The document presents results from a statistical analysis of responses to a survey on sexual ideologies administered to BPHS students. It includes confidence intervals and hypothesis tests computed for various survey questions. For questions on age, grade level, number of sexual partners and frequency of sex acts, the analyses found no significant differences between male and female respondents. However, males reported significantly more sexual partners and higher frequency of thinking about sex than females. Tests also indicated responses on abortion views and number of sexual partners varied with grade level.
Lecture on causal inference to the pediatric hematology/oncology fellows at Texas Children's hospital as part of their Biostatistics for Busy Clinicians lecture seriers.
1. The document discusses hypothesis testing methodology and various hypothesis testing processes. It covers topics like the null and alternative hypotheses, type 1 and type 2 errors, and significance levels.
2. Several examples of hypothesis testing are provided, including testing means using z-tests and t-tests, and testing proportions using z-tests. The steps of hypothesis testing are outlined.
3. Factors that affect the probability of type 2 errors are discussed, such as the significance level, population standard deviation, and sample size.
Psychologists use the scientific method to construct testable theories about human behavior and mental processes. This involves making careful observations, developing hypotheses and operational definitions, conducting experiments and studies while controlling for biases and alternative explanations, analyzing results statistically, and replicating findings. The goal is to gain objective knowledge about psychological phenomena while recognizing the limitations of any particular study or perspective.
Psychologists use the scientific method to construct testable theories about human behavior and mental processes. This involves making observations, developing hypotheses and theories, conducting experiments, and drawing conclusions. Critical thinking is important in psychology to avoid biases, examine assumptions, and evaluate evidence rather than blindly accepting arguments. Theories are explained using principles and predict observations, while hypotheses make testable predictions.
The document discusses a one-sample t-test used to compare sample data to a standard value. It provides an example comparing intelligence scores of university students to the average score of 100. The sample of 6 students had a mean of 120. Running a one-tailed t-test in SPSS, the results showed the mean score was significantly higher than 100 with t(5)=3.15, p=.02. This allows the inference that the population mean intelligence at the university is greater than the standard score of 100.
The document provides an overview of hypothesis testing, including defining the null and alternative hypotheses, types of errors, significance levels, critical values, test statistics, and conducting hypothesis tests using both the traditional and p-value methods. Examples are provided for z-tests, t-tests, and tests of proportions to demonstrate applications of hypothesis testing methodology.
1. The document discusses hypothesis testing using the z-test. It outlines the steps of hypothesis testing including stating hypotheses, setting the criterion, computing test statistics, comparing to the criterion, and making a decision.
2. Examples are provided to demonstrate a non-directional and directional z-test, including stating hypotheses, computing test statistics, comparing to criteria, and interpreting results.
3. Key concepts reviewed are the central limit theorem, type I and II errors, significance levels, rejection regions, p-values, and confidence intervals in hypothesis testing.
Lecture on causal inference to the pediatric hematology/oncology fellows at Texas Children's hospital as part of their Biostatistics for Busy Clinicians lecture seriers.
1. The document discusses hypothesis testing methodology and various hypothesis testing processes. It covers topics like the null and alternative hypotheses, type 1 and type 2 errors, and significance levels.
2. Several examples of hypothesis testing are provided, including testing means using z-tests and t-tests, and testing proportions using z-tests. The steps of hypothesis testing are outlined.
3. Factors that affect the probability of type 2 errors are discussed, such as the significance level, population standard deviation, and sample size.
Psychologists use the scientific method to construct testable theories about human behavior and mental processes. This involves making careful observations, developing hypotheses and operational definitions, conducting experiments and studies while controlling for biases and alternative explanations, analyzing results statistically, and replicating findings. The goal is to gain objective knowledge about psychological phenomena while recognizing the limitations of any particular study or perspective.
Psychologists use the scientific method to construct testable theories about human behavior and mental processes. This involves making observations, developing hypotheses and theories, conducting experiments, and drawing conclusions. Critical thinking is important in psychology to avoid biases, examine assumptions, and evaluate evidence rather than blindly accepting arguments. Theories are explained using principles and predict observations, while hypotheses make testable predictions.
The document discusses a one-sample t-test used to compare sample data to a standard value. It provides an example comparing intelligence scores of university students to the average score of 100. The sample of 6 students had a mean of 120. Running a one-tailed t-test in SPSS, the results showed the mean score was significantly higher than 100 with t(5)=3.15, p=.02. This allows the inference that the population mean intelligence at the university is greater than the standard score of 100.
The document provides an overview of hypothesis testing, including defining the null and alternative hypotheses, types of errors, significance levels, critical values, test statistics, and conducting hypothesis tests using both the traditional and p-value methods. Examples are provided for z-tests, t-tests, and tests of proportions to demonstrate applications of hypothesis testing methodology.
1. The document discusses hypothesis testing using the z-test. It outlines the steps of hypothesis testing including stating hypotheses, setting the criterion, computing test statistics, comparing to the criterion, and making a decision.
2. Examples are provided to demonstrate a non-directional and directional z-test, including stating hypotheses, computing test statistics, comparing to criteria, and interpreting results.
3. Key concepts reviewed are the central limit theorem, type I and II errors, significance levels, rejection regions, p-values, and confidence intervals in hypothesis testing.
The document provides an overview of hypothesis testing. It begins by defining a hypothesis test and its purpose of ruling out chance as an explanation for research study results. It then outlines the logic and steps of a hypothesis test: 1) stating hypotheses, 2) setting decision criteria, 3) collecting data, 4) making a decision. Key concepts discussed include type I and type II errors, statistical significance, test statistics like the z-score, and assumptions of hypothesis testing. Factors that can influence a hypothesis test like effect size, sample size, and alpha level are also covered.
Tests of significance are statistical methods used to assess evidence for or against claims based on sample data about a population. Every test of significance involves a null hypothesis (H0) and an alternative hypothesis (Ha). H0 represents the theory being tested, while Ha represents what would be concluded if H0 is rejected. A test statistic is computed and compared to a critical value to either reject or fail to reject H0. Type I and Type II errors can occur. Steps in hypothesis testing include stating hypotheses, selecting a significance level and test, determining decision rules, computing statistics, and interpreting the decision. Hypothesis tests are used to answer questions about differences in groups or claims about populations.
Crime Analysis using Regression and ANOVATom Donoghue
A statistical analysis of damage to property using a predictive regression model. Also an investigation to ascertain possible differences in reported divisional burglary rates using ANOVA.
D. G. Mayo (Virginia Tech) "Error Statistical Control: Forfeit at your Peril" presented May 23 at the session on "The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference," 2015 APS Annual Convention in NYC.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and analyzing the results. The four steps of hypothesis testing are: 1) stating the null and alternative hypotheses, 2) setting the significance level typically at 5%, 3) computing a test statistic to quantify how unlikely the sample results would be if the null was true, and 4) making a decision to either reject or fail to reject the null hypothesis based on comparing the test statistic to the significance level. The goal is to systematically evaluate whether a hypothesized population parameter, such as a mean, is likely to be true based on the sample results.
This document outlines the process of hypothesis testing. It begins with defining key terms like the null hypothesis (H0), alternative hypothesis (H1), significance level, test statistic, critical value, and decision rule. It then explains the steps involved: 1) setting up H0 and H1, 2) choosing a significance level, 3) calculating the test statistic, 4) finding the critical value, and 5) making a decision by comparing the test statistic and critical value. The overall goal of hypothesis testing is to evaluate claims about a population parameter based on a sample's data.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 4: Probability
4.3: Complements and Conditional Probability, and Bayes' Theorem
This presentation is designed for students of Ouachita Baptist University's Summer II online course "The Contemporary World" for 2013. It treats conceptual continuities among various religious traditions, and discusses the details (as we know them) of the growing American surveillance state post-9/11.
The document discusses classified advertising websites. It provides the top 5 classified websites for India, the US & UK, Australia, and Canada. It also discusses what classified ads are and how Oodle is the largest classified aggregator, aggregating listings from sites like eBay and newspapers.
This document summarizes a presentation on permaculture, polyculture, community, and cooperation. It discusses exploitative social patterns like state capitalism being analogous to army ant raiding patterns. It presents stable social patterns exemplified by Ebenezer Howard's Garden Cities. It also discusses the importance of local economies, cooperation, commonwork involving multiple livelihoods, and community.
List of best ping websites and doc sharingSushen Jamwal
Pinging a website involves notifying search engines and directories of updates to prompt faster indexing. It works by sending a message telling search bots new content is available. While pinging can speed indexing, it should only be done when new content is added, not excessively, to avoid being blocked. Popular pinging sites include Pingomatic and PingMyBlog. Document sharing sites also help increase inbound links when related documents with website links are uploaded, as they attract targeted traffic and build trust and expertise. Top document sharing sites are Scribd, DocStoc, and SlideShare.
Search engine optimization seo, smo by sushen jamwalSushen Jamwal
This document provides an overview of search engine optimization (SEO). It begins with defining search engines and how they work by crawling websites, indexing content, and ranking pages in search results. It then defines SEO and explains why it is important for websites. The document outlines key SEO terms and strategies including keywords, links, titles, descriptions. It also discusses limitations of search engines in indexing content and recommendations for good SEO practices versus poor practices.
The document analyzes statistics from a survey administered to Baldwin Park High School students. It provides confidence intervals for survey questions asking about relationships, beliefs, and preferences. Hypothesis tests are conducted to compare survey results to larger studies and compare responses between genders. Chi-square tests determine whether certain opinions vary with age. While some opinions differed significantly between genders or with prior studies, age was found to not influence most beliefs and behaviors examined.
This document provides examples and explanations of statistical inference and constructing confidence intervals. It discusses two simple examples: a lady tasting tea and detecting human energy fields. It then explains how to calculate probabilities of these events occurring by chance and use them to assess abilities. The document also covers calculating standard errors and using them to construct confidence intervals for means, proportions, differences in means, and differences in proportions. Examples are provided for estimating population parameters from sample data, including average family income, university tuitions, and presidential approval ratings.
The document provides an overview of hypothesis testing using t-tests, including the steps, assumptions, and examples of single sample, paired sample, and independent samples t-tests. It discusses key concepts like the null and alternative hypotheses, characteristics of the comparison distribution, determining critical values, calculating the test statistic, and making a decision. Examples are provided to demonstrate how to conduct and report the results of each type of t-test.
4 1 probability and discrete probability distributionsLama K Banna
This document discusses probabilities and probability distributions. It begins by defining an experiment and sample space. A random variable is defined as a numerical value determined by the outcome of an experiment. Random variables can be discrete or continuous. Probability distributions show all possible outcomes of an experiment and their probabilities. The binomial distribution is discussed as modeling discrete experiments with binary outcomes and fixed probabilities. Key properties of the binomial include the mean, variance, and use of the binomial probability formula and tables to calculate probabilities of various outcomes.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject or fail to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to either reject or fail to reject the null hypothesis.
Chapter8 introduction to hypothesis testingBOmebratu
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
This document contain all topics of research methodology of module-3 according to the syllabus of BPUT odisha. The document is done for the PG and PHD students who are doing research.
1. You are conducting a study to see if the probability of a true ne.docxcarlstromcurtis
1. You are conducting a study to see if the probability of a true negative on a test for a certain cancer is significantly more than 0.25.
With
H
1 : p >> 0.25 you obtain a test statistic of z=1.397z=1.397.
Use a normal distribution calculator and the test statistic to find the P-value accurate to 4 decimal places. It may be left-tailed, right-tailed, or 2-tailed.
P-value =
2. You are conducting a study to see if the probability of catching the flu this year is significantly more than 0.27.
With
H
1 : p >> 0.27 you obtain a test statistic of z=1.722z=1.722.
Use a normal distribution calculator and the test statistic to find the P-value accurate to 4 decimal places. It may be left-tailed, right-tailed, or 2-tailed.
P-value =
3. You are conducting a study to see if the probability of a true negative on a test for a certain cancer is significantly more than 0.81. You use a significance level of α=0.001α=0.001.
H0:p=0.81H0:p=0.81
H1:p>0.81H1:p>0.81
You obtain a sample of size n=218n=218 in which there are 184 successes.
What is the test statistic for this sample? (Report answer accurate to three decimal places.)
test statistic =
What is the p-value for this sample? (Report answer accurate to four decimal places.)
p-value =
The p-value is...
a) less than (or equal to) αα
b) greater than αα
This test statistic leads to a decision to...
a) reject the null
b) accept the null
c) fail to reject the null
As such, the final conclusion is that...
a) There is sufficient evidence to warrant rejection of the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
b)There is not sufficient evidence to warrant rejection of the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
c)The sample data support the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
d)There is not sufficient sample evidence to support the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
4. You are conducting a study to see if the proportion of men over 50 who regularly have their prostate examined is significantly different from 0.23. You use a significance level of α=0.02α=0.02.
H0:p=0.23H0:p=0.23
H1:p≠0.23H1:p≠0.23
You obtain a sample of size n=167n=167 in which there are 32 successes.
What is the test statistic for this sample? (Report answer accurate to three decimal places.)
test statistic =
What is the p-value for this sample? (Report answer accurate to four decimal places.)
p-value =
The p-value is...
A) less than (or equal to) αα
B) greater than αα
This test statistic leads to a decision to...
A)reject the null
B)accept the null
C)fail to reject the null
As such, the final conclusion is that...
A) There is sufficient evidence to warrant rejection of the claim that the proportion of men over 50 who regularly have their prostate .
The document provides an overview of hypothesis testing. It begins by defining a hypothesis test and its purpose of ruling out chance as an explanation for research study results. It then outlines the logic and steps of a hypothesis test: 1) stating hypotheses, 2) setting decision criteria, 3) collecting data, 4) making a decision. Key concepts discussed include type I and type II errors, statistical significance, test statistics like the z-score, and assumptions of hypothesis testing. Factors that can influence a hypothesis test like effect size, sample size, and alpha level are also covered.
Tests of significance are statistical methods used to assess evidence for or against claims based on sample data about a population. Every test of significance involves a null hypothesis (H0) and an alternative hypothesis (Ha). H0 represents the theory being tested, while Ha represents what would be concluded if H0 is rejected. A test statistic is computed and compared to a critical value to either reject or fail to reject H0. Type I and Type II errors can occur. Steps in hypothesis testing include stating hypotheses, selecting a significance level and test, determining decision rules, computing statistics, and interpreting the decision. Hypothesis tests are used to answer questions about differences in groups or claims about populations.
Crime Analysis using Regression and ANOVATom Donoghue
A statistical analysis of damage to property using a predictive regression model. Also an investigation to ascertain possible differences in reported divisional burglary rates using ANOVA.
D. G. Mayo (Virginia Tech) "Error Statistical Control: Forfeit at your Peril" presented May 23 at the session on "The Philosophy of Statistics: Bayesianism, Frequentism and the Nature of Inference," 2015 APS Annual Convention in NYC.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and analyzing the results. The four steps of hypothesis testing are: 1) stating the null and alternative hypotheses, 2) setting the significance level typically at 5%, 3) computing a test statistic to quantify how unlikely the sample results would be if the null was true, and 4) making a decision to either reject or fail to reject the null hypothesis based on comparing the test statistic to the significance level. The goal is to systematically evaluate whether a hypothesized population parameter, such as a mean, is likely to be true based on the sample results.
This document outlines the process of hypothesis testing. It begins with defining key terms like the null hypothesis (H0), alternative hypothesis (H1), significance level, test statistic, critical value, and decision rule. It then explains the steps involved: 1) setting up H0 and H1, 2) choosing a significance level, 3) calculating the test statistic, 4) finding the critical value, and 5) making a decision by comparing the test statistic and critical value. The overall goal of hypothesis testing is to evaluate claims about a population parameter based on a sample's data.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 4: Probability
4.3: Complements and Conditional Probability, and Bayes' Theorem
This presentation is designed for students of Ouachita Baptist University's Summer II online course "The Contemporary World" for 2013. It treats conceptual continuities among various religious traditions, and discusses the details (as we know them) of the growing American surveillance state post-9/11.
The document discusses classified advertising websites. It provides the top 5 classified websites for India, the US & UK, Australia, and Canada. It also discusses what classified ads are and how Oodle is the largest classified aggregator, aggregating listings from sites like eBay and newspapers.
This document summarizes a presentation on permaculture, polyculture, community, and cooperation. It discusses exploitative social patterns like state capitalism being analogous to army ant raiding patterns. It presents stable social patterns exemplified by Ebenezer Howard's Garden Cities. It also discusses the importance of local economies, cooperation, commonwork involving multiple livelihoods, and community.
List of best ping websites and doc sharingSushen Jamwal
Pinging a website involves notifying search engines and directories of updates to prompt faster indexing. It works by sending a message telling search bots new content is available. While pinging can speed indexing, it should only be done when new content is added, not excessively, to avoid being blocked. Popular pinging sites include Pingomatic and PingMyBlog. Document sharing sites also help increase inbound links when related documents with website links are uploaded, as they attract targeted traffic and build trust and expertise. Top document sharing sites are Scribd, DocStoc, and SlideShare.
Search engine optimization seo, smo by sushen jamwalSushen Jamwal
This document provides an overview of search engine optimization (SEO). It begins with defining search engines and how they work by crawling websites, indexing content, and ranking pages in search results. It then defines SEO and explains why it is important for websites. The document outlines key SEO terms and strategies including keywords, links, titles, descriptions. It also discusses limitations of search engines in indexing content and recommendations for good SEO practices versus poor practices.
The document analyzes statistics from a survey administered to Baldwin Park High School students. It provides confidence intervals for survey questions asking about relationships, beliefs, and preferences. Hypothesis tests are conducted to compare survey results to larger studies and compare responses between genders. Chi-square tests determine whether certain opinions vary with age. While some opinions differed significantly between genders or with prior studies, age was found to not influence most beliefs and behaviors examined.
This document provides examples and explanations of statistical inference and constructing confidence intervals. It discusses two simple examples: a lady tasting tea and detecting human energy fields. It then explains how to calculate probabilities of these events occurring by chance and use them to assess abilities. The document also covers calculating standard errors and using them to construct confidence intervals for means, proportions, differences in means, and differences in proportions. Examples are provided for estimating population parameters from sample data, including average family income, university tuitions, and presidential approval ratings.
The document provides an overview of hypothesis testing using t-tests, including the steps, assumptions, and examples of single sample, paired sample, and independent samples t-tests. It discusses key concepts like the null and alternative hypotheses, characteristics of the comparison distribution, determining critical values, calculating the test statistic, and making a decision. Examples are provided to demonstrate how to conduct and report the results of each type of t-test.
4 1 probability and discrete probability distributionsLama K Banna
This document discusses probabilities and probability distributions. It begins by defining an experiment and sample space. A random variable is defined as a numerical value determined by the outcome of an experiment. Random variables can be discrete or continuous. Probability distributions show all possible outcomes of an experiment and their probabilities. The binomial distribution is discussed as modeling discrete experiments with binary outcomes and fixed probabilities. Key properties of the binomial include the mean, variance, and use of the binomial probability formula and tables to calculate probabilities of various outcomes.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject or fail to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to either reject or fail to reject the null hypothesis.
Chapter8 introduction to hypothesis testingBOmebratu
Researchers use hypothesis testing to evaluate claims about populations by taking samples and comparing sample statistics to hypothesized population parameters. The four steps of hypothesis testing are:
1) State the null and alternative hypotheses, with the null hypothesis presuming the claim is true.
2) Set criteria for deciding whether to reject the null hypothesis.
3) Calculate a test statistic from the sample.
4) Compare the test statistic to the criteria to determine whether to reject the null hypothesis.
This document contain all topics of research methodology of module-3 according to the syllabus of BPUT odisha. The document is done for the PG and PHD students who are doing research.
1. You are conducting a study to see if the probability of a true ne.docxcarlstromcurtis
1. You are conducting a study to see if the probability of a true negative on a test for a certain cancer is significantly more than 0.25.
With
H
1 : p >> 0.25 you obtain a test statistic of z=1.397z=1.397.
Use a normal distribution calculator and the test statistic to find the P-value accurate to 4 decimal places. It may be left-tailed, right-tailed, or 2-tailed.
P-value =
2. You are conducting a study to see if the probability of catching the flu this year is significantly more than 0.27.
With
H
1 : p >> 0.27 you obtain a test statistic of z=1.722z=1.722.
Use a normal distribution calculator and the test statistic to find the P-value accurate to 4 decimal places. It may be left-tailed, right-tailed, or 2-tailed.
P-value =
3. You are conducting a study to see if the probability of a true negative on a test for a certain cancer is significantly more than 0.81. You use a significance level of α=0.001α=0.001.
H0:p=0.81H0:p=0.81
H1:p>0.81H1:p>0.81
You obtain a sample of size n=218n=218 in which there are 184 successes.
What is the test statistic for this sample? (Report answer accurate to three decimal places.)
test statistic =
What is the p-value for this sample? (Report answer accurate to four decimal places.)
p-value =
The p-value is...
a) less than (or equal to) αα
b) greater than αα
This test statistic leads to a decision to...
a) reject the null
b) accept the null
c) fail to reject the null
As such, the final conclusion is that...
a) There is sufficient evidence to warrant rejection of the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
b)There is not sufficient evidence to warrant rejection of the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
c)The sample data support the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
d)There is not sufficient sample evidence to support the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
4. You are conducting a study to see if the proportion of men over 50 who regularly have their prostate examined is significantly different from 0.23. You use a significance level of α=0.02α=0.02.
H0:p=0.23H0:p=0.23
H1:p≠0.23H1:p≠0.23
You obtain a sample of size n=167n=167 in which there are 32 successes.
What is the test statistic for this sample? (Report answer accurate to three decimal places.)
test statistic =
What is the p-value for this sample? (Report answer accurate to four decimal places.)
p-value =
The p-value is...
A) less than (or equal to) αα
B) greater than αα
This test statistic leads to a decision to...
A)reject the null
B)accept the null
C)fail to reject the null
As such, the final conclusion is that...
A) There is sufficient evidence to warrant rejection of the claim that the proportion of men over 50 who regularly have their prostate .
Statsmath1. You are conducting a study to see if the probabi.docxrafaelaj1
Stats
math
1. You are conducting a study to see if the probability of a true negative on a test for a certain cancer is significantly more than 0.25.
With
H
1 : p >> 0.25 you obtain a test statistic of z=1.397z=1.397.
Use a normal distribution calculator and the test statistic to find the P-value accurate to 4 decimal places. It may be left-tailed, right-tailed, or 2-tailed.
P-value =
2. You are conducting a study to see if the probability of catching the flu this year is significantly more than 0.27.
With
H
1 : p >> 0.27 you obtain a test statistic of z=1.722z=1.722.
Use a normal distribution calculator and the test statistic to find the P-value accurate to 4 decimal places. It may be left-tailed, right-tailed, or 2-tailed.
P-value =
3. You are conducting a study to see if the probability of a true negative on a test for a certain cancer is significantly more than 0.81. You use a significance level of α=0.001α=0.001.
H0:p=0.81H0:p=0.81
H1:p>0.81H1:p>0.81
You obtain a sample of size n=218n=218 in which there are 184 successes.
What is the test statistic for this sample? (Report answer accurate to three decimal places.)
test statistic =
What is the p-value for this sample? (Report answer accurate to four decimal places.)
p-value =
The p-value is...
a) less than (or equal to) αα
b) greater than αα
This test statistic leads to a decision to...
a) reject the null
b) accept the null
c) fail to reject the null
As such, the final conclusion is that...
a) There is sufficient evidence to warrant rejection of the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
b)There is not sufficient evidence to warrant rejection of the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
c)The sample data support the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
d)There is not sufficient sample evidence to support the claim that the probability of a true negative on a test for a certain cancer is more than 0.81.
4. You are conducting a study to see if the proportion of men over 50 who regularly have their prostate examined is significantly different from 0.23. You use a significance level of α=0.02α=0.02.
H0:p=0.23H0:p=0.23
H1:p≠0.23H1:p≠0.23
You obtain a sample of size n=167n=167 in which there are 32 successes.
What is the test statistic for this sample? (Report answer accurate to three decimal places.)
test statistic =
What is the p-value for this sample? (Report answer accurate to four decimal places.)
p-value =
The p-value is...
A) less than (or equal to) αα
B) greater than αα
This test statistic leads to a decision to...
A)reject the null
B)accept the null
C)fail to reject the null
As such, the final conclusion is that...
A) There is sufficient evidence to warrant rejection of the claim that the proportion of men over 50 who regularly have their pros.
1) The chi-square test is a nonparametric test used to analyze categorical data when assumptions of parametric tests are violated. It compares observed frequencies to expected frequencies specified by the null hypothesis.
2) The chi-square test can test for goodness of fit, evaluating if sample proportions match population proportions. It can also test independence, assessing relationships between two categorical variables.
3) To perform the test, observed and expected frequencies are calculated and entered into the chi-square formula. The resulting statistic is compared to critical values of the chi-square distribution to determine significance.
The four steps of hypothesis testing are:
1. State the null and alternative hypotheses. The null hypothesis assumes the claim is true while the alternative contradicts it.
2. Set the significance level, typically 5%, which is the probability of a Type I error of rejecting a true null hypothesis.
3. Compute the test statistic to determine how far the sample mean is from the population mean stated in the null hypothesis.
4. Make a decision by comparing the p-value to the significance level. If p < 0.05, reject the null hypothesis.
The document discusses normal and standard normal distributions. It provides examples of using a normal distribution to calculate probabilities related to bone mineral density test results. It shows how to find the probability of a z-score falling below or above certain values. It also explains how to determine the sample size needed to estimate an unknown population proportion within a given level of confidence.
The document provides information about goodness-of-fit tests and contingency tables. It defines a goodness-of-fit test as testing whether an observed frequency distribution fits a claimed distribution. It also provides the notation, requirements, and steps to conduct a goodness-of-fit test including: defining the null and alternative hypotheses, calculating the test statistic as a chi-square value, finding the critical value, and making a decision to reject or fail to reject the null hypothesis. Several examples demonstrate how to perform goodness-of-fit tests to determine if sample data fits a claimed distribution.
A chi-squared test (χ2) is basically a data analysis on the basis of observations of a random set of variables. Usually, it is a comparison of two statistical data sets. This test was introduced by Karl Pearson in 1900 for categorical data analysis and distribution. So, it was mentioned as Pearson’s chi-squared test.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey
Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
This document describes the results of a statistical survey project conducted by Jonathan Peñate and Arnold Gonzalez. It includes the survey questions, sample sizes, means, standard deviations, and confidence intervals calculated for various survey questions. It also includes hypothesis tests comparing results to larger studies and testing for differences in responses between groups. The confidence intervals and hypothesis tests indicate there is no strong evidence of differences in the means or proportions compared.
The quiz has two portions Multiple Choice (8 problems, 32 p.docxhelen23456789
The quiz has two portions:
Multiple Choice
(8 problems, 32 points).
Show work/explanation as appropriate
.
Short Answer
(3 problems, 38 points)
Show work
.
MULTIPLE CHOICE
. Choose the one alternative that best completes the statement or answers the question.
(
4 points
) If the P-value of a hypothesis test comparing two means was 0.25, what can you conclude? (Select all that apply):
A. You can accept the null hypothesis
B. There was a significant difference between the means
C. You failed to reject the null hypothesis
D. There did not appear to be significant difference between the means
(
4 points
) Imagine a researcher wanted to test the effect of the new drug on reducing blood pressure. In this study, there were 50 participants. The researcher measured the participants’ blood pressure before and after the drug intake. If we want to compare the mean blood pressure from the two time periods with a two-tailed t test, how many degrees of freedom are there?
A. 49
B. 50
C. 99
D. 100
(
4 points
) When sample size increases, ____
A. Power increases a great degree at first, reaches its peak, and then slowly decreases
B. Power decreases a great degree at first, reaches its lowest point, and then slowly increases
C. Power increases a great degree at first, and then increases slowly
D. Power decreases a great degree at first, and then decreases slowly
(
4 points
) α=0.05 for a two-tailed test. Assume that the data has a normal distribution and the number of observations is greater than fifty. Find the critical z value used to test a null hypothesis.
A. ±1.768
B. ±1.764
C. ±1.96
D. ±2.575
(
4 points
) In a sample of 47 adults selected randomly from one town, it is found that 9 of them have been exposed to a particular strain of the flu. Find the P-value for a test of the claim that the proportion of all adults in the town that have been exposed to this strain of the flu is 8%.
A. 0.0024
B. 0.0524
C. 0.0228
D. 0.0048
(
4 points
) For a simple random sample, the size is n=17, σ is not known, and the original population is normally distributed. Determine whether the give conditions justify testing a claim about a population mean µ.
A. Yes
B. No
(
4 points
) A medical researcher claims that 20% of children suffer from a certain disorder. Indentify the type I error for the test.
A. Fail to reject the claim that the percentage of children who suffer from the disorder is equal to 20% when the percentage is actually 20%.
B. Reject the claim that the percentage of children who suffer from the disorder is equal to 20% when that percentage is actually 20%.
C. Fail to reject the claim that the percentage of children who suffer from the disorder is equal to 20% when that percentage is actually different from 20%.
D.Reject the claim that the percentage of children who suffer from the disorder is different from 20% when that percentage really is different f.
1) The document discusses how biologists use statistical hypothesis testing and data analysis to conduct research. It provides an example of a conservation biologist hypothesizing the sex ratio of black rhinos is 1:1 male to female.
2) To test this, the biologist collects a sample of 300 rhinos during an aerial survey, finding 133 males and 167 females. This differs from the expected 1:1 ratio under the null hypothesis.
3) The chi-square goodness-of-fit test is used to analyze if the observed sample values are significantly different than the expected values under the null hypothesis. For this example, the chi-square test statistic exceeds the critical value, so the null hypothesis of a 1:1
Similar to Sexual ideologies of bphs students (1) (20)
1. Statistical Analyses of the Sexual Ideologies of BPHS Students Cristian Castillo George Damian Steven Do
2. Confidence Intervals for Means Question 2: Age X = 16.80, n = 87, df = 86, t* = 1.663, s = 0.696 16.80 ± 1.663 * (0.696/(sqrt87)) = (16.652, 16.948) We are 95% confident that the true mean age of survey participants is between 16.652 and 16.948 years.
3. Confidence Intervals for Means Question 3: Grade Level X = 11.43, n = 83, df = 82, t* = 1.664, s = 1.270 11.43 ± 1.664 * (1.270/(sqrt83)) = (11.153, 11.707) We are 95% confident that the true mean grade level of survey participants is between 11.153 and 11.707.
4. Confidence Intervals for Means Question 4: Number of times engaged in unprotected sex (in the past year) X = 1.012, n = 83, df= 82, t* = 1.664, s = 2.652 1.012 ± 1.664 * (2.652/(sqrt83)) = (.4329 , 1.591) We are 95% confident that the true mean of the number of times a survey participant engaged in unprotected sex in the past year is between .4329 and 1.591.
5. Confidence Intervals for Means Question 5: Number of sexual partners in the past year X = 1, n = 83, df = 82, t* = 1.664, s = 1.807 1 ± 1.664 * (1.807/(sqrt83)) = (.6054, 1.395) We are 95% confident that the true mean number of sexual partners that a survey participant has had in the past year is between .6054 and 1.395.
6. Confidence Intervals for Means Question 6: Number of times in a day where one thinks about sex X = 3.104, n = 67, df= 66, t* = 1.668, s = 5.216 3.104 ± 1.668 * (5.216/(sqrt67)) = (1.831, 4.376) We are 95% confident that the true mean number of times in a day when a survey participant thinks about sex is between 1.831 and 4.376.
7. Confidence Intervals for Proportions Question 1: Gender p = 0.414, q = 0.586, z* = 1.960, n = 83 0.414 ± 1.960 * sqrt[(.414)(.586)/(87)] = (0.310, 0.517) We are 95% confident that the true proportion of affirmative (male) participants is between .310 and .517.
8. Confidence Intervals for Proportions Question 7: Abortion p = 0.409, q = 0.591, z* = 1.960, n = 83 0.409 ± 1.960 * sqrt[(.409)(.591)/(83)] = (0.303, 0.515) We are 95% confident that the true proportion of affirmative responses (agreement to the method of abortion) is between .303 and .515.
9. Confidence Intervals for Proportions Question 8: Abstinence p = 0.479, q = 0.521, z* = 1.960, n = 73 0.479 ± 1.960 * sqrt[(.479)(.521)/(73)] =(0.364, 0.594) We are 95% confident that the true proportion of affirmative responses (for the concept of abstinence) is between .364 and ,594.
10. Confidence Intervals for Proportions Question 9: Adoption if one cannot raise a newborn p = 0.90, q = 0.10, z* = 1.960, n = 80 0.90 ± 1.960 * sqrt[(.90)(.10)/(80)] = (0.834, 0.966) We are 95% confident that the true proportion of affirmative responses (the concept of putting a child up for adoption if one does not possess adequate qualities to raise it) is between .834 and .966.
11. Confidence Intervals for Proportions Question 10: Raising a newborn p = .833, q = .167, z* = 1.960, n = 84 0.833 ± sqrt[(.833)(.167)/(84)] = (0.753, 0.913) We are 95% confident that the true proportion of affirmative responses (raising a newborn if it was your bearing) is between .753 and .913.
12. Hypothesis Test (Larger Study) Question 7: Abortion “A new Gallup Poll, conducted May 7-10, finds 51% of Americans calling themselves "pro-life" on the issue of abortion and 42% "pro-choice.”” We do not know whether or not the study was conducted randomly, but there is no reason not to assume the sample as unrepresentative.1 Our sample of responses to the concept of abortion showed that 34 out of the 83 respondents claimed a pro-choice point view.
13. Hypothesis Test (Larger Study) Question 7: Abortion H0: p = .51 Ha: p ≠ .51 n = 83 2. Randomness: Survey sample was acquired at random. 10% condition: The sample consisted of less than 10% of the population. np = 83(.51) = 42.33 > 10 nq = 83(.49) = 40.67 > 10 3. We will conduct a 1-proportion z-test. 4. ˆp = .410 z = .410-.51/sqrt[(.51)(.49)/83] = -1.83 p = 0.0674 5. Since the p-value greater than .05, we do not reject the null hypothesis. There is not enough evidence to say that the proportion of the pro-life opinions on abortion in the Gallup poll differ from our sample of pro-life opinions.
14. Hypothesis Test (Larger Study) Question 8: Abstinence “Over 50 percent of teens chose to be abstinent, and abstinence is becoming more popular. 73 percent of teens say they do not think it is embarrassing for a teen to be a virgin, and 58 percent say teens should not have sex, regardless of what precautions they take…” Randomness is not stated, but there is no reason to assume otherwise.2 Our sample of responses to the concept of abstinence showed that 35 out of the 73 respondents claimed a pro-abstinence point view.
15. Hypothesis Test (Larger Study) Question 8: Abstinence H0: p = .58 Ha: p ≠ .58 n = 73 2. Randomness: Survey sample was acquired at random. 10% condition: The sample consisted of less than 10% of the population. np = 73(.58) = 42.34 > 10 nq = 73(.42) = 30.66 > 10 3. We will conduct a 1-proportion z-test. 4. ˆp = .479 z = .479-.58/sqrt[(.58)(.42)/73] = -1.74 p = 0.082 5. Since the p-value greater than .05, we do not reject the null hypothesis. There is not enough evidence to say that the proportion of the pro-abstinence opinions on teenhelp.com differ from our sample of pro-abstinence opinions.
16. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 2: Age H0: MeanM = MeanF ; Ha: MeanM ≠ MeanF MeanM = Mean age of males MeanF = Mean age of females 2. Randomness: Survey sample was acquired randomly. Independence: Age of one individual does not affect another’s. 10% condition: 36 males and 51 females are less than 10% of the population. Nearly normal: Distributions are nearly normal with no outliers. (See next slide) 3. We will conduct a 2-sample t-test. 4. nM=36, xM=16.81, sM=0.668; nF=51, xF=16.82, sF=0.713; df = 85 P(t ≠ (16.81 – 16.82)/sqrt[(0.6682/36 + 0.7132/51)] t = 0.0669, p = 0.947 5. Since the p-value is much greater than .05, we have no conclusive evidence against the null hypothesis. There is no difference between the mean age of males and females within the sample.
18. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 3: Grade Level H0: MeanM = MeanF ; Ha: MeanM ≠ MeanF MeanM = Mean grade level of males MeanF = Mean grade level of females 2. Randomness: Survey sample was acquired randomly. Independence: One individual’s grade level does not affect another’s. 10% condition: 36 males and 47 females are less than 10% of the population. Nearly normal: Male distribution is skewed left while female distribution is roughly symmetric. (See next slide) 3. We will conduct a 2-sample t-test. 4. nM=36, xM=11.555, sM=0.558; nF=47, xF=11.574, sF=0.499; df = 81 P(t ≠ (11.555– 11.574)/sqrt[(0.5582/36 + 0.4992/47)] t = -0.160, p = 0.872 5. Since the p-value is greater than .05, we have no conclusive evidence against the null hypothesis. There is no difference between the mean grade level s of males and females within the sample.
20. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 4: Number of times engaged in unprotected sex (in the past year) H0: MeanM = MeanF ; Ha: MeanM ≠ MeanF MeanM = Mean number of times males had unprotected sex in the past year MeanF = Mean number of times females had unprotected sex in the past year 2. Randomness: Survey sample was acquired randomly. Independence: Aside from partnership within the sample, the number of times one chooses to have unprotected sex does not affect another’s. 10% condition: 34 males and 49 females are less than 10% of the population. Nearly normal: Both distributions are skewed to the right. (See next slide) 3. We will conduct a 2-sample t-test. 4. nM=34, xM=1.353, sM=3.374; nF=49, xF=0.775, sF=2.013; df = 81 P(t ≠ (1.353– 0.775)/sqrt[(0.3.3742/34 + 2.0132/49)] t = 0.895, p = 0.375 5. Since the p-value is greater than .05, we have insufficient evidence against the null hypothesis. There is no difference between the mean number of times a male/female had unprotected sex in the past year.
21. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 4: Number of times engaged in unprotected sex (in the past year)
22. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 5: Number of sexual partners (in the past year) H0: MeanM = MeanF ; Ha: MeanM ≠ MeanF MeanM = Mean number of sexual partners males had in the past year MeanF = Mean number of sexual partners females had in the past year 2. Randomness: Survey sample was acquired randomly. Independence: Aside from partnership within the sample, the number of partners one chooses to engage in sex with does not affect another’s. 10% condition: 34 males and 49 females are less than 10% of the population. Nearly normal: Both distributions are skewed to the right. (See next slide) 3. We will conduct a 2-sample t-test. 4. nM=34, xM=1.706, sM=2.541; nF=49, xF=0.510, sF=0.739; df = 81 P(t ≠ (1.706– 0.510)/sqrt[(2.5412/34 + 0.7392/49)] t = 2.667, p = 0.0113 5. Since the p-value is less than .05, we conclude that there is sufficient evidence against the null hypothesis. We reject the claim that the mean number of sex partners a male had in the past year is equal to the mean number of sex partners a female had in the past year.
23. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 5: Number of sexual partners (in the past year)
24. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 6: Number of times a day one thinks about sex H0: MeanM = MeanF ; Ha: MeanM ≠ MeanF MeanM = Mean number of times a day males think about sex MeanF = Mean number of times a day females think about sex 2. Randomness: Survey sample was acquired randomly. Independence: The number of times a day one individual thinks about sex does not affect another’s. 10% condition: 28 males and 39 females are less than 10% of the population. Nearly normal: Male distribution is bimodal while female distribution is skewed right. (See next slide) 3. We will conduct a 2-sample t-test. 4. nM=28, xM=5.393, sM=5.852; nF=39, xF=1.462, sF=4.038; df = 65 P(t ≠ (5.393– 1.462)/sqrt[(5.8522/28 + 4.0382/39)] t = 3.068, p = 0.00364 5. Since the p-value is less than .05, we can conclude that there is sufficient evidence against the null hypothesis. We reject the claim that the mean number of times a day males thinks about sex is equal to the mean number of times a day females think about sex.
25. Hypothesis Test Comparing Affirmative Responses (Males vs. Females) Question 6: Number of times a day one thinks about sex
26. x2 Test for Homogeneity Among Grades Question 7: Abortion
27. x2 Test for Homogeneity Among Grades Question 7: Abortion Ho: Affirmative responses (agree) to the concept of abortion are independent of grade level. Ha: Affirmative responses to the concept of abortion are dependent on grade level. Randomness: Survey sample was selected at random. 10% condition: We sampled less than 10% of the world’s population of high school students. We will conduct a x2 test for homogeneity. X2 = (0-0.384)2/0.384 + (0-0.57)2/0.57 + (1-0.047)2/0.047 + (11-12.66)2/12.66 + (20-18.80)2/18.80 + (2-1.54)2/1.54 + (22-19.95)2/19.95 + (29-29.63)2/29.63 + (1-2.42)2/2.42 = 21.99 P = .000201 Since the P-value is less than .05, we proceed to reject the null hypothesis. There is not enough evidence to suggest that the affirmative responses regarding the concept of abortion are independent of grade level.
28. x2 Test for Homogeneity Among Grades Question 8: Abstinence
29. x2 Test for Homogeneity Among Grades Question 8: Abstinence H0: Affirmative responses (for) to the concept of abstinence are independent of grade level. Ha: Affirmative responses to the concept of abstinence are independent of grade level. Randomness: Survey sample was selected at random. 10% condition: We sampled less than 10% of the world’s population of high school students. We will conduct a x2 test for homogeneity. X2 = (0-0.402)2/0.402 + (0-0.425)2/0.425 + (1-0.172)2/0.172 + (12-13.28)2/13.28 + (14-14.03)2/14.03 + (7-5.69)2/5.69 + (23-21.32)2/21.32 + (23-22.52)2/22.52 + (7-9.14)2/9.14 = 5.866 P = 0.209 Since the p-value is greater than .05, it is concluded that there is insufficient evidence against the null hypothesis. We do not reject the hypothesis that affirmative responses to the concept of abstinence are independent of grade levels.
30. x2 Test for Homogeneity Among Grades Question 9: Adoption if one cannot raise a newborn
31. x2 Test for Homogeneity Among Grades Question 9: Adoption if one cannot raise a newborn Ha: Affirmative responses (agree) to the idea of adoption if one cannot raise a newborn are independent of grade level. H0: Affirmative responses to the idea of adoption if one cannot raise a newborn are dependent of grade level. Randomness: Survey sample was selected at random. 10% condition: We sampled less than 10% of the world’s population of high school students. We’ll conduct a x2 test for homogeneity. X2 = (0-0.839)2/0.839 + (0-0.092)2/0.092 + (1-0.069)2/0.069 + (25-28.53)2/28.53 + (5-3.13)2/3.13 + (4-2.35)2/2.35 + (48-43.63)2/43.63 + (3-3.13)2/3.13 + (1-3.59)2/3.59 = 19.194 P = .000719 Since the p-value is less than .05, we proceed to reject the null hypothesis. There is insufficient evidence to conclude that affirmative responses toward the idea of putting a child up for adoption is independent of grade level.
32. x2 Test for Homogeneity Among Grades Question 10: Raising a newborn
33. x2 Test for Homogeneity Among Grades Question 10: Raising a newborn Ha: Affirmative responses (for) toward the idea of raising a newborn if one were to impregnate/be impregnated are independent of grade level. H0: Affirmative responses toward the idea of raising a newborn if one were to impregnate/be impregnated are dependent of grade level. Randomess: Survey sample was selected at random. 10% condition: We sampled less than 10% of the world’s population. We’ll conduct a x2 test for homogeneity. X2 = (0-0.779)2/0.779 + (0-0.186)2/0.186 + (1-0.035)2/0.035 + (29-26.48)2/26.48 +(4-6.33)2/6.33 + (1-1.19)2/1.19 + (38-39.73)2/39.73 + (12-9.48)2/9.49 + (1-1.78)2/1.78 = 29.87 P = .0000052 Since the p-value is lower than .05, we will proceed to reject the null hypothesis. There is not enough evidence to conclude that affirmative responses to the idea of raising a newborn if one were to impregnate/be impregnated are
34. References 1"More Americans-Pro-Life - Than-Pro-Choice- for First Time." Gallup.Com - Daily News, Polls, Public Opinion on Government, Politics, Economics, Management. Web. 25 May 2011. <http://www.gallup.com/poll/118399/more-americans-pro-life-than-pro-choice-first-time.aspx>. 2"Benefits of Teen Abstinence - Teen Sexuality." Teen Help - Advice for Parents and Teens. Web. 26 May 2011. <http://www.teenhelp.com/teen-sexuality/teen-abstinence.html>.