is significantly
different from $112.
b. Compute the test statistic.
c. At 95% confidence using the p-value approach, test the hypotheses. Let α = 0.1.
ANS:
a. H0: µ = 112
Ha: µ ≠ 112
b. t = -1.75
c. p-value is between 0.025 and 0.05; therefore, do not reject H 0
PTS: 1 TOP: Hypothesis Tests
19. Identify the null and alternative hypotheses for the following problems.
a. The manager of a restaurant believes that it takes a customer less than or equal to 25 minutes to
eat lunch.
b. Economists have stated that the marginal propensity to consume is at least 90¢ out of every
dollar.
c. It has been stated that 75 out of every 100 people who go to the movies on Saturday night buy
popcorn.
ANS:
a. H0: µ 25
Ha: µ > 25
b. H0: p 0.9
Ha: p < 0.9
c. H0: p = 0.75
Ha: p ≠ 0.75
PTS: 1 TOP: Hypothesis Tests
20. A student believes that the average grade on the statistics final examination was 87. A sample of 36
final examinations was taken. The average grade in the sample was 83.96 with a standard deviation of
12.
24. a. State the null and alternative hypotheses.
b. Using the critical value approach, test the hypotheses at the 5% level of significance.
c. Using the p-value approach, test the hypotheses at the 5% level of significance.
ANS:
a. H0: µ = 87
Ha: µ ≠ 87
b. test statistic t = -1.52, critical t = 2.03; do not reject H0
c. p-value is between .05 and 0.1; therefore, do not reject H0
PTS: 1 TOP: Hypothesis Tests
21. A carpet company advertises that it will deliver your carpet within 15 days of purchase. A sample of
49 past customers is taken. The average delivery time in the sample was 16.2 days. The standard
deviation of the population (σ) is known to be 5.6 days.
a. State the null and alternative hypotheses.
b. Using the critical value approach, test to determine if their advertisement is legitimate. Let α
= .05.
c. Using the p-value approach, test the hypotheses at the 5% level of significance.
ANS:
a. H0: µ 15
Ha: µ > 15
b. test statistic Z = 1.5 < 1.645; therefore do not reject H0
c. Do not reject H0; p-value is (.5 - .4332) = 0.0668
PTS: 1 TOP: Hypothesis Tests
22. A sample of 30 cookies is taken to test the claim that each cookie contains at least 9 chocolate chips.
The average number of chocolate chips per cookie in the sample was 7.8 with a standard deviation of
3.
a. State the null and alternative hypotheses.
b. Using the critical value approach, test the hypotheses at the 5% level of significance.
c. Using the p-value approach, test the hypothesis at the 5% level of significance.
d. Compute the probability of a Type II error if the true number of chocolate chips per cookie is
8.
ANS:
a. H0: µ 9
Ha: µ < 9
b. test statistic t = -2.190 < -1.699; reject H0
c. reject H0; the p-value is between .01 to .025
d. A Type II error has not been committed since H0 was rejected.
PTS: 1 TOP: Hypothesis Tests
25. 23. A group of young businesswomen wish to open a high fashion boutique in a vacant store but only if
the average income of households in the area is at least $25,000. A random sample of 9 households
showed the following results.
$28,000 $24,000 $26,000 $25,000
$23,000 $27,000 $26,000 $22,000
$24,000
Assume the population of incomes is normally distributed.
a. Compute the sample mean and the standard deviation.
b. State the hypotheses for this problem.
c. Compute the test statistic.
d. At 95% confidence using the p-value approach, what is your conclusion?
ANS:
a. = 25,000 s = 1,936.49
b. H0: µ 25,000
Ha: µ < 25,000
c. test statistic t = 0
d. p-value = 0.5; do not reject H0, the boutique should be opened.
PTS: 1 TOP: Hypothesis Tests
24. Nancy believes that the average running time of movies is equal to 140 minutes. A sample of 4 movies
was taken and the following running times were obtained. Assume the population of the running times
is normally distributed.
150 150 180 170
a. Compute the sample mean and the standard deviation.
b. State the null and alternative hypotheses.
c. Using the critical value approach, test the hypotheses at the 10% level of significance.
d. Using the p-value approach, test the hypotheses at the 10% level of significance.
ANS:
a. = 162.5 s = 15
b. H0: µ = 140
Ha: µ ≠ 140
c. Reject H0; test statistic t = 3 > 2.353
d. The p-value is between .05 to .10; Reject H0
PTS: 1 TOP: Hypothesis Tests
25. A student believes that no more than 20% (i.e., 20%) of the students who finish a statistics course
get an A. A random sample of 100 students was taken. Twenty-four percent of the students in the
sample received A's.
a. State the null and alternative hypotheses.
b. Using the critical value approach, test the hypotheses at the 1% level of significance.
26. c. Using the p-value approach, test the hypotheses at the 1% level of significance.
ANS:
a. H0: P 0.2
Ha: P > 0.2
b. Do not reject H0; test statistic Z = 1 < 2.33
c. Do not reject H0; p-value = 0.1587 > 0.01
PTS: 1 TOP: Hypothesis Tests
26. An official of a large national union claims that the fraction of women in the union is not significantly
different from one-half. Using the critical value approach and the sample information reported below,
carry out a test of this statement. Let α = 0.05.
sample size 400
women 168
men 232
ANS:
H0: P = 0.5
Ha: P ≠ 0.5 Reject H0; test statistic Z = -3.2 < -1.96
PTS: 1 TOP: Hypothesis Tests
27. A law enforcement agent believes that at least 88% of the drivers stopped on Saturday nights for
speeding are under the influence of alcohol. A sample of 66 drivers who were stopped for speeding on
a Saturday night was taken. Eighty percent of the drivers in the sample were under the influence of
alcohol.
a. State the null and alternative hypotheses.
b. Compute the test statistic.
c. Using the p-value approach, test the hypotheses at the .05 level of significance.
ANS:
a. H0: P 0.88
Ha: P < 0.88
b. Z = -2
c. p-value = 0.0228 < 0.05; reject H0
PTS: 1 TOP: Hypothesis Tests
28. Two thousand numbers are selected randomly; 960 were even numbers.
a. State the hypotheses to determine whether the proportion of odd numbers is significantly
different from 50%.
b. Compute the test statistic.
c. At 90% confidence using the p-value approach, test the hypotheses.
ANS:
a. H0: P = 0.5
27. Ha: P ≠ 0.5
b. Z = 1.79
c. p-value = .0734 < 0.10; reject H0
PTS: 1 TOP: Hypothesis Tests
29. In the last presidential election, a national survey company claimed that no more than 50% (i.e., <
50%) of all registered voters voted for the Republican candidate. In a random sample of 400 registered
voters, 208 voted for the Republican candidate.
a. State the null and the alternative hypotheses.
b. Compute the test statistic.
c. At 95% confidence, compute the p-value and test the hypotheses.
ANS:
a. H0: P 0.5
Ha: P > 0.5
b. Z = 0.8
c. p-value = 0.2119 > 0.05; do not reject H0.
PTS: 1 TOP: Hypothesis Tests
30. An automobile manufacturer stated that it will be willing to mass produce electric-powered cars if
more than 30% of potential buyers indicate they will purchase the newly designed electric cars. In a
sample of 500 potential buyers, 160 indicated that they would buy such a product.
a. State the hypotheses for this problem
b. Compute the standard error of .
c. Compute the test statistic.
d. At 95% confidence, what is your conclusion? Should the manufacturer produce the new
electric powered car?
ANS:
a. H0: P 0.3
Ha: P > 0.3
b. 0.0205
c. Z = 0.98
d. p-value = 0.1635 <0.05; do not reject H0; no, the manufacturer should not produce the cars.
PTS: 1 TOP: Hypothesis Tests
31. It is said that more males register to vote in a national election than females. A research organization
selected a random sample of 300 registered voters and reported that 165 of the registered voters were
male.
a. Formulate the hypotheses for this problem.
b. Compute the standard error of .
c. Compute the test statistic.
d. Using the p-value approach, can you conclude that more males registered to vote than females?
Let α = .05.
28. ANS:
a. H0: P 0.5
Ha: P > 0.5
b. 0.0289
c. Z = 1.73
d. p-value = 0.0418 < .05; reject H0; yes, more males than females registered to vote.
PTS: 1 TOP: Hypothesis Tests
32. Consider the following hypothesis test:
Ho: µ = 10
Ha: µ ≠ 10
A sample of 81 provides a sample mean of 9.5 and a sample standard deviation of 1.8.
a. Determine the standard error of the mean.
b. Compute the value of the test statistic.
c. Determine the p-value; and at 95% confidence, test the above hypotheses.
ANS:
a. 0.2
b. t = -2.5
c. p-value is between .01 and .02 (two tail test); reject H0
PTS: 1 TOP: Hypothesis Tests
33. Consider the following hypothesis test:
Ho: µ 14
Ha: µ < 14
A sample of 64 provides a sample mean of 13 and a sample standard deviation of 4.
a. Determine the standard error of the mean.
b. Compute the value of the test statistic.
c. Determine the p-value; and at 95% confidence, test the above hypotheses.
ANS:
a. 0.5
b. t = -2
c. p-value is between .01 and .025; reject H0
PTS: 1 TOP: Hypothesis Tests
34. Consider the following hypothesis test:
Ho: µ 40
29. Ha: µ < 40
A sample of 49 provides a sample mean of 38 and a sample standard deviation of 7.
a. Determine the standard error of the mean.
b. Compute the value of the test statistic.
c. Determine the p-value; and at 95% confidence, test the above hypotheses.
ANS:
a. 1
b. t = -2
c. p-value is between .025 and .05; reject H0
PTS: 1 TOP: Hypothesis Tests
35. Consider the following hypothesis test:
Ho: µ 38
Ha: µ > 38
You are given the following information obtained from a random sample of six observations. Assume
the population has a normal distribution.
X
38
40
42
32
46
42
a. Compute the mean of the sample
b. Determine the standard deviation of the sample.
c. Determine the standard error of the mean.
d. Compute the value of the test statistic.
e. At 95% confidence using the p-value approach, test the above hypotheses.
ANS:
a. 40
b. 4.73
c. 1.93
d. 1.036
e. p-value is between 0.1 and 0.2; do not reject H0.
PTS: 1 TOP: Hypothesis Tests
36. Consider the following hypothesis test:
Ho: P 0.8
Ha: P > 0.8
30. A sample of 400 provided a sample proportion of 0.853.
a. Determine the standard error of the proportion.
b. Compute the value of the test statistic.
c. Determine the p-value; and at 95% confidence, test the above hypotheses.
ANS:
a. 0.02
b. Z = 2.65
c. p-value = 0.004; reject H0
PTS: 1 TOP: Hypothesis Tests
37. You are given the following information obtained from a random sample of 5 observations. Assume
the population has a normal distribution.
20 18 17 22 18
You want to determine whether or not the mean of the population from which this sample was taken is
significantly less than 21.
a. State the null and the alternative hypotheses.
b. Compute the standard error of the mean.
c. Determine the test statistic.
d. Determine the p-value and at 90% confidence, test whether or not the mean of the population is
significantly less than 21.
ANS:
a. H0: µ 21
Ha: µ < 21
b. 0.8944
c. t = -2.236
d. p-value is between .025 and .05; reject H0, the mean is significantly less than 21.
PTS: 1 TOP: Hypothesis Tests
38. Consider the following hypothesis test:
Ho: p = 0.5
Ha: p ≠ 0.5
A sample of 800 provided a sample proportion of 0.58.
a. Determine the standard error of the proportion.
b. Compute the value of the test statistic.
c. Determine the p-value, and at 95% confidence, test the hypotheses.
ANS:
a. 0.01768
b. Z = 4.53
31. c. p-value is almost zero; reject H0
PTS: 1 TOP: Hypothesis Tests
39. You are given the following information obtained from a random sample of 4 observations.
25 47 32 56
You want to determine whether or not the mean of the population from which this sample was taken is
significantly different from 48. (Assume the population is normally distributed.)
a. State the null and the alternative hypotheses.
b. Determine the test statistic.
c. Determine the p-value; and at 95% confidence test to determine whether or not the mean of the
population is significantly different from 48.
ANS:
a. H0: µ = 48
Ha: µ ≠ 48
b. t = -1.137
c. p-value is between 0.2 and 0.4 (two tailed); do not reject H0.
PTS: 1 TOP: Hypothesis Tests
40. Confirmed cases of West Nile virus in birds for a sample of six counties in the state of Georgia are
shown below.
County Cases
Catoosa 6
Chattooga 3
Dade 3
Gordon 5
Murray 3
Walker 4
We are interested in testing the following hypotheses regarding these data:
H0: µ 3
Ha: µ > 3
a. Compute the mean and the standard deviation of the sample.
b. Compute the standard error of the mean.
c. Determine the test statistic.
d. Determine the p-value and at 95% confidence, test the hypotheses.
ANS:
a. 4 and 1.265 (rounded)
b. 0.5164
c. t = 1.94 (rounded)
d. p-value is between 0.05 and 0.1; reject H0 and conclude that the mean of the population is
significantly more than 3.
32. PTS: 1 TOP: Hypothesis Tests
41. A sample of 64 account balances from a credit company showed an average daily balance of $1,040.
The standard deviation of the population is known to be $200. We are interested in determining if the
mean of all account balances (i.e., population mean) is significantly different from $1,000.
a. Develop the appropriate hypotheses for this problem.
b. Compute the test statistic.
c. Compute the p-value.
d. Using the p-value approach at 95% confidence, test the above hypotheses.
e. Using the critical value approach at 95% confidence, test the hypotheses.
ANS:
a. Ho: µ = 1000
Ha: µ ≠ 1000
b. 1.60
c. 0.1096
d. The p-value = 0.1096, which is larger than α = 0.05 (95% confidence). Hence, the null
hypothesis is not rejected; and we conclude that there is not sufficient evidence to indicate
that the advertising campaigns have been effective.
e. In Part b, the Z statistic was computed and its value was 1.60. Since 1.60 is between -1.96
and 1.96, the null hypothesis cannot be rejected; and we conclude that there is no evidence
that the mean is significantly different from $1,000.
PTS: 1 TOP: Hypothesis Tests
42. Consider the following hypotheses test.
Ho: µ ≥ 80
Ha: µ < 80
A sample of 121 provided a sample mean of 77.3. The population standard deviation is known to be
16.5.
a. Compute the value of the test statistic.
b. Determine the p-value; and at 93.7% confidence, test the above hypotheses.
c. Using the critical value approach at 93.7% confidence, test the hypotheses.
ANS:
a. Z = -1.8
b. p-value = 0.0359 < 0.063, reject Ho
c. test statistic Z = -1.8 < Z.063 = -1.53, reject Ho
PTS: 1 TOP: Hypothesis Tests
43. Automobiles manufactured by the Efficiency Company have been averaging 42 miles per gallon of
gasoline in highway driving. It is believed that its new automobiles average more than 42 miles per
gallon. An independent testing service road-tested 36 of the automobiles. The sample showed an
average of 42.8 miles per gallon with a standard deviation of 1.2 miles per gallon.
a. With a 0.05 level of significance using the critical value approach, test to determine whether
or not the new automobiles actually do average more than 42 miles per gallon.
33. b. What is the p-value associated with the sample results? What is your conclusion based on
the p-value?
ANS:
a. Ho: µ < 42
Ha: µ > 42
Since t = 4.0 > 1.690, reject Ho and conclude that the new cars average more than 42 miles
per gallon.
b. p-value < 0.005, therefore reject Ho (area to the right of t = 4.0 is almost zero)
PTS: 1 TOP: Hypothesis Tests
44. The average starting salary of students who graduated from colleges of Business in 2009 was $48,400.
A sample of 100 graduates of 2010 showed an average starting salary of $50,000. Assume the
standard deviation of the population is known to be $8,000. We want to determine whether or not
there has been a significant increase in the starting salaries.
a. State the null and alternative hypotheses to be tested.
b. Compute the test statistic.
c. The null hypothesis is to be tested at 95% confidence. Determine the critical value for this
test.
d. What do you conclude?
e. Compute the p-value.
ANS:
a. Ho: µ ≤ 48,400
Ha : µ > 48,400
b. Test Statistic Z = 2.0
c. Critical Z = 1.64
d. Reject Ho and conclude that there has been a significant increase.
e. P-value = .0228
PTS: 1 TOP: Hypothesis Testing
45. The average price of homes sold in the U.S. in the past year was $220,000. A random sample of 81
homes sold this year showed an average price of $210,000. It is known that the standard deviation of
the population is $36,000. At 95% confidence test to determine if there has been a significant decrease
in the average price homes.
a. State the null and alternative hypotheses to be tested.
b. Compute the test statistic.
c. Determine the critical value for this test.
d. What do you conclude?
e. Compute the p-value.
ANS:
a. Ho: µ ≥ 220,000
Ha : µ < 220,000
b. Test statistic Z = -2.5
c. Critical Z = -1.64
d. Reject Ho and conclude that there has been a significant decrease.
e. P-value = 0.0062