This document provides a summary of analyses conducted on a secondary dataset about alcohol consumption among UK secondary school students. Descriptive statistics show that the average age was 13, with roughly equal proportions of boys and girls. 43% reported ever drinking alcohol. Hypothesis testing found the proportion who drank was less than the reported UK rate of 45%. Chi-square analysis revealed a weak association between peer pressure and drinking frequency. Correlation found a weak negative relationship between family attitude toward drinking and student drinking frequency. The document describes and interprets the results of these analyses to understand patterns of underage alcohol use.
This document provides an introduction to descriptive statistics. It discusses organizing and presenting both qualitative and quantitative data. For qualitative data, it describes frequency distribution tables, relative frequencies, percentages, and graphs like bar charts and pie charts. For quantitative data, it covers stem-and-leaf displays, frequency distributions, class widths and midpoints, relative frequencies and percentages. It also discusses histograms for presenting grouped quantitative data. Examples are provided to illustrate these concepts and techniques.
This chapter discusses statistical inferences about two populations. It covers testing hypotheses and constructing confidence intervals about:
1) The difference in two population means using the z-statistic and t-statistic.
2) The difference in two related populations when the differences are normally distributed.
3) The difference in two population proportions.
4) Two population variances when the populations are normally distributed.
The chapter presents the z-test for differences in two means and the t-test for independent and related samples. It also discusses tests and intervals for differences in proportions and variances. Sample problems and solutions are provided to illustrate the concepts and computations.
The document discusses small sample tests of hypotheses. It explains that for small sample sizes (n<30), a t-distribution is used instead of the normal distribution to account for the small sample size. There are three cases discussed for small sample tests: testing a population mean, comparing the means of two independent samples, and comparing the means of two paired samples. For each case, the assumptions, test statistic (involving a t-distribution), and an example are provided.
This document provides an overview of Chapter 8 in a statistics textbook. The chapter covers statistical inference for estimating parameters of single populations, including: point and interval estimation, estimating the population mean when the standard deviation is known or unknown, estimating the population proportion, estimating the population variance, and estimating sample size. Key concepts introduced include confidence intervals, the t-distribution, chi-square distribution, and determining necessary sample size. The chapter outline and learning objectives are also summarized.
This document provides an overview and outline of Chapter 12 which covers the analysis of categorical data using two chi-square tests: the chi-square goodness-of-fit test and the chi-square test of independence. These tests are useful for analyzing nominal data, such as categories from market research, to determine if observed frequencies match expected distributions or if two variables are independent. The chapter also provides examples of solving problems using these tests and key terms related to categorical data analysis.
Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests
Chapter Topic:
Hypothesis Testing Methodology
Z Test for the Mean ( Known)
p-Value Approach to Hypothesis Testing
Connection to Confidence Interval Estimation
One-Tail Tests
t Test for the Mean ( Unknown)
Z Test for the Proportion
Potential Hypothesis-Testing Pitfalls and Ethical Issues
Chapter 8 Confidence Interval Estimation
Estimation Process
Point Estimates
Interval Estimates
Confidence Interval Estimation for the Mean ( Known )
Confidence Interval Estimation for the Mean ( Unknown )
Confidence Interval Estimation for the Proportion
This document provides an outline and overview of Chapter 9 from a statistics textbook. The chapter covers hypothesis testing for single populations, including:
- Establishing null and alternative hypotheses
- Understanding Type I and Type II errors
- Testing hypotheses about single population means when the standard deviation is known or unknown
- Testing hypotheses about single population proportions and variances
- Solving for Type II errors
The chapter teaches students how to implement the HTAB (Hypothesis, Test Statistic, Accept/Reject regions, Boundaries, Conclusion) system to scientifically test hypotheses using statistical techniques like z-tests and t-tests. Key concepts covered include one-tailed and two-tailed tests, critical values, p
This document provides an introduction to descriptive statistics. It discusses organizing and presenting both qualitative and quantitative data. For qualitative data, it describes frequency distribution tables, relative frequencies, percentages, and graphs like bar charts and pie charts. For quantitative data, it covers stem-and-leaf displays, frequency distributions, class widths and midpoints, relative frequencies and percentages. It also discusses histograms for presenting grouped quantitative data. Examples are provided to illustrate these concepts and techniques.
This chapter discusses statistical inferences about two populations. It covers testing hypotheses and constructing confidence intervals about:
1) The difference in two population means using the z-statistic and t-statistic.
2) The difference in two related populations when the differences are normally distributed.
3) The difference in two population proportions.
4) Two population variances when the populations are normally distributed.
The chapter presents the z-test for differences in two means and the t-test for independent and related samples. It also discusses tests and intervals for differences in proportions and variances. Sample problems and solutions are provided to illustrate the concepts and computations.
The document discusses small sample tests of hypotheses. It explains that for small sample sizes (n<30), a t-distribution is used instead of the normal distribution to account for the small sample size. There are three cases discussed for small sample tests: testing a population mean, comparing the means of two independent samples, and comparing the means of two paired samples. For each case, the assumptions, test statistic (involving a t-distribution), and an example are provided.
This document provides an overview of Chapter 8 in a statistics textbook. The chapter covers statistical inference for estimating parameters of single populations, including: point and interval estimation, estimating the population mean when the standard deviation is known or unknown, estimating the population proportion, estimating the population variance, and estimating sample size. Key concepts introduced include confidence intervals, the t-distribution, chi-square distribution, and determining necessary sample size. The chapter outline and learning objectives are also summarized.
This document provides an overview and outline of Chapter 12 which covers the analysis of categorical data using two chi-square tests: the chi-square goodness-of-fit test and the chi-square test of independence. These tests are useful for analyzing nominal data, such as categories from market research, to determine if observed frequencies match expected distributions or if two variables are independent. The chapter also provides examples of solving problems using these tests and key terms related to categorical data analysis.
Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests
Chapter Topic:
Hypothesis Testing Methodology
Z Test for the Mean ( Known)
p-Value Approach to Hypothesis Testing
Connection to Confidence Interval Estimation
One-Tail Tests
t Test for the Mean ( Unknown)
Z Test for the Proportion
Potential Hypothesis-Testing Pitfalls and Ethical Issues
Chapter 8 Confidence Interval Estimation
Estimation Process
Point Estimates
Interval Estimates
Confidence Interval Estimation for the Mean ( Known )
Confidence Interval Estimation for the Mean ( Unknown )
Confidence Interval Estimation for the Proportion
This document provides an outline and overview of Chapter 9 from a statistics textbook. The chapter covers hypothesis testing for single populations, including:
- Establishing null and alternative hypotheses
- Understanding Type I and Type II errors
- Testing hypotheses about single population means when the standard deviation is known or unknown
- Testing hypotheses about single population proportions and variances
- Solving for Type II errors
The chapter teaches students how to implement the HTAB (Hypothesis, Test Statistic, Accept/Reject regions, Boundaries, Conclusion) system to scientifically test hypotheses using statistical techniques like z-tests and t-tests. Key concepts covered include one-tailed and two-tailed tests, critical values, p
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.1: Basics of Hypothesis Testing
1. The document discusses sampling methods and the central limit theorem. It describes various probability sampling methods like simple random sampling, systematic random sampling, and stratified random sampling.
2. It defines the sampling distribution of the sample mean and explains that according to the central limit theorem, the sampling distribution will follow a normal distribution as long as the sample size is large.
3. The mean of the sampling distribution is equal to the population mean, and its variance is equal to the population variance divided by the sample size. This allows probabilities to be determined about a sample mean falling within a certain range.
This document provides an overview and outline of Chapter 14: Multiple Regression Analysis from a textbook. It discusses key concepts in multiple regression including developing multiple regression models with two or more predictors, performing significance tests on the overall model and regression coefficients, interpreting residuals, R-squared, and adjusted R-squared values, and interpreting computer output for multiple regression analyses. Examples of multiple regression problems and solutions are provided.
Reporting a one way repeated measures anovaKen Plummer
The document provides guidance on reporting the results of a one-way repeated measures ANOVA in APA style. It includes templates for reporting the main ANOVA results and any post-hoc pairwise comparisons between conditions. Key sections are highlighted to fill in values from an example SPSS output to generate a complete APA-style results section reporting a significant effect of time of season on pizza consumption.
This document provides an overview of Chapter 7 from a statistics textbook. The chapter covers sampling and sampling distributions. It has 6 main learning objectives, including determining when to use sampling vs a census, distinguishing random and nonrandom sampling, and understanding the impact of the central limit theorem. The chapter outline lists 7 sections that will be covered, such as sampling, sampling distributions of the mean and proportion, and key terms. It provides examples to illustrate the central limit theorem and formulas from it.
This document provides an outline and overview of Chapter 3: Descriptive Statistics from a statistics textbook. It discusses key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), measures of shape (skewness, kurtosis), and correlation. The chapter will cover calculating these statistics for both ungrouped and grouped data, and interpreting them to describe data distributions. It emphasizes that descriptive statistics are used to numerically summarize and characterize data sets.
This document is a project report submitted by Rahul Surendra Jain, a student at N.G. Bedekar College of Commerce studying for his M.Com part-II in Business Management. The project analyzes the organizational behavior of Nestle. It includes Rahul's declaration that the information is true and original, as well as certificates from his college confirming the project work. The project report then provides an executive summary and analyzes various aspects of Nestle's organization including its company overview, strategies, structure, external and internal environments, culture, decision making processes, and conflicts.
Reporting a multiple linear regression in apaKen Plummer
A multiple linear regression was calculated to predict weight based on height and sex. A significant regression equation was found (F(2,13)=981.202, p<.000), with an R2 of .993. Participants' predicted weight is equal to 47.138 + 2.101(height) - 39.133(sex), where height is measured in inches and sex is coded as 0 for male and 1 for female. Both height and sex were significant predictors of weight.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 2: Exploring Data with Tables and Graphs
2.2: Histograms
Descriptive statistics and anova analysisSakthivel R
Descriptive statistics and ANOVA analysis are statistical methods. Descriptive statistics summarize data in a clear way and lay the foundation for statistical knowledge by analyzing the distribution, dispersion, and other characteristics of data. ANOVA (analysis of variance) is a technique used to analyze the effects of independent variables on a dependent variable. It extends the t-test to compare more than two sample means and determines whether the means are equal or different.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
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.
This document outlines the steps for hypothesis testing, including:
1. Defining the null and alternative hypotheses (H0 and H1). H0 is presumed true while H1 has the burden of proof.
2. Conducting a 5-step hypothesis testing procedure: state hypotheses, select significance level, select test statistic, formulate decision rule, make decision and interpret.
3. Distinguishing between one-tailed and two-tailed tests. Keywords in the problem statement determine if it is left-tailed, right-tailed, or two-tailed.
4. Examples are provided for testing hypotheses about population means when the population standard deviation is known or unknown, and for testing hypotheses about
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 1: Introduction to Statistics
Section 1.2: Types of Data, Key Concept
This chapter introduces students to the design of experiments and analysis of variance. It covers one-way and two-way ANOVA, randomized block designs, and interaction. Students learn to compute and interpret results from one-way ANOVA, randomized block designs, and two-way ANOVA. They also learn about multiple comparison tests and when to use them to analyze differences between specific treatment means.
MAS Holdings is a $1.6 billion Sri Lankan conglomerate and one of the world's largest apparel manufacturers. It was founded in 1987 and has 48 manufacturing facilities across 15 countries. MAS has diversified into other businesses like IT and owns brands like amanté. It is a leader in sustainable practices and was the first in its industry to achieve LEED Platinum certification for its Thurulie green manufacturing plant. MAS has over 74,000 employees worldwide and works with major brands like Nike, Marks & Spencer, and Victoria's Secret.
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.
A manufacturing company must choose between three aggregate production plans to meet demand that fluctuates over a six-month period. Plan I varies the workforce size, Plan II keeps the workforce constant and uses overtime and idle time, and Plan III keeps the workforce constant and allows inventory and stockouts. The costs of each plan are compared. Plan I costs $24,500, Plan II costs $36,600, and Plan III costs $9,600. Plan III, which keeps the workforce constant and allows for inventory and stockouts, is identified as the preferred plan due to having the lowest cost.
This document contains a marketing plan for a new children's food product called Superbaby Milk Cubes (SMC) produced by Superbaby Foods Limited. The plan includes an executive summary, background on the company and product, analyses of the market environment, competitors, customers, and target market. It outlines goals to establish the company as a challenger, maximize profits, and achieve customer loyalty. Tactical plans include extensive advertising and promotions over 6 months to build brand awareness and image for the new SMC product line.
1) The document analyzes customer data from a retailer to determine which demographic groups are most likely to redeem coupons and should therefore be targeted for coupon campaigns.
2) It finds that homeowners, those with incomes of $35,000-$99,000, and households with 2 adults and children are the most likely to redeem coupons.
3) However, the Chi-squared Automatic Interaction Detection (CHAID) model developed predicts that homeowners, probable homeowners, and probable renters are most likely to redeem, which contradicts the initial data analysis showing renters and unknown homeownership are most likely.
UNDERSTANDING CONSUMER PURCHASING BEHAVIOUR AND CONSUMPTION IN THE ORAL CARE ...TIEZHENG YUAN
The key findings from the secondary research are:
1. Young adults aged 16-24 are most interested in teeth whitening benefits from oral care products.
2. While hygiene benefits like preventing cavities and gum disease are important, consumers increasingly seek cosmetic benefits like whitening teeth.
3. Toothpaste manufacturers offer a variety of products focused on both hygiene and cosmetic benefits like whitening teeth.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.1: Basics of Hypothesis Testing
1. The document discusses sampling methods and the central limit theorem. It describes various probability sampling methods like simple random sampling, systematic random sampling, and stratified random sampling.
2. It defines the sampling distribution of the sample mean and explains that according to the central limit theorem, the sampling distribution will follow a normal distribution as long as the sample size is large.
3. The mean of the sampling distribution is equal to the population mean, and its variance is equal to the population variance divided by the sample size. This allows probabilities to be determined about a sample mean falling within a certain range.
This document provides an overview and outline of Chapter 14: Multiple Regression Analysis from a textbook. It discusses key concepts in multiple regression including developing multiple regression models with two or more predictors, performing significance tests on the overall model and regression coefficients, interpreting residuals, R-squared, and adjusted R-squared values, and interpreting computer output for multiple regression analyses. Examples of multiple regression problems and solutions are provided.
Reporting a one way repeated measures anovaKen Plummer
The document provides guidance on reporting the results of a one-way repeated measures ANOVA in APA style. It includes templates for reporting the main ANOVA results and any post-hoc pairwise comparisons between conditions. Key sections are highlighted to fill in values from an example SPSS output to generate a complete APA-style results section reporting a significant effect of time of season on pizza consumption.
This document provides an overview of Chapter 7 from a statistics textbook. The chapter covers sampling and sampling distributions. It has 6 main learning objectives, including determining when to use sampling vs a census, distinguishing random and nonrandom sampling, and understanding the impact of the central limit theorem. The chapter outline lists 7 sections that will be covered, such as sampling, sampling distributions of the mean and proportion, and key terms. It provides examples to illustrate the central limit theorem and formulas from it.
This document provides an outline and overview of Chapter 3: Descriptive Statistics from a statistics textbook. It discusses key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), measures of shape (skewness, kurtosis), and correlation. The chapter will cover calculating these statistics for both ungrouped and grouped data, and interpreting them to describe data distributions. It emphasizes that descriptive statistics are used to numerically summarize and characterize data sets.
This document is a project report submitted by Rahul Surendra Jain, a student at N.G. Bedekar College of Commerce studying for his M.Com part-II in Business Management. The project analyzes the organizational behavior of Nestle. It includes Rahul's declaration that the information is true and original, as well as certificates from his college confirming the project work. The project report then provides an executive summary and analyzes various aspects of Nestle's organization including its company overview, strategies, structure, external and internal environments, culture, decision making processes, and conflicts.
Reporting a multiple linear regression in apaKen Plummer
A multiple linear regression was calculated to predict weight based on height and sex. A significant regression equation was found (F(2,13)=981.202, p<.000), with an R2 of .993. Participants' predicted weight is equal to 47.138 + 2.101(height) - 39.133(sex), where height is measured in inches and sex is coded as 0 for male and 1 for female. Both height and sex were significant predictors of weight.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 2: Exploring Data with Tables and Graphs
2.2: Histograms
Descriptive statistics and anova analysisSakthivel R
Descriptive statistics and ANOVA analysis are statistical methods. Descriptive statistics summarize data in a clear way and lay the foundation for statistical knowledge by analyzing the distribution, dispersion, and other characteristics of data. ANOVA (analysis of variance) is a technique used to analyze the effects of independent variables on a dependent variable. It extends the t-test to compare more than two sample means and determines whether the means are equal or different.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
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.
This document outlines the steps for hypothesis testing, including:
1. Defining the null and alternative hypotheses (H0 and H1). H0 is presumed true while H1 has the burden of proof.
2. Conducting a 5-step hypothesis testing procedure: state hypotheses, select significance level, select test statistic, formulate decision rule, make decision and interpret.
3. Distinguishing between one-tailed and two-tailed tests. Keywords in the problem statement determine if it is left-tailed, right-tailed, or two-tailed.
4. Examples are provided for testing hypotheses about population means when the population standard deviation is known or unknown, and for testing hypotheses about
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 1: Introduction to Statistics
Section 1.2: Types of Data, Key Concept
This chapter introduces students to the design of experiments and analysis of variance. It covers one-way and two-way ANOVA, randomized block designs, and interaction. Students learn to compute and interpret results from one-way ANOVA, randomized block designs, and two-way ANOVA. They also learn about multiple comparison tests and when to use them to analyze differences between specific treatment means.
MAS Holdings is a $1.6 billion Sri Lankan conglomerate and one of the world's largest apparel manufacturers. It was founded in 1987 and has 48 manufacturing facilities across 15 countries. MAS has diversified into other businesses like IT and owns brands like amanté. It is a leader in sustainable practices and was the first in its industry to achieve LEED Platinum certification for its Thurulie green manufacturing plant. MAS has over 74,000 employees worldwide and works with major brands like Nike, Marks & Spencer, and Victoria's Secret.
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.
A manufacturing company must choose between three aggregate production plans to meet demand that fluctuates over a six-month period. Plan I varies the workforce size, Plan II keeps the workforce constant and uses overtime and idle time, and Plan III keeps the workforce constant and allows inventory and stockouts. The costs of each plan are compared. Plan I costs $24,500, Plan II costs $36,600, and Plan III costs $9,600. Plan III, which keeps the workforce constant and allows for inventory and stockouts, is identified as the preferred plan due to having the lowest cost.
This document contains a marketing plan for a new children's food product called Superbaby Milk Cubes (SMC) produced by Superbaby Foods Limited. The plan includes an executive summary, background on the company and product, analyses of the market environment, competitors, customers, and target market. It outlines goals to establish the company as a challenger, maximize profits, and achieve customer loyalty. Tactical plans include extensive advertising and promotions over 6 months to build brand awareness and image for the new SMC product line.
1) The document analyzes customer data from a retailer to determine which demographic groups are most likely to redeem coupons and should therefore be targeted for coupon campaigns.
2) It finds that homeowners, those with incomes of $35,000-$99,000, and households with 2 adults and children are the most likely to redeem coupons.
3) However, the Chi-squared Automatic Interaction Detection (CHAID) model developed predicts that homeowners, probable homeowners, and probable renters are most likely to redeem, which contradicts the initial data analysis showing renters and unknown homeownership are most likely.
UNDERSTANDING CONSUMER PURCHASING BEHAVIOUR AND CONSUMPTION IN THE ORAL CARE ...TIEZHENG YUAN
The key findings from the secondary research are:
1. Young adults aged 16-24 are most interested in teeth whitening benefits from oral care products.
2. While hygiene benefits like preventing cavities and gum disease are important, consumers increasingly seek cosmetic benefits like whitening teeth.
3. Toothpaste manufacturers offer a variety of products focused on both hygiene and cosmetic benefits like whitening teeth.
Statistics are used by organizations to measure and analyze business performance. American Express uses statistics such as total returns to shareholders, numbers of cardholders by age group, and cardholder spending by age to analyze business units, identify targeted customer groups, and inform marketing campaigns. Statistics on labor force characteristics by gender help conclude that male monthly incomes are typically higher than females, though this does not necessarily mean males spend more.
DELL CASE STUDY - UNDERSTANDING DELL’S CUSTOMERS AS A KEY IN DEVELOPING MARKE...TIEZHENG YUAN
This report summarizes a survey conducted by Dell of recent purchasers of their PCs and notebooks in the UK. The survey aimed to understand customers' demographics, internet usage, satisfaction and loyalty towards Dell, perceptions of Dell's performance, price sensitivity, and personality characteristics. Key findings include that most customers were college-educated, middle-aged, and had household incomes between £30,000-£75,000. Customers spent 1-10 hours online per week and were highly satisfied with Dell's products, services, and prices. A positive relationship was found between satisfaction, loyalty, and perceptions of Dell's performance. Customers were also found to be price sensitive.
This document presents a marketing research proposal for Lenovo smartphones entering the UK market. It outlines desk research including analyzing Lenovo's positioning and competitors, as well as primary qualitative and quantitative research. The qualitative research involves focus groups divided by age to understand attitudes towards smartphones and brands. Quantitative research uses online questionnaires of 4250 UK residents quota sampled by age to measure attitudes. The research aims to define Lenovo's target segment by understanding smartphone usage, purchase behavior, and competitors' strengths and weaknesses in the UK market.
This document summarizes a capstone project that assessed the knowledge of RN case managers regarding evidence-based nutritional guidelines for heart failure patients. A survey was distributed to RN case managers to evaluate their confidence level and knowledge of guidelines around sodium and fluid intake. The results identified several knowledge deficits and indicated a need for increased education and support from leadership to ensure nurses have the most up-to-date clinical guidelines. Suggested changes included implementing regular educational requirements for nurses and allocating more time for nurses to obtain necessary knowledge to properly educate patients.
This document describes a research study that examined factors influencing university students' consumption of energy drinks in Dhaka, Bangladesh. The study used surveys to collect data on 200 students' demographic characteristics, purchasing behaviors, health consciousness, and perceptions of energy drink attributes. Statistical analyses including chi-square tests, factor analysis, and regression analysis were used to understand relationships between gender, purchasing patterns, advertising preferences, consumption frequency, and health attitudes. The results identified taste, caffeine content, social aspects, refreshment, and brand loyalty as key influences on students' energy drink consumption.
What is the Association between COPD and HRQoL in Manchester in 2011? Helen Beaumont-Kellner
This study examined the association between Chronic Obstructive Pulmonary Disease (COPD) and health-related quality of life in Manchester, UK in 2011. The study found that COPD patients reported lower health-related quality of life scores compared to a control group without chronic conditions. COPD patients were also more likely to be current or former smokers and less likely to have received education after age 16. However, the study had some limitations due to its small sample size and inability to account for all confounding variables like age.
This document provides data from a study on steroid usage, grip strength, aggression, and happiness. It includes the following information:
1) A table with data from 14 participants, including the number of weeks on steroids, grip strength, aggression score, happiness score, and investigator number.
2) Descriptions of what each column in the data table represents, such as the scales for grip strength, aggression, and happiness.
3) The sample size is 14 participants.
Parental alcohol consumption and the risk of congenital heart diseases in off...BARRY STANLEY 2 fasd
Conclusions: Although the role of potential bias and evidence of heterogeneity should be carefully evaluated, our review indicates that parental alcohol exposures are significantly associated with the risk of CHDs in offspring, which highlights the necessity of improving health awareness to prevent alcohol exposure during preconception and conception periods.
1) The study examined whether serum carbon isotope values (d13C), which reflect consumption of corn- and cane-based foods like sugar-sweetened beverages (SSBs), change in response to changes in SSB intake over an 18-month behavioral intervention trial.
2) At baseline, average SSB intake was 13.8 ounces per day and average serum d13C value was -19.3 per mil. A reduction of 12 ounces per day in SSB intake was associated with a 0.17 per mil reduction in serum d13C.
3) After adjusting for potential confounders, a reduction of 12 ounces per day in SSB intake over 18 months was associated with a
1. The following are body mass index (BMI) scores measured in 12.docxjackiewalcutt
1. The following are body mass index (BMI) scores measured in 12 patients who are free of diabetes and participating in a study of risk factors for obesity. Body mass index is measured as the ratio of weight in kilograms to height in meters squared. Generate a 95% confidence interval estimate of the true BMI.
25
27
31
33
26
28
38
41
24
32
35
40
2. Consider the data in Problem 1. How many subjects would be needed to ensure that a 95% confidence interval estimate of BMI had a margin of error not exceeding 2 units?
3. The mean BMI in patients free of diabetes was reported as 28.2. The investigator conducting the study described in Problem 1 hypothesizes that the BMI in patients free of diabetes is higher. Based on the data in Problem 1 is there evidence that the BMI is significantly higher that 28.2? Use a 5% level of significance.
4. Peak expiratory flow (PEF) is a measure of a patient’s ability to expel air from the lungs. Patients with asthma or other respiratory conditions often have restricted PEF. The mean PEF for children free of asthma is 306. An investigator wants to test whether children with chronic bronchitis have restricted PEF. A sample of 40 children with chronic bronchitis are studied and their mean PEF is 279 with a standard deviation of 71. Is there statistical evidence of a lower mean PEF in children with chronic bronchitis? Run the appropriate test at =0.05.
5. Consider again the study in Problem 4, a different investigator conducts a second study to investigate whether there is a difference in mean PEF in children with chronic bronchitis as compared to those without. Data on PEF are collected and summarized below. Based on the data, is there statistical evidence of a lower mean PEF in children with chronic bronchitis as compared to those without? Run the appropriate test at =0.05.
Group
Number of Children
Mean PEF
Std Dev PEF
Chronic Bronchitis
25
281
68
No Chronic Bronchitis
25
319
74
6. Using the data presented in Problem 5,
a) Construct a 95% confidence interval for the mean PEF in children without chronic bronchitis.
b) How many children would be required to ensure that the margin of error in (a) does not exceed 10 units?
7. A clinical trial is run to investigate the effectiveness of an experimental drug in reducing preterm delivery to a drug considered standard care and to placebo. Pregnant women are enrolled and randomly assigned to receive either the experimental drug, the standard drug or placebo. Women are followed through delivery and classified as delivering preterm (< 37 weeks) or not. The data are shown below.
Preterm Delivery
Experimental Drug
Standard Drug
Placebo
Yes
17
23
35
No
83
77
65
Is there a statistically significant difference in the proportions of women delivering preterm among the three treatment groups? Run the test at a 5% level of significance.
8. Using the data in Problem 7, generate a 95% confidence interval for the difference in proportions of women ...
STATISTICS PRACTICE QUIZ 3Question 1A telecommunications com.docxmckellarhastings
STATISTICS PRACTICE QUIZ 3
Question 1
A telecommunications company asserts that, according to a study they conducted, patients who have access to more cable channels during recovery from surgery are discharged sooner than those who receive only basic cable in their hospital rooms. Which of the following demonstrates clinical significance of the study?
Patients with more cable channels were discharged an average of one-half day sooner than those with fewer channels
The study used a sample size of 12
Patients with fewer cable channels requested additional nursing support an average of 3 times more during their recovery.
Patients with more cable channels tended to prefer nature and arts channels over history or news and current affairs channels
Question 2
After completing the power analysis, the researchers determine they need a sample size of 400 to have adequate power in their study. After enrolling subjects, they have a lower response rate than anticipated and they only enroll 320. Inadequate enrollment may increase the risk of:
A type II error
Systematic bias
A type I error
Statistical significance
Question 3
Researchers study the relationship between interpersonal violence and health in college age women. The researchers examined the average score on a psychological distress scale and compared the score for abused versus non-abused women. If the researchers report a statistically significant difference and are
incorrect about this conclusion what type of error could it be?
A type I error
A type II error
A clinical error
An error of omission
Question 4
Researchers studied the relationship between Vitamin B12 consumption and hair growth and report a p value of 0.56. The study was a pilot study with an alpha of 0.10. You know this means:
There is no statistically significant relationship between Vitamin B12 consumption and hair growth.
Vitamin B12 consumption is association with a 56% increase in hair growth.
The sample size was too small and a type I error was made
There is a statistically significant relationship between Vitamin B12 consumption and hair growth.
Question 5
Researchers study the relationship between interpersonal violence and health in college age women. They selected an alpha of 0.05. The researchers examined the average score on a psychological distress scale and compared the score for abused versus non-abused women. A p value of 0.016 is reported. Based on this information, you know:
There is a statistically significant difference in the average psychological distress score
This is a clinically significant result
There is no significant difference in the average psychological distress score.
In this study women who were abused were statistically more likely to report psychological distress.
Question 6
A study reports that administering vancomycin incorrectly is associated with red man syndrome. You know this means:
The p value is less than alpha
A type I error was made in administering the medic.
The document summarizes data gathered from a study on student academic behavior and performance. It provides details on:
1) The demographic characteristics of 44 student respondents, including their age, gender, family income, and parental status.
2) The academic behavior ratings of students, as perceived by teachers, with most students rated as "slightly unacceptable".
3) The process used to analyze the data, which involved calculating frequencies, percentages, means, and using Likert scales and Pearson's correlation coefficient to determine relationships between variables.
Summary statistics for binary data lecture DrZahid Khan
This document summarizes key concepts related to analyzing binary and categorical data. It discusses binary variables, prevalence, rates, case-control studies, cohort studies, relative risk, number needed to treat, and crossover trials. For case-control studies, it provides an example comparing smoking status between lung cancer patients and normal individuals, calculating an odds ratio of 6. For cohort studies, it gives data comparing lung cancer incidence between smokers and non-smokers, calculating a relative risk of 4. It also discusses how to calculate absolute risk difference and number needed to treat from cohort study data.
A two-way ANOVA and binary logistic regression were conducted to analyze factors influencing knowledge of calorie and BMI among students and staff of the Faculty of Health Sciences, UKM. The two-way ANOVA found no significant interaction between race and school but both school and race had a main effect on knowledge scores. Post-hoc tests found significant differences between diagnostic and healthcare schools, and rehabilitation and healthcare schools. The logistic regression found that only education level significantly predicted knowledge, with graduates having 15 times higher odds of higher knowledge than undergraduates. No other factors like gender, race, family history or BMI significantly predicted knowledge.
Question 1 of 254.0 PointsWhen assessing a positive relationship.docxteofilapeerless
Question 1 of 25
4.0 Points
When assessing a positive relationship between alcohol consumption and oral cancer using a case-control study, increasing the sample size of the study will result in which of the following
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The measure of the benefit to the population derived by modifying a risk factor is the:
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An approach to estimating the effects due to the single exposure factor is to compute the:
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A statistical association may be causal or noncausal. In addition, many diseases require that more than one factor be present for disease to develop. Examples of multiple causation models include:
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You are investigating the role of physical activity in heart disease and suggest that physical activity protects against having a heart attack. While presenting these data to your colleagues, someone asks if you have thought about confounders such as factor X. This factor X could have confounded your interpretation of the data if it
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Question 6 of 25
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The strategy which is not aimed at reducing selection bias is:
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The purpose of a double-blind study is to
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Which of the following is not a method for controlling the effects of confounding in epidemiologic studies
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Surgeons at Hospital A report that the mortality rate at the end of a one-year follow-up after a new coronary bypass procedure is 15%. At Hospital B, the surgeons report a one-year mortality rate of 8% for the same procedure. Before concluding that the surgeons at Hospital B have vastly superior skill, which of the following possible confounders would you examine?
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Sensitivity and specificity of a screening test refer to its:
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Lead time bias is best described as:
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The adverse consequences of using a screening test which has a low specificity include:
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The degree of agreement between several trained experts refers to
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A test that determines whether disease is actually present is a:
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A person with an inapparent infection
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The epidemiological triangle considers which factor(s) in the pathogenesis of disease:
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The Centers for Disease Control and Prevention published an article concerning the high rate of foot fungal disease in New Orleans. The article explains that there has been a high rate of foot fungal disease in New Orleans for decades. Foot fungal disease in New Orle.
Podium Presentation Midwest Social and Administrative Conference,Chicago,2008aramasa3
The document describes a study that developed and tested a survey instrument to measure patient satisfaction with pharmacy services among HIV-infected patients receiving care from either a mail-order or community pharmacy. Exploratory factor analysis established the construct validity of the survey and showed two key factors. Reliability testing found high internal consistency. The survey can be used to compare patient satisfaction across different pharmacy settings and inform efforts to improve patient-reported outcomes.
This document provides a summary of data from AlcoholEdu for College, an online alcohol prevention program, at a university. Key findings include:
- The most common risky drinking behaviors reported by students were pregaming and doing shots. The most common negative consequences were hangovers and blacking out.
- 16% of students reported high-risk drinking. 30% of students felt the course prepared them to make responsible decisions about alcohol.
- Knowledge increased from 75% correct on the pre-assessment to 88% on the post-assessment. Behavioral intentions like reducing drinks and drinking frequency also increased after the course.
- The most common location for student drinking was off-campus residences.
This document provides details about a study that examined symptoms in patients with chronic obstructive pulmonary disease (COPD) and moderate or severe air flow limitation. The study found that patients reported an average of 7-8 symptoms regardless of the severity of their air flow limitation. The most common symptoms in both groups were shortness of breath, cough, dry mouth, and lack of energy. There were no significant differences in demographic characteristics, smoking history, medication use, or reported symptoms between the moderate and severe groups.
1. In 2006, Merck released a vaccine named Gardasil for HPV - the .docxjackiewalcutt
1. In 2006, Merck released a vaccine named Gardasil for HPV - the most common cause of cervical cancer among young women. The company conducted four placebo controlled double blind clinical studies of women aged 16 to 26. The results were
Treatment
n
Cervical cancer cases
n
Genital wart cases
Gardasil
8487
0
7897
1
Placebo
8460
32
7899
91
(a): Give a 98% confidence interval for the differences in the proportion of young women who develop cervical cancer with and without the vaccine.
Lower confidence level:
Upper confidence level:
(b): Give a 98% confidence interval for the differences in the proportion of young women who develop genital warts with and without the vaccine.
Lower confidence level:
Upper confidence level:
2. Here is data on the age (in months) that 20 children said their first words of English.
15
26
10
9
15
20
18
11
8
20
7
9
10
11
11
10
12
17
11
10
If you treat this as an SRS, does it provide evidence at significance level 4% that the mean age at first word is greater than 12 months?
(a): Give the associated P-value.
P-value::
3.
Here is data on the age (in months) that 20 children said their first words of English.
15
26
10
9
15
20
18
11
8
20
7
9
10
11
11
10
12
17
11
10
Treat this as an SRS. Use it to provide a 90% confidence interval of the mean age in months at which children say their first words.
Lower confidence level:
Upper confidence level:
4. Fabrics respond differently to the same dye. Clothing manufacturers need to be able to match the colors of different fabrics. A researcher dyed samples of cotton and of ramie with a dye called procion blue, applied in the same way to each sample. Then she measured the lightness of the color with a colorimeter on a scale in which black is 0, white is 100. Here are the data for 8 pieces of each fabric.
Cotton:
48.82
48.88
48.98
49.04
48.68
49.34
48.75
49.12
Ramie:
41.72
41.83
42.05
41.44
41.27
42.27
41.12
41.49
Do these samples provide evidence significant at the 1% level that the dye affects the cotton and ramie fabrics differently?
Formulate H0 and Ha.
a. Give the test statistic:
b. Give the P-value:
c. Your decision for the hypothesis test:
A. Reject Ha.
B. Reject H0.
C. Do Not Reject H0.
D. Do Not Reject Ha.
5. Fabrics respond differently to the same dye. Clothing manufacturers need to be able to match the colors of different fabrics. A researcher dyed samples of cotton and of ramie with a dye called procion blue, applied in the same way to each sample. Then she measured the lightness of the color with a colorimeter on a scale in which black is 0, white is 100. Here are the data for 8 pieces of each fabric.
Cotton:
48.82
48.88
48.98
49.04
48.68
49.34
48.75
49.12
Ramie:
41.72
41.83
42.05
41.44
41.27
42.27
41.12
41.49
Evidently one procedure gives darker colors than the other. Make a confidence interval estimate for the mean darkness of cotton minus the mean darkness of ramie after this dye procedure. Use 99% as your confidence level.
Lower Confidence Le ...
This document provides an overview and objectives of Chapter 1: Introduction to Statistics from an elementary statistics textbook. It covers key statistical concepts like data, population, sample, variables, and the two branches of statistics - descriptive and inferential. Potential pitfalls in statistical analysis like misleading conclusions, biased samples, and nonresponse are also discussed. Examples are provided to illustrate concepts like voluntary response samples, statistical versus practical significance, and interpreting correlation.
This document provides an introduction and tables for determining sample sizes in various health studies. It covers one-sample situations like estimating a population proportion with absolute or relative precision and hypothesis tests for a population proportion. Two-sample situations covered include estimating the difference between two population proportions and hypothesis tests for two population proportions. It also addresses case-control studies, cohort studies, lot quality assurance sampling, and incidence-rate studies. Tables of minimum sample sizes are provided for each situation.
Similar to Business Statistics assignment 2014 (20)
1. MSc Marketing and Business Analysis
Marketing Statistics
17/11/2014
B064536
2. 1
Table of contents
Section number and title page
1. Description of secondary school student alcohol consumption dataset 2
2. Descriptive and summary statistics 3
3. One-tailed test about a population proportion 7
4. Chi-square test of association 9
5. Correlation 11
6. Further analysis 12
7. Limitation 12
8. Conclusion 12
List of references 13
Appendices
Appendix 1. Survey questions 14
Appendix 2. Cross tab 16
List of tables
Table 1. Variables used 3
Table 2. Summary statistics 3
Table 3. Gender frequency 4
Table 4. School year 5
Table 5. Ever had a proper alcoholic drink 6
Table 6. Chi-square test 9
Table 7. Symmetric measures 9
Table 8. Correlation between family attitude and drinking frequency 11
List of figures
Figure 1. Sample gender in percentage 4
Figure 2. School year in percentage 5
Figure 3. Ever had a proper alcoholic drink 6
3. 2
1. Description of secondary school student alcohol consumption dataset
The secondary data was obtained from UK Data Service (2014). The dataset was collected
through a survey conducted by National centre for Social Research (2012) on secondary school
pupils (aged 11 to 15) . The survey (see Appendix 1) aim to gain insight on the number of
youth alcohol drinkers and their drinking behaviour. A total of 7589 valid responses were
gathered.
The key reason for selecting this dataset is to gain insight on student drinkers so as to develop
effective strategies to curb underage drinking. A body of evidence suggest that drinking at a
young age, in particular heavy and regular drinking, can result in physical or mental problems
and put childern at risk of alcohol related accident or injury. More broadly it is also associated
with missing or falling behind at school, violent and antisocial behaviour. It is therfore
necessary to develop strategies to tackle problem drinking at both national and local level.
This report will firstly provide descriptive and summary statistic about the sample. Next, it will
conduct one-tailed population proportion hypothesis test to investigate the proportion of UK
pupils who drank alcohol before. This is followed by Chi-square test to ascertain if peer
pressure and student alcohol consumption frequence are associated. Following next, correlation
analysis will be conducted to investigate the strength of type of relationship between family
attitude and student drinking frequency. The report will also mention on further analysis and
limitation of dataset.
4. 3
2. Descriptive and Summary Statistics
Table 1. Variables used
Variable name Measurment Analysis conducted
Age Ratio Summary statistics
Gender Nominal Summary statistics
School year Ordinal Summary statistics
Unit of alcohol drank in last 7
days
Ratio Summary statistics
Ever had a proper alcoholic
drink
Nominal Summary statistic and
hypothesis test
Peer pressure Nominal Chi-square
Family attitude to pupil
drinking
Interval Correlation
Monthly usual drinking
frequency
Interval Summary statistic, Chi-square,
correlation
Table 2. Summary Statistics
Age 11-15 Units of
alcohol drank
in last 7 days
Usual
drinking
frequency
(monthly)
N
Valid 7589 7172 7314
Missing 0 417 275
Mean 13.1735 1.4194 6.7829
Median 13.0000 1.0000 8.0000
Mode 15.00 1.00 8.00
Std. Deviation 1.39074 1.47192 1.68427
Minimum 11.00 1.00 1.00
Maximum 15.00 8.00 8.00
From Table 2, it can be observed that the mean age of the sample is 13 years old. As for students
who drank alcohol before, their mean consumption was 1.4 units. Besides that, the student’s
mean monthly drinking frequency is 6.78 times.
Furthermore, the three variables analysed in Table 2, has sample standard deviation of 1.684
(Usual drinking frequency), 1.472 (Units of alcohol drank) and 1.391 (Age) respectively. This
shows that there is little variability in each variable analysed. Sample standard deviation is
calculated by using the formula: s =
1
2
n
xxi
Next, some sample characteristics will be presented using frequency tables and charts.
5. 4
Table 3. Gender Frequency
Frequency Percent Valid Percent Cumulative
Percent
Valid
Boy 3809 50.2 50.2 50.2
Girl 3780 49.8 49.8 100.0
Total 7589 100.0 100.0
Figure 1. Sample Gender in Percentage
From Table 3, in the sample of 7589 respondents, 50.2% are boy (3809) and 49.8% (3780) are
girl. Figure 1 displayed the gender percentage.
6. 5
Table 4. School Year
Frequency Percent Valid Percent Cumulative
Percent
Valid
Year 7 1481 19.5 19.5 19.5
Year 8 1526 20.1 20.1 39.6
Year 9 1580 20.8 20.8 60.4
Year 10 1553 20.5 20.5 80.9
Year 11 1449 19.1 19.1 100.0
Total 7589 100.0 100.0
Figure 2. School Year in Percentage
From Table 4, majority of respondents come from year 9 (20.8%), followed by year 10 (20.5%),
year 8 (20.1%), year 7 (19.5%) and lastly year 11 (19.1%). Figure 2, clearly display the
respondent’s school year in percentage.
18
18.5
19
19.5
20
20.5
21
Year 7 Year 8 Year 9 Year 10 Year 11
Percentage
School Year
SchoolYear in Percentage
7. 6
Table 5. Ever had a proper alcoholic drink
Frequency Percent Valid Percent Cumulative
Percent
Valid
Yes 3222 42.5 43.1 43.1
No 4256 56.1 56.9 100.0
Total 7478 98.5 100.0
Missing Not answered 111 1.5
Total 7589 100.0
Figure 3. Ever had a proper alcoholic drink
From Table 5, it can be observed that 43% of respondents have had a proper alcoholic drink
before. On the other hand, 57% did not had a proper alcoholic drink before.
Next, one-tailed test about population proportion will be conducted.
8. 7
3. One-Tailed Test About a Population Proportion (Hypothesis Test)
Rationale for conducting one-tailed test about a population proportion:
National Statistic (2013) estimated that 45% of UK pupils (age 11 to 15) had drunk alcohol at
least once. However, according to the data used in this report, it showed that in a valid sample
of 7478 UK students, 3222 pupils had drunk alcohol at least once. It is therefore in the interest
of the researcher to investigate whether the population porportion is really 45% or is it lower
as presented in the data used.
H0: π = 0.45
H1: π < 0.45
Level of significance: 0.05
Test statistic is calculated using the following formula:
Z =
0
Where:
Population standard deviation =
n
00 1
Assuming n ≥ 5 and n (1- ) ≥ 5
Checking assumption:
7478×0.45= 3365.1 ≥ 5, 7478×0.55= 4112.9 ≥ 5, therefore assumption holds and the
researcher proceed to calculate test statistic.
Test statistic calculation:
n
00 1
=
00575.0
7478
55.045.0
Z =
0
= 30.3
00575.0
45.0431.0
Using critical value approach:
At 5% significance level, crtitical value = - 1.645
Z = -3.30 < - 1.645, therefore reject H0.
9. 8
Checking using p-value approach:
From standard normal cumulative proability table, z = -3.30, p-value = 0.0005
p-value = 0.0005 < 0.05, therefore both approach are consistent, reject H0.
There is sufficient evidence to reject H0 as p-value = 0.0005 < 0.05 and therefore accept H1.
The reseracher conclude that the porportion of UK pupils who had drunk alcohol at least once
are less than 45%, at 95% confidence level.
Next, Chi-square test of association will be conducted.
10. 9
4. Chi-square test of association
Rationale for conducting Chi-square test:
Borsari and Carery (2001) claimed that excessive drinking is associated with peer pressure
among university students. It is therefore in the interest of the researcher to test this claim
among young pupils. In addition, peer pressure is a nominal variable and student drinking
frequency is an interval variable. Therefore, Chi-square test is most appropriate.
H0: Peer pressure and student drinking frequency are independent
H1: Peer pressure and student drinking frequency are dependent
Level of significan: 0.05
Test statistic is calculated using the following formula:
Calculate expected table (see appendix 2) using
n
CR
e ji
ij
ˆ
Pearson Chi-square statistic (see Table 6) using
r
i
c
j ij
ijij
e
eo
x
1 1
2
2
ˆ
ˆ
Table 6. Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 155.105 7 .000
Likelihood Ratio 152.157 7 .000
Linear-by-Linear
Association
130.174 1 .000
N of Valid Cases 6981
Compare with x 2
(r-1)(c-1),alpha = x 2
(2-1)(8-1),0.05 = x 2
7, 0.05 =14.067
Since Chi-square value = 155.105 > 14.067, there is sufficient evidence to reject H0. There is
association between peer pressure and student drinking frequency, at 95% confidence level.
Chi-square only test whether the relationship exists, therefore, the researcher uses Contingency
coefficient, Cramer’s V and Phi coefficient to measure strength of association.
Table 7. Symmetric Measures
Value Approx.
Sig.
Nominal by
Nominal
Phi .149 .000
Cramer's V .149 .000
Contingency
Coefficient
.147 .000
N of Valid Cases 6981
11. 10
Phi coefficient (Table 7) is calculated using the formula:
𝜑 =
1010 CCRR
bcad
= 0.149, Phi can take the value of [-1,1]. Phi=0.149 indicates a weak
positive association. The significance value of 0.000 means Phi value is significant.
Cramer’s V (Table 7) is calculated using the formula:
V =
1,1min
2
cr
n
x
= 0.149, Cramer’s V takes value between 0 and 1. V=0.149 indicates
a weak association. The significance value of 0.000 means Cramer’s V is significant.
Contingency coefficient (Table 7) is calculated using the formula:
C =
nx
x
2
2
= 0.147, Contingency coefficient takes value between 0 and 1. C=0.147 indicates
a weak association. The significance value of 0.000 means Contingency coefficient is
significant.
Therefore, it can be concluded that there is association between peer pressure and student
drinking frequency. However, the strength of association is not strong.
Next, correlation analysis will be conducted.
12. 11
5. Correlation
Rationale for conducting correlation analysis:
National Statistic (2012) reported that family attitude and student drinking frequency are
associated. The researcher is interested in investigating the strength of type of relationship
using correlation. Both variables are in scale measurement, it is therefore suitable to conduct
correlation analysis.
Key theory of correlation:
Correlation is a measure of linear association and does not necessarily indicate causation. The
correlation coefficient can take on values between -1 and +1. Values near -1 indicate a strong
negative linear relationship. Values near +1 indicate a strong positive linear relationship.
Table 8. Correlation between family attitude and drinking frequency
Family
attitudes to
pupil drinking
Usual
drinking
frequency
Family attitudes to pupil
drinking
Pearson Correlation 1 .553**
Sig. (2-tailed) .000
N 7183 7147
Usual drinking frequency
Pearson Correlation .553**
1
Sig. (2-tailed) .000
N 7147 7314
**. Correlation is significant at the 0.01 level (2-tailed).
From Table 8, it can be observed that parent’s attitude to pupil drinking and usual drinking
frequency have a moderate positive linear correlation of 0.553 at 0.01 significance level. It can
be explained that student drinking frequency is related to parent’s attitude. Family members
who disapproves student drinking tends to be related to lower drinking frequency. Conversely,
parents who does not mind student drinking tends to be related to higher drinking frequency.
The Pearson Correlation is calculated using the following formula:
r =
yx
xy
ss
s
=
11
1
22
n
yy
n
xx
n
yyxx
ii
ii
=
22
yyxx
yyxx
ii
ii
= 0.553
where:
sxy = covariance (measure of the of the linear association between two variables)
sx = standard deviation of x
Sy = standard deviation of y
13. 12
6. Further Analysis
The researcher would like to conduct multiple regression analysis on the dataset to model the
form of the relationship on pupil drinking behaviour (dependent) and other independent
variables. Besides that, factor analysis could be applied to identify and confirm the
dimensionality of existing scales. Furthermore, the researcher would also like to conduct
cluster analysis on the dataset to segment student drinkers base on different characteristics.
These further analysis allows researchers to gain insight on student drinkers so that effective
actions could be taken to curb alcohol consumption among young pupils.
7. Limitation
The use of stratified sampling in this research could lead to sampling bias as stratas are difficult
to identify. In addition, in this research, the stratas were divided according to school type
(comprehensive, secondary modern, grammar and private). One key assumption of stratified
sampling is that the stratas are homogenous. However, there is a possibility that the stratas are
heterogenous. For example, in private schools there are single and mixed gender schools, also
there are international schools.
Another limitation of this research was questionnaire administartion. Students were given
paper copy of the questionnaire and were asked to complete the questionnaire within 60
minutes, under exam condition with teacher supervision. The use of paper questionnaire leads
to many missing values as students did not answer all questions. Besides that, the duration of
questionnaire was too long, students might lose interest and not complete the questionnaire.
Lastly, the presence of teacher supervision might pressure students to provide socially desirable
answers.
Future researcher could conduct computer adminstrated questionnaires with skip logic and
compulsary questions. This would reduce the number of missing values. The duration of the
questionnaire could be shortened to around 20 minutes to prevent students from losing interest.
Lastly, there would be no teacher supervision to avoid any pschological pressure on students.
8. Conclusion
In conclusion, this report has presented descriptive and summary statistic about the sample. It
also conducted hypothesis test on population proportion of UK pupils who drank alcohol at
least one. Chi-square test was also conducted to ascertain if peer pressure and drinking
frequency were associated. Correlation test was conducted to measure the strength of type of
relationship between family attitude and drinking frequency. Lastly, this report also mentioned
on further analysis and limitation of dataset.
15. 14
Appendix 1. Survey questions
Are you a boy or a girl?
Boy
Girl
Which year are you at school?
Year 7
Year 8
Year 9
Year 10
Year 11
How old are you now?
_______Years old
Have you ever had a proper alcoholic drink?
Yes
No
How often do you usually have an alcoholic dink in a month?
0-3 times
4-7 times
8-11 times
12-15 times
16-19 times
20-23 times
24-27 times (7)
28-31 times (8)
How do your parents/guardian feel about you drinking alcohol?
They won’t like me to drink alcohol at all (1)
They don’t like but allow me to drink limited amount
They won’t mind as long as I don’t drink too much
They would let me drink as much as I like
16. 15
Write down the number of pints, half pints, large and small cans or bottles of alcohol that you
have consumed in the past 7 day?
_____Pints
_____Half pints
_____Large can
_____Smallcan
_____bottle
I drink due to peer pressure
Yes
No
17. 16
Appendix 2. Cross Tab
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
People my age drink
because ofpressure from
friends * (D) Usual drinking
frequency (8 cat)
6981 92.0% 608 8.0% 7589 100.0%
18. People my age drink because of pressure from friends * (D) Usual drinking frequency (8 cat) Crosstabulation
(D) Usual drinking frequency (8 cat) Total
Almost
every day
About twice
a week
About once
a week
About once a
fortnight
About once
a month
A few times
a year
Never
drinks now
Never had
a drink
People my age drink because
of pressure from friends
True
Count 10 64 96 199 267 843 133 2599 4211
Expected
Count
10.3 103.8 146.6 250.3 313.1 822.2 121.2 2443.6 4211.0
False
Count 7 108 147 216 252 520 68 1452 2770
Expected
Count
6.7 68.2 96.4 164.7 205.9 540.8 79.8 1607.4 2770.0
Total
Count 17 172 243 415 519 1363 201 4051 6981
Expected
Count
17.0 172.0 243.0 415.0 519.0 1363.0 201.0 4051.0 6981.0