This document provides an overview of key statistical concepts for non-statisticians. It defines different types of data and variables, different ways of displaying and summarizing data, measures of central tendency and dispersion, normal and non-normal distributions, and different types of clinical research studies. The goal is to introduce basic statistical concepts in an accessible way for those without a statistics background.
QuestionWhich of the following data sets is most likel.docxcatheryncouper
Question
Which of the following data sets is most likely to be normally distributed? For other choices, explain why you believe they would not follow a normal distribution.
The hand span (measured from the tip of the thumb to the tip of the extended 5th finger) of a random sample of high school seniors.
The annual salaries of all employees of a large shipping company
The annual salaries of a random sample of 50 CEOs of major companies (25 men and 25 women)
The dates of 100 pennies taken from a cash drawer in a convenience store
Question
Assume than the mean weight of 1 year old girls in the US is normally distributed with a mean value of 9.5 kg and standard deviation of 1.1. Without using a calculator (use the empirical rule 68 %, 95 %, 99%), estimate the percentage of 1 year old girls in the US that meet the following conditions. Draw a sketch and shade the proper region for each problem…
Less than 8.1 kg
Between 7.3 and 11.7 kg.
More than 12.8 kg.
Question
The grades on a marketing research course midterm are normally distributed with a mean (81) and standard deviation (6.3) . Calculate the z score for each of the following exam grades. Draw and label a sketch for each example.
65
83
93
100
Question
The grades on a marketing research course midterm are normally distributed with a mean (81) and standard deviation (6.3) . Calculate the z score for each of the following exam grades. Draw and label a sketch for each example.
65
83
93
100
Question…
What is the relative frequency of observations below 1.18? That is, find the relative frequency of the event Z < 1.18.
z .00 .01 ... .08 .09
0.0 .5000 .5040 ... .5319 .5359
0.1 .5398 .5438 ... .5714 .5753
... ... ... ... ... ...
1.0 .8413 .8438 ... .8599 .8621
1.1 .8643 .8665 ... .8810 8830
1.2 .8849 .8869 ... .8997 .9015
... ... ... ... ... ...
Question
Find the value z such that the event Z > z has relative frequency 0.80.
Question
For borrowers with good credits the mean debt for revolving and installment accounts is $ 15, 015. Assume the standard deviation is $3,540 and that debt amounts are normally distributed.
What is the probability that the debt for a borrower with good credit is more than $ 18,000.
Question
The average stock price for companies making up the S&P 500 is $30, and the standard deviation is $ 8.20. Assume the stock prices are normally distributed.
How high does a stock price have to be to put a company in the top 10 % … ?
Question
The scores on a statewide geometry exam were normally distributed with μ=72 and σ=8. What fraction of test-takers had a grade between 70 and 72 on the exam? Use the cumulative z-table provided below.
z. 00 .01 .02. 03. 04. 05. 06. 07 .08 .09
0.00. 50000 .50400 .50800 .51200 .51600 .51990 .52390 .52790 .53190 .5359
0.10. 53980 .54380 .54780 .55170 .55570 .55960 .56360 .56750 .57140 .5753
0.20. 57930 .58320 .58710 .59100 .59480 .59870 .60260 .60640 .61 ...
Segunda parte del Curso de Perfeccionamiento Profesional no Conducente a Grado Académico: Inglés Técnico para Profesionales de Ciencias de la Salud. DEPARTAMENTO ADMINISTRATIVO SOCIAL. Escuela de Enfermería. ULA. Mérida. Venezuela. Se oferta en la modalidad presencial de 3 ó 4 unidades crédito y los costos son solidarios y dependen de la zona del país que lo solicite.
El inglés técnico se basa en el tipo de vocabulario que va a manejar y el objetivo para el que va a estudiar inglés. En general en inglés técnico se busca poder comprender textos, y principalmente, textos técnicos de las disciplinas de salud en este caso que esté buscando, por ejemplo, si estas estudiando algo que tenga que ver con Medicina o Enfermería, empezara a ver nombres de enfermedades, enfoques epidemiológicos, entre otros. A diferencia del inglés normal que es mayormente comunicación diaria y gramática.
Durante las sesiones de aprendizaje se presentan las nociones generales acerca de la gramática de escritura inglesa y su transferencia en nuestra lengua española. En este módulo, se inicia la experiencia práctica eligiendo textos para observar los elementos facilitados.
Seguidamente, los participantes las ideas que se encuentran alrededor de fuentes en línea para profundizar en el aprendizaje en materia de inglés técnico.
The steps to calculate variance are:
1) Find the mean (Y-bar) of the data set. For Class A, Y-bar = 110.54
2) For each data point, calculate the deviation from the mean:
Data Point - Y-bar
102 - 110.54 = -8.54
115 - 110.54 = 4.46
3) Square each deviation to make all values positive
(-8.54)2 = 72.9116
(4.46)2 = 19.8516
4) Calculate the average of the squared deviations by summing them and dividing by the sample size (n-1)
5) The result is the variance.
So for
The document discusses key concepts related to probability and statistics, including:
- Probability is a number that reflects the likelihood of an event occurring, ranging from 0 to 1.
- Standard deviation measures how spread out values are from the mean.
- The normal distribution is a bell-shaped curve used to model naturally occurring phenomena.
- The t-distribution is similar to the normal distribution but with heavier tails, used when sample sizes are small.
- The normal probability curve is a graphical representation of the normal distribution, and is used to determine probabilities and percentiles.
This document discusses various statistical concepts and their applications in clinical laboratories. It defines descriptive statistics, statistical analysis, measures of central tendency (mean, median, mode), measures of variation (variance, standard deviation), probability distributions (binomial, Gaussian, Poisson), and statistical tests (t-test, chi-square, F-test). It provides examples of how these statistical methods are used to monitor laboratory test performance, interpret results, and compare different laboratory instruments and methods.
This document provides an overview of different types of variables and methods for summarizing clinical data, including descriptive statistics. It discusses categorical variables like gender and ordinal variables like disease staging. For continuous variables it explains measures of central tendency like mean, median and mode, and measures of variation like range, standard deviation, and interquartile range. Graphs for summarizing univariate data are also covered, such as bar charts for categorical variables and histograms and box plots for continuous variables.
QuestionWhich of the following data sets is most likel.docxcatheryncouper
Question
Which of the following data sets is most likely to be normally distributed? For other choices, explain why you believe they would not follow a normal distribution.
The hand span (measured from the tip of the thumb to the tip of the extended 5th finger) of a random sample of high school seniors.
The annual salaries of all employees of a large shipping company
The annual salaries of a random sample of 50 CEOs of major companies (25 men and 25 women)
The dates of 100 pennies taken from a cash drawer in a convenience store
Question
Assume than the mean weight of 1 year old girls in the US is normally distributed with a mean value of 9.5 kg and standard deviation of 1.1. Without using a calculator (use the empirical rule 68 %, 95 %, 99%), estimate the percentage of 1 year old girls in the US that meet the following conditions. Draw a sketch and shade the proper region for each problem…
Less than 8.1 kg
Between 7.3 and 11.7 kg.
More than 12.8 kg.
Question
The grades on a marketing research course midterm are normally distributed with a mean (81) and standard deviation (6.3) . Calculate the z score for each of the following exam grades. Draw and label a sketch for each example.
65
83
93
100
Question
The grades on a marketing research course midterm are normally distributed with a mean (81) and standard deviation (6.3) . Calculate the z score for each of the following exam grades. Draw and label a sketch for each example.
65
83
93
100
Question…
What is the relative frequency of observations below 1.18? That is, find the relative frequency of the event Z < 1.18.
z .00 .01 ... .08 .09
0.0 .5000 .5040 ... .5319 .5359
0.1 .5398 .5438 ... .5714 .5753
... ... ... ... ... ...
1.0 .8413 .8438 ... .8599 .8621
1.1 .8643 .8665 ... .8810 8830
1.2 .8849 .8869 ... .8997 .9015
... ... ... ... ... ...
Question
Find the value z such that the event Z > z has relative frequency 0.80.
Question
For borrowers with good credits the mean debt for revolving and installment accounts is $ 15, 015. Assume the standard deviation is $3,540 and that debt amounts are normally distributed.
What is the probability that the debt for a borrower with good credit is more than $ 18,000.
Question
The average stock price for companies making up the S&P 500 is $30, and the standard deviation is $ 8.20. Assume the stock prices are normally distributed.
How high does a stock price have to be to put a company in the top 10 % … ?
Question
The scores on a statewide geometry exam were normally distributed with μ=72 and σ=8. What fraction of test-takers had a grade between 70 and 72 on the exam? Use the cumulative z-table provided below.
z. 00 .01 .02. 03. 04. 05. 06. 07 .08 .09
0.00. 50000 .50400 .50800 .51200 .51600 .51990 .52390 .52790 .53190 .5359
0.10. 53980 .54380 .54780 .55170 .55570 .55960 .56360 .56750 .57140 .5753
0.20. 57930 .58320 .58710 .59100 .59480 .59870 .60260 .60640 .61 ...
Segunda parte del Curso de Perfeccionamiento Profesional no Conducente a Grado Académico: Inglés Técnico para Profesionales de Ciencias de la Salud. DEPARTAMENTO ADMINISTRATIVO SOCIAL. Escuela de Enfermería. ULA. Mérida. Venezuela. Se oferta en la modalidad presencial de 3 ó 4 unidades crédito y los costos son solidarios y dependen de la zona del país que lo solicite.
El inglés técnico se basa en el tipo de vocabulario que va a manejar y el objetivo para el que va a estudiar inglés. En general en inglés técnico se busca poder comprender textos, y principalmente, textos técnicos de las disciplinas de salud en este caso que esté buscando, por ejemplo, si estas estudiando algo que tenga que ver con Medicina o Enfermería, empezara a ver nombres de enfermedades, enfoques epidemiológicos, entre otros. A diferencia del inglés normal que es mayormente comunicación diaria y gramática.
Durante las sesiones de aprendizaje se presentan las nociones generales acerca de la gramática de escritura inglesa y su transferencia en nuestra lengua española. En este módulo, se inicia la experiencia práctica eligiendo textos para observar los elementos facilitados.
Seguidamente, los participantes las ideas que se encuentran alrededor de fuentes en línea para profundizar en el aprendizaje en materia de inglés técnico.
The steps to calculate variance are:
1) Find the mean (Y-bar) of the data set. For Class A, Y-bar = 110.54
2) For each data point, calculate the deviation from the mean:
Data Point - Y-bar
102 - 110.54 = -8.54
115 - 110.54 = 4.46
3) Square each deviation to make all values positive
(-8.54)2 = 72.9116
(4.46)2 = 19.8516
4) Calculate the average of the squared deviations by summing them and dividing by the sample size (n-1)
5) The result is the variance.
So for
The document discusses key concepts related to probability and statistics, including:
- Probability is a number that reflects the likelihood of an event occurring, ranging from 0 to 1.
- Standard deviation measures how spread out values are from the mean.
- The normal distribution is a bell-shaped curve used to model naturally occurring phenomena.
- The t-distribution is similar to the normal distribution but with heavier tails, used when sample sizes are small.
- The normal probability curve is a graphical representation of the normal distribution, and is used to determine probabilities and percentiles.
This document discusses various statistical concepts and their applications in clinical laboratories. It defines descriptive statistics, statistical analysis, measures of central tendency (mean, median, mode), measures of variation (variance, standard deviation), probability distributions (binomial, Gaussian, Poisson), and statistical tests (t-test, chi-square, F-test). It provides examples of how these statistical methods are used to monitor laboratory test performance, interpret results, and compare different laboratory instruments and methods.
This document provides an overview of different types of variables and methods for summarizing clinical data, including descriptive statistics. It discusses categorical variables like gender and ordinal variables like disease staging. For continuous variables it explains measures of central tendency like mean, median and mode, and measures of variation like range, standard deviation, and interquartile range. Graphs for summarizing univariate data are also covered, such as bar charts for categorical variables and histograms and box plots for continuous variables.
This document provides an overview of basic biostatistics and descriptive statistics. Biostatistics analyzes data from biological and medical sciences. Descriptive statistics are used to organize, summarize, and describe data through measures of central tendency like mean, median, and mode and measures of variation like range, standard deviation, and variance. These statistics provide a simple summary of populations and samples to analyze health status, support scientific research, and interpret clinical data.
Measures of disease frequency include rates, ratios, and proportions. A ratio expresses the relation between two quantities where the numerator is not part of the denominator. A proportion indicates the relation of a part to the whole, with the numerator included in the denominator. A rate measures the occurrence of an event in a population during a time period. Other concepts discussed include incidence, prevalence, measures of central tendency (mean, median, mode), and measures of variation (range, standard deviation). Factors that can affect study outcomes include various types of biases such as selection, response, information, and confounding variables.
This document provides guidance on presenting results from research studies. It discusses how to describe categorical, dichotomous, and continuous variables using percentages, means, medians and other statistical measures. Key recommendations include focusing on effect sizes rather than just significance, using confidence intervals to indicate precision, and choosing appropriate statistical tests based on the type of data. Tables and figures are suggested for presenting results along with keeping the number of key findings limited to the most essential. Overall, the document offers best practices for clearly communicating the important results from a study.
The document provides an introduction to statistics concepts including central tendency, dispersion, probability, and random variables. It discusses different measures of central tendency like mean, median and mode. It also covers dispersion concepts like variance and standard deviation. The document introduces key probability concepts such as experiments, sample spaces, events, and conditional probability. It defines random variables and discusses discrete and continuous random variables.
This document discusses various measures of central tendency and variability used in statistics. It describes the three main measures of central tendency as the mode, median, and mean. For measures of variability, it defines concepts like range, variance, and standard deviation. The range is described as the highest score minus the lowest score and provides a simple measure of variation. Variance is defined as the mean of the squared deviations from the mean and standard deviation is the square root of the variance, providing a measure of how data points cluster around the mean. Examples are provided to demonstrate calculating each of these statistical measures.
This document provides an overview of quantitative data analysis techniques including data verification, frequency distributions, measures of central tendency and variability, and distributions. Key points:
- Data should be verified to ensure accuracy through techniques like ordering, frequency distributions, and scatterplots to identify outliers.
- Descriptive statistics like the mode, median, and mean are used to describe variables. The standard deviation measures variability.
- Distributions can be normal, positively skewed, or negatively skewed depending on where values are clustered. The normal distribution is symmetric and bell-shaped.
This document provides guidance on key principles for conducting rigorous statistical analysis and research. It discusses the importance of clearly articulating the story being told with the data through use of graphs and tables. Variables of interest, outcomes, and potential confounding factors should be identified. The generalizability and interestingness of results are important to consider. Prospective studies are preferable to retrospective studies, which require consideration of multiple factors to establish credibility. Multiple statistical tests on a single data set require adjustments to avoid inflated false positive rates. Data collection and coding should be done consistently to allow for proper analysis. Overall, the document emphasizes the need for thoughtful statistical methodology to ensure useful and meaningful results.
- The document discusses key concepts in descriptive statistics including types of distributions, measures of central tendency, and measures of dispersion.
- It covers normal, skewed, and other types of distributions. Measures of central tendency discussed are mean, median, and mode. Measures of dispersion covered are variance and standard deviation.
- The document uses examples and explanations to illustrate how to calculate and interpret these important statistical measures.
- The document discusses key concepts in descriptive statistics including types of distributions, measures of central tendency, and measures of dispersion.
- It covers normal, skewed, and other types of distributions. Measures of central tendency discussed are mean, median, and mode. Measures of dispersion covered are variance and standard deviation.
- The document uses examples and explanations to illustrate how to calculate and interpret these important statistical measures.
The degrees of freedom for this problem is 5 (number of categories - 1). Looking up the critical value for chi-squared with 5 degrees of freedom and a confidence level of 95% gives us 11.07. Since the calculated chi-squared value of 8 is less than the critical value of 11.07, we would fail to reject the null hypothesis that the dice rolls are random at the 95% confidence level.
The document discusses statistical analysis and concepts such as standard deviation, normal distribution, and t-tests. It provides examples of how to calculate and interpret standard deviation to understand the variation in data compared to the mean. It also explains how a t-test can be used to determine if there is a statistically significant difference between the means of two samples by taking into account the means, standard deviations, and population sizes.
This document provides an overview of key concepts in statistics, including:
1. Statistics involves the systematic presentation of numerical data to minimize erroneous conclusions when information is incomplete. Induction and deduction are two main methods of assessment, and samples are used to make reasonable conclusions about whole populations.
2. For a sample to be representative, it should be randomly selected, large in size, and stratified if necessary to account for subgroups. Random allocation in experiments helps ensure intervention and control groups are similar.
3. Common statistical terms are defined, including mean, median, mode, range, and standard deviation. Normal distribution and confidence intervals are also explained.
The document discusses data distribution and presentation. It covers topics like the normal distribution curve, calculating probabilities using the standardized normal distribution table, and presenting data through tables and graphs. Specifically, it provides details on creating frequency distribution tables for qualitative and quantitative variables. It also discusses cross tabulation and different types of graphs like pie charts, simple bar charts, and multiple bar charts for presenting categorical data.
Overview of different statistical tests used in epidemiologicalshefali jain
This document provides an overview of different statistical tests used in epidemiological studies and their applications. It discusses topics such as data types (quantitative, categorical), variables, statistics, null and alternative hypotheses, errors in significance testing, and choices between parametric and nonparametric tests. The key information provided includes classifications of variable types, definitions of common statistics, explanations of hypotheses testing and p-values, and guidance on selecting appropriate tests based on the scale and distribution of the data.
Hcai 5220 lecture notes on campus sessions fall 11(2)Twene Peter
This chapter discusses descriptive statistics such as measures of central tendency (mean, median, mode), measures of variation (range, variance, standard deviation, coefficient of variation), and the shape of distributions (skewness). It also covers the normal distribution and how to calculate probabilities using the normal distribution. Key points include:
1) The mean, median, and mode are measures of central tendency while the range, variance, standard deviation, and coefficient of variation measure the spread or variation of data.
2) The normal distribution is a bell-shaped symmetric distribution that is useful in inferential statistics.
3) Probabilities using the normal distribution can be calculated by transforming the data into standard normal variables (Z
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.
This document discusses key concepts in statistical analysis:
1) Error bars represent the variability in data and can show the range or standard deviation. The standard deviation summarizes how data values are spread around the mean, with 68% of values within one standard deviation.
2) The standard deviation is useful for comparing means and spreads between samples. Larger differences in means and standard deviations between samples indicate they are less likely from the same population.
3) A t-test measures the overlap between two data sets and determines if their differences are statistically significant or likely due to chance. A significance level of 5% is commonly used, below which the null hypothesis that sets are the same is rejected.
Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterpri...cambridgeWD
Clinical trials and health outcomes research differ in important ways that impact statistical modeling approaches. Clinical trials typically use homogeneous samples and focus on a single endpoint, while health outcomes data is heterogeneous with multiple endpoints. Predictive modeling techniques used in health outcomes research, like those in SAS Enterprise Miner, are better suited than traditional methods as they can handle complex real-world data without strong assumptions and more accurately predict rare events. Validation of models on separate test data is also important for generalizing results.
Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterpri...cambridgeWD
This document discusses the differences between clinical trials and health outcomes research. Clinical trials use homogeneous samples, surrogate endpoints, and focus on a single outcome. They are also typically underpowered for rare events. Health outcomes research uses heterogeneous data from the general population to examine multiple real endpoints simultaneously. It has larger samples and data that allow analysis of rare occurrences. Predictive modeling is better suited than traditional statistical methods for analyzing heterogeneous health outcomes data due to relaxed assumptions like normality.
Memory. Theories of memory . Forgetting pptSnehamurali18
(1) The document discusses various aspects of forgetting and memory, including what is forgotten over time, how forgetting occurs, and theories that attempt to explain forgetting.
(2) It describes how memories of faces, languages, and skills can be retained very long-term in a "permastore", while identification details are more likely to be forgotten.
(3) Forgetting can be due to memory decay over time, interference from new memories that are encoded, or a lack of memory consolidation being completed. Context mismatch between encoding and retrieval can also lead to forgetting.
Chi square test social research refer.pptSnehamurali18
This document discusses various statistical tests, including parametric tests that require normally distributed data like t-tests and ANOVA, non-parametric tests that don't require normality like the Mann-Whitney U test, and the chi-square test. It explains that chi-square is used to determine if there is a relationship between two categorical variables by comparing observed and expected frequencies in a contingency table. It provides steps for conducting a chi-square test including stating hypotheses, calculating expected values, determining degrees of freedom, finding the test statistic, and interpreting results. Two examples of applying chi-square to test associations between disease prevalence and other factors are also presented.
This document provides an overview of basic biostatistics and descriptive statistics. Biostatistics analyzes data from biological and medical sciences. Descriptive statistics are used to organize, summarize, and describe data through measures of central tendency like mean, median, and mode and measures of variation like range, standard deviation, and variance. These statistics provide a simple summary of populations and samples to analyze health status, support scientific research, and interpret clinical data.
Measures of disease frequency include rates, ratios, and proportions. A ratio expresses the relation between two quantities where the numerator is not part of the denominator. A proportion indicates the relation of a part to the whole, with the numerator included in the denominator. A rate measures the occurrence of an event in a population during a time period. Other concepts discussed include incidence, prevalence, measures of central tendency (mean, median, mode), and measures of variation (range, standard deviation). Factors that can affect study outcomes include various types of biases such as selection, response, information, and confounding variables.
This document provides guidance on presenting results from research studies. It discusses how to describe categorical, dichotomous, and continuous variables using percentages, means, medians and other statistical measures. Key recommendations include focusing on effect sizes rather than just significance, using confidence intervals to indicate precision, and choosing appropriate statistical tests based on the type of data. Tables and figures are suggested for presenting results along with keeping the number of key findings limited to the most essential. Overall, the document offers best practices for clearly communicating the important results from a study.
The document provides an introduction to statistics concepts including central tendency, dispersion, probability, and random variables. It discusses different measures of central tendency like mean, median and mode. It also covers dispersion concepts like variance and standard deviation. The document introduces key probability concepts such as experiments, sample spaces, events, and conditional probability. It defines random variables and discusses discrete and continuous random variables.
This document discusses various measures of central tendency and variability used in statistics. It describes the three main measures of central tendency as the mode, median, and mean. For measures of variability, it defines concepts like range, variance, and standard deviation. The range is described as the highest score minus the lowest score and provides a simple measure of variation. Variance is defined as the mean of the squared deviations from the mean and standard deviation is the square root of the variance, providing a measure of how data points cluster around the mean. Examples are provided to demonstrate calculating each of these statistical measures.
This document provides an overview of quantitative data analysis techniques including data verification, frequency distributions, measures of central tendency and variability, and distributions. Key points:
- Data should be verified to ensure accuracy through techniques like ordering, frequency distributions, and scatterplots to identify outliers.
- Descriptive statistics like the mode, median, and mean are used to describe variables. The standard deviation measures variability.
- Distributions can be normal, positively skewed, or negatively skewed depending on where values are clustered. The normal distribution is symmetric and bell-shaped.
This document provides guidance on key principles for conducting rigorous statistical analysis and research. It discusses the importance of clearly articulating the story being told with the data through use of graphs and tables. Variables of interest, outcomes, and potential confounding factors should be identified. The generalizability and interestingness of results are important to consider. Prospective studies are preferable to retrospective studies, which require consideration of multiple factors to establish credibility. Multiple statistical tests on a single data set require adjustments to avoid inflated false positive rates. Data collection and coding should be done consistently to allow for proper analysis. Overall, the document emphasizes the need for thoughtful statistical methodology to ensure useful and meaningful results.
- The document discusses key concepts in descriptive statistics including types of distributions, measures of central tendency, and measures of dispersion.
- It covers normal, skewed, and other types of distributions. Measures of central tendency discussed are mean, median, and mode. Measures of dispersion covered are variance and standard deviation.
- The document uses examples and explanations to illustrate how to calculate and interpret these important statistical measures.
- The document discusses key concepts in descriptive statistics including types of distributions, measures of central tendency, and measures of dispersion.
- It covers normal, skewed, and other types of distributions. Measures of central tendency discussed are mean, median, and mode. Measures of dispersion covered are variance and standard deviation.
- The document uses examples and explanations to illustrate how to calculate and interpret these important statistical measures.
The degrees of freedom for this problem is 5 (number of categories - 1). Looking up the critical value for chi-squared with 5 degrees of freedom and a confidence level of 95% gives us 11.07. Since the calculated chi-squared value of 8 is less than the critical value of 11.07, we would fail to reject the null hypothesis that the dice rolls are random at the 95% confidence level.
The document discusses statistical analysis and concepts such as standard deviation, normal distribution, and t-tests. It provides examples of how to calculate and interpret standard deviation to understand the variation in data compared to the mean. It also explains how a t-test can be used to determine if there is a statistically significant difference between the means of two samples by taking into account the means, standard deviations, and population sizes.
This document provides an overview of key concepts in statistics, including:
1. Statistics involves the systematic presentation of numerical data to minimize erroneous conclusions when information is incomplete. Induction and deduction are two main methods of assessment, and samples are used to make reasonable conclusions about whole populations.
2. For a sample to be representative, it should be randomly selected, large in size, and stratified if necessary to account for subgroups. Random allocation in experiments helps ensure intervention and control groups are similar.
3. Common statistical terms are defined, including mean, median, mode, range, and standard deviation. Normal distribution and confidence intervals are also explained.
The document discusses data distribution and presentation. It covers topics like the normal distribution curve, calculating probabilities using the standardized normal distribution table, and presenting data through tables and graphs. Specifically, it provides details on creating frequency distribution tables for qualitative and quantitative variables. It also discusses cross tabulation and different types of graphs like pie charts, simple bar charts, and multiple bar charts for presenting categorical data.
Overview of different statistical tests used in epidemiologicalshefali jain
This document provides an overview of different statistical tests used in epidemiological studies and their applications. It discusses topics such as data types (quantitative, categorical), variables, statistics, null and alternative hypotheses, errors in significance testing, and choices between parametric and nonparametric tests. The key information provided includes classifications of variable types, definitions of common statistics, explanations of hypotheses testing and p-values, and guidance on selecting appropriate tests based on the scale and distribution of the data.
Hcai 5220 lecture notes on campus sessions fall 11(2)Twene Peter
This chapter discusses descriptive statistics such as measures of central tendency (mean, median, mode), measures of variation (range, variance, standard deviation, coefficient of variation), and the shape of distributions (skewness). It also covers the normal distribution and how to calculate probabilities using the normal distribution. Key points include:
1) The mean, median, and mode are measures of central tendency while the range, variance, standard deviation, and coefficient of variation measure the spread or variation of data.
2) The normal distribution is a bell-shaped symmetric distribution that is useful in inferential statistics.
3) Probabilities using the normal distribution can be calculated by transforming the data into standard normal variables (Z
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.
This document discusses key concepts in statistical analysis:
1) Error bars represent the variability in data and can show the range or standard deviation. The standard deviation summarizes how data values are spread around the mean, with 68% of values within one standard deviation.
2) The standard deviation is useful for comparing means and spreads between samples. Larger differences in means and standard deviations between samples indicate they are less likely from the same population.
3) A t-test measures the overlap between two data sets and determines if their differences are statistically significant or likely due to chance. A significance level of 5% is commonly used, below which the null hypothesis that sets are the same is rejected.
Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterpri...cambridgeWD
Clinical trials and health outcomes research differ in important ways that impact statistical modeling approaches. Clinical trials typically use homogeneous samples and focus on a single endpoint, while health outcomes data is heterogeneous with multiple endpoints. Predictive modeling techniques used in health outcomes research, like those in SAS Enterprise Miner, are better suited than traditional methods as they can handle complex real-world data without strong assumptions and more accurately predict rare events. Validation of models on separate test data is also important for generalizing results.
Clinical Trials Versus Health Outcomes Research: SAS/STAT Versus SAS Enterpri...cambridgeWD
This document discusses the differences between clinical trials and health outcomes research. Clinical trials use homogeneous samples, surrogate endpoints, and focus on a single outcome. They are also typically underpowered for rare events. Health outcomes research uses heterogeneous data from the general population to examine multiple real endpoints simultaneously. It has larger samples and data that allow analysis of rare occurrences. Predictive modeling is better suited than traditional statistical methods for analyzing heterogeneous health outcomes data due to relaxed assumptions like normality.
Memory. Theories of memory . Forgetting pptSnehamurali18
(1) The document discusses various aspects of forgetting and memory, including what is forgotten over time, how forgetting occurs, and theories that attempt to explain forgetting.
(2) It describes how memories of faces, languages, and skills can be retained very long-term in a "permastore", while identification details are more likely to be forgotten.
(3) Forgetting can be due to memory decay over time, interference from new memories that are encoded, or a lack of memory consolidation being completed. Context mismatch between encoding and retrieval can also lead to forgetting.
Chi square test social research refer.pptSnehamurali18
This document discusses various statistical tests, including parametric tests that require normally distributed data like t-tests and ANOVA, non-parametric tests that don't require normality like the Mann-Whitney U test, and the chi-square test. It explains that chi-square is used to determine if there is a relationship between two categorical variables by comparing observed and expected frequencies in a contingency table. It provides steps for conducting a chi-square test including stating hypotheses, calculating expected values, determining degrees of freedom, finding the test statistic, and interpreting results. Two examples of applying chi-square to test associations between disease prevalence and other factors are also presented.
Salient features of mental health care Act-draft 1 ,.pptxSnehamurali18
The document provides an overview of the Mental Healthcare Act of 2017 in India. Some key points:
- It defines mental illness and aims to protect the rights of those suffering from mental illness.
- It establishes a Central and State Mental Health Authority to regulate mental health institutions and practitioners.
- It sets up Mental Health Review Boards to safeguard the rights of those with mental illness and manage advance directives.
- Some rights established include right to confidentiality, free treatment if homeless or below poverty line, and making an advance directive stating treatment preferences.
- Suicide is decriminalized and restraints and electroconvulsive therapy are regulated. Implementation challenges include lack of funding and consideration of local
This document outlines several schools of family therapy, including psychodynamic, behavioral, strategic, Milan's systemic, and solution-focused approaches. It describes key concepts and therapeutic techniques for each approach. For example, behavioral family therapy applies principles of behaviorism to change family interactions, while strategic family therapy uses indirect techniques like reframing and paradoxical interventions. The document also discusses integrative approaches that combine concepts and strategies from different schools of family therapy.
The document discusses suicide and suicidal behavior from various perspectives including medical, social, biological and psychological models. It notes that suicide is a complex issue with multiple contributing factors including mental illness, physical illness, substance abuse, sociological factors and biological factors. It describes different types of suicidal behaviors and discusses warning signs. Risk factors mentioned include depression, substance abuse, medical illness, bereavement, humiliation and a family history of suicide. The document provides information on assessing suicide risk and approaches to prevention and intervention.
Suicide is defined as purposely ending one's own life. Common methods include bleeding, drowning, hanging, jumping from heights, explosions, firearms, poisoning, vehicular impact, and electrocution. Mental illnesses like depression and mood disorders, stress, substance abuse, unemployment, loss of self-esteem or reputation, economic debt, anxiety, and frustration can cause suicidal thoughts. Additional risk factors include a personal or family history of suicide attempts, physical or sexual abuse, social rejection, grief, domestic violence, harassment, chronic illness, and torture.
Suicide is defined as purposely ending one's own life. Common methods include bleeding, drowning, hanging, jumping from heights, explosions, firearms, poisoning, vehicular impact, and electrocution. Suicide is often caused by mental illness, stress, mood disorders, substance abuse, unemployment, loss of self-esteem, economic debt, anxiety, frustration, previous suicide attempts, abuse, family history, social rejection, grief, domestic violence, harassment, chronic illness, and other factors that decrease one's will to live. People who are at highest risk tend to have multiple risk factors contributing to their suicidal thoughts or behaviors.
This document discusses disability certification in psychiatry. It provides information on evaluating and assessing autism using the Indian Scale for Assessment of Autism (ISAA). The ISAA rates individuals on a scale from 1 to 5 across six domains to determine the severity of autism as mild, moderate or severe. It also discusses the Indian disability evaluation and assessment scale (IDEAS) for measuring psychiatric disability in conditions like schizophrenia, bipolar disorder, dementia and obsessive compulsive disorder. The IDEAS evaluates disability across four areas and provides a global disability score to determine eligibility for welfare benefits. The document also provides guidance on assessing and determining disability levels for individuals with mental retardation based on their intelligent quotient score.
This document discusses disability certification in psychiatry. It provides information on evaluating and assessing autism using the Indian Scale for Assessment of Autism (ISAA). The ISAA rates individuals on a scale from 1 to 5 across six domains to determine the severity of autism as mild, moderate or severe. It also discusses the Indian disability evaluation and assessment scale (IDEAS) for measuring psychiatric disability in conditions like schizophrenia, bipolar disorder, dementia and obsessive compulsive disorder. The IDEAS evaluates disability across four areas and provides a global disability score to determine eligibility for welfare benefits. The document also provides guidance on assessing and determining disability levels for individuals with mental retardation based on their intelligent quotient score.
Salient features of mental health care Act-final.pptxSnehamurali18
The document provides an overview of the Mental Healthcare Act of 2017 in India. It discusses the need for the act, its various chapters and contents, and salient features. Some key points include defining mental illness and treatment decisions, provisions around advance directives, nominated representatives, rights of those with mental illness, and responsibilities of government authorities. It also notes merits like a more pro-consumer approach, but flags implementation challenges and need for further strengthening of rehabilitation aspects.
Family counseling, also known as family therapy, works with families and couples to nurture change in relationships. It views problems as stemming from interactions between family members rather than individuals. Several theoretical frameworks have emerged, including systems theory influenced by cybernetics which focuses on communication patterns. By the 1960s, distinct schools had developed like structural family therapy, strategic therapy, and intergenerational therapy. Licensing requirements vary by location but often involve a master's degree and supervised clinical hours to become a licensed marriage and family therapist.
This document provides an overview of social casework as a primary method of social work. It discusses the objectives of social casework as understanding and solving internal client problems, strengthening ego power, remediating and preventing problems in social functioning. The key principles of social casework outlined are individualization, purposeful expression of feelings, controlled emotional involvement, acceptance, non-judgmental attitude, self-determination, and confidentiality. It also describes the components of a social casework setting as involving a client with a problem, a social service agency or department as the place, and a problem-solving process between the client and social worker.
This document discusses social work research and the scientific method. It defines social work research as the systematic investigation of problems in the field of social work. The purpose of social work research is to evaluate the effectiveness of interventions and treatments and to build theory to help social workers address problems. Social research and social work research are similar in their goal of promoting human welfare, but social work research specifically aims to gain knowledge to control or change human behavior. The scientific method is characterized by systematic observation, classification, and interpretation of data to accumulate reliable knowledge. It aims for objectivity, logical reasoning, and generalization of findings.
This document discusses several theories and approaches to case work practice, including psychosocial approach, problem solving approach, crisis intervention, behaviour modification, and functional approach. It provides details on each approach, including their origins, key principles, stages or phases of practice, assessment methods, treatment principles and goals. The psychosocial approach views the client in their social situation and addresses both interpersonal relationships and personality. Behaviour modification aims to increase desired behaviours and decrease undesired behaviours through techniques like positive reinforcement. The functional approach focuses on understanding the client's situation and using agency resources and the worker-client relationship to facilitate change.
This document provides an overview of several theories and approaches in social work case management practice, including psychosocial approach, functional approach, crisis intervention theory, and behavior modification. It describes the key principles, objectives, stages, and targets of each approach. The psychosocial approach emphasizes attention to both interpersonal relationships and personality systems. The functional approach focuses on understanding the client's situation and using agency functions and processes to facilitate change. Crisis intervention theory aims to restore functioning and coping capacity during times of stress.
This document discusses social work research and the scientific method. It defines social work research as the systematic investigation of problems in the field of social work. The purpose of social work research is to evaluate the effectiveness of interventions and treatments and to build theory to help social workers address problems. Social research and social work research are similar in their goal of promoting human welfare, but social work research specifically aims to gain knowledge to control or change human behavior. The scientific method is characterized by systematic observation, classification, and interpretation of data to accumulate reliable knowledge. It aims to discover facts objectively through logical reasoning and is free from any particular subject matter.
Family counseling aims to help families and couples experiencing problems by nurturing change through examining family relationships and dynamics. It views issues as related to systems of interaction rather than just individuals. Several theoretical frameworks have emerged, beginning in the 1940s-50s with psychoanalysis and social psychiatry influences. Key developments included Gregory Bateson's introduction of cybernetics and systems theory ideas, and the emergence of distinct schools in the 1960s focusing on experiential, intergenerational, psychodynamic, and behavioral approaches. The field has since diversified with integration of different theories and influences from individual psychotherapy.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. The following lecture has been approved for
University Undergraduate Students
This lecture may contain information, ideas, concepts and discursive anecdotes
that may be thought provoking and challenging
It is not intended for the content or delivery to cause offence
Any issues raised in the lecture may require the viewer to engage in further
thought, insight, reflection or critical evaluation
3. Keep it simple
“Some people hate the very name of statistics but.....their power of
dealing with complicated phenomena is extraordinary. They are the
only tools by which an opening can be cut through the formidable
thicket of difficulties that bars the path of those who pursue the science
of man.”
Sir Francis Galton, 1889
5. How Many Make a Sample?
“8 out of 10 owners who expressed a preference, said their cats
preferred it.”
How confident can we be about such statistics?
8 out of 10?
80 out of 100?
800 out of 1000?
80,000 out of 100,000?
6. Types of Data / Variables
Continuous Discrete
BP Children
Height Age last birthday
Weight colds in last year
Age
Ordinal Nominal
Grade of condition Sex
Positions 1st 2nd 3rd Hair colour
“Better- Same-Worse” Blood group
Height groups Eye colour
Age groups
7. Conversion & Re-classification
Easier to summarise Ordinal / Nominal data
Cut-off Points (who decides this?)
Allows Continuous variables to be changed into Nominal variables
BP > 90mmHg = Hypertensive
BP =< 90mmHg = Normotensive
Easier clinical decisions
Categorisation reduces quality of data
Statistical tests may be more “sensational”
Good for summaries Bad for “accuracy”
BMI
Obese vs Underweight
8. Types of statistics / analyses
DESCRIPTIVE STATISTICS Describing a phenomena
Frequencies How many…
Basic measurements Meters, seconds, cm3, IQ
INFERENTIAL STATISTICS Inferences about phenomena
Hypothesis Testing Proving or disproving theories
Confidence Intervals If sample relates to the larger population
Correlation Associations between phenomena
Significance testing e.g diet and health
12. Dispersion
Range Spread of data
Mean Arithmetic average
Median Location
Mode Frequency
SD Spread of data
about the mean
Range 50-112 mmHg
Mean 82mmHg Median 82mmHg Mode 82mmHg
SD ± 10mmHg
13. Dispersion
An individual score therefore possess a standard deviation (away from the
mean), which can be positive or negative
Depending on which side of the mean the score is
If add the positive and negative deviations together, it equals zero
(the positives and negatives cancel out)
central value (mean)
negative deviation positive deviation
14. 5’6” 5’7” 5’8” 5’9” 5’10” 5’11” 6’ 6’1” 6’2” 6’3” 6’4”
Range
1st 5th 25th 50th 75th 95th 99th
Dispersion
Range
The interval between the highest and lowest measures
Limited value as it involves the two most extreme (likely faulty) measures
Percentile
The value below / above which a particular percentage of values fall
(median is the 50th percentile)
e.g 5th percentile - 5% of values fall below it, 95% of values fall above it.
A series of percentiles (1st, 5th, 25th, 50th, 75th, 95, 99th) gives a good general
idea of the scatter and shape of the data
15. Standard Deviation
To get around this, we square each of the observations
Makes all the values positive (a minus times a minus….)
Then sum all those squared observations to calculate the mean
This gives the variance - where every observation is squared
Need to take the square root of the variance, to get the standard deviation
SD = Σ x2 – (Σ x)2 / N
(N – 1)
16. Non Normal Distribution
Some distributions fail to be symmetrical
If the tail on the left is longer than the right,
the distribution is negatively skewed (to the left)
If the tail on the right is longer than the left, the
distribution is positively skewed
(to the right)
Grouped Data
Normal Distribution
SD is useful because of the shape of many distributions of data.
Symmetrical, bell-shaped / normal / Gaussian distribution
17. central value (mean)
3 SD 2 SD 1 SD 0 SD 1 SD 2 SD 3 SD
Normal Distributions
Standard Normal Distribution has a mean of 0 and a standard deviation of 1
The total area under the curve amounts to 100% / unity of the observations
Proportions of observations within any given range can be obtained from the
distribution by using statistical tables of the standard normal distribution
95% of measurements / observations lie within 1.96 SD’s either side of the
mean
18. balls dropped through a
succession of metal pins…..
…..a normal distribution
of balls
do not have a normal
distribution here. Why?
Quincunx machine 1877
19. The distribution derived from the
quincunx is not perfect
It was only made from 18 balls
Normal & Non-normal distributions
20. 5’6” 5’7” 5’8” 5’9” 5’10” 5’11” 6’ 6’1” 6’2” 6’3” 6’4”
Height
%
of
population
Distributions
Sir Francis Galton (1822-1911) Alumni of Birmingham University
9 books and > 200 papers
Fingerprints, correlation of calculus, twins, neuropsychology, blood
transfusions, travel in undeveloped countries, criminality and meteorology)
Deeply concerned with improving standards of measurement
21. Normal & Non-normal distributions
Galton’s quincunx machine ran with hundreds of balls
a more “perfect” shaped normal distribution.
Obvious implications for the size of samples of populations used
The more lead shot runs through the quincunx machine, the smoother the
distribution
in the long run . . . . .
22. Exposed Controls T P
n=197 n=178
Age 45.5 48.9 2.19 0.07
(yrs) ( 9.4) ( 7.3)
I.Q 105 99 1.78 0.12
( 10.8) ( 8.7)
Speed 115.1 94.7 3.76 0.04
(ms) ( 13.4) ( 12.4)
Presentation of data
Table of means
23. Exposed Controls
Healthy 50 150 200
Unwell 147 28 175
197 178 375
Chi square (test of association) shows:
Chi square = 7.2 P = 0.02
Presentation of data
Category tables
25. 0
1000
2000
3000
4000
5000
6000
7000
1 2 3 4 5 6 7 8 9 10
User rating
Votes
Movie goers’ ratings for both movies
Vacation
Empire
Bar Charts
Some Real Data
A combination of distributions is acceptable to facilitate comparisons
26. With a scatter diagram, each
individual observation becomes a
point on the scatter plot, based on two
co-ordinates, measured on the
abscissa and the ordinate
Two perpendicular lines are drawn through the medians - dividing the plot into
quadrants
Each quadrant should outlie 25% of all observations
Correlation and Association
ordinate
abscissa
27. Correlation is a numerical expression between 1 and -1 (extending through all points
in between). Properly called the Correlation Coefficient.
A decimal measure of association (not necessarily causation) between variables
Correlation of 1
Maximal - any value
of one variable
precisely determines
the other. Perfect +ve
Correlation of -1 Any value of one
variable precisely determines the other,
but in an opposite direction to a
correlation of 1. As one value increases,
the other decreases. Perfect -ve
Correlation of 0 - No
relationship between
the variables. Totally
independent of each
other. “Nothing”
Correlation of 0.5 - Only a slight
relationship between the variables i.e
half of the variables can be predicted
by the other, the other half can’t.
Medium +ve
Correlations between 0 and 0.3 are weak
Correlations between 0.4 and 0.7 are moderate
Correlations between 0.8 and 1 are strong
Correlation and Association
28. Correlation is a numerical expression between 1 and -1 (extending through all points
in between).
Properly called the Correlation Coefficient.
A decimal measure of association (not necessarily causation) between variables
How can the above variables be correlated?
Correlation and Association
29. POPULATIONS
Can be mundane or extraordinary
SAMPLE
Must be representative
INTERNALY VALIDITY OF SAMPLE
Sometimes validity is more important than generalizability
SELECTION PROCEDURES
Random
Opportunistic
Conscriptive
Quota
Sampling Keywords
30. THEORETICAL
Developing, exploring, and testing ideas
EMPIRICAL
Based on observations and measurements of reality
NOMOTHETIC
Rules pertaining to the general case (nomos - Greek)
PROBABILISTIC
Based on probabilities
CAUSAL
How causes (treatments) effect the outcomes
Sampling Keywords
31. Clinical Research
Types of clinical research
Experimental vs. Observational
Longitudinal vs. Cross-sectional
Prospective vs. Retrospective
Longitudinal
Prospective
Experimental
Randomised Controlled Trial
Observational
Longitudinal Cross-sectional
Survey
Retrospective
Prospective
Case control studies
Cohort studies
32. patients
Treatment group
Control group
Outcome measured
Outcome measured
patients Outcome measured #1 Treatment Outcome measured #2
Experimental Designs
Between subjects studies
Within Subjects studies
33. prospectively measure risk factors
cohort end point measured
aetiology
prevalence
development
odds ratios
retrospectively measure risk factors
start point measured cases
aetiology
odds ratios
prevalence
development
Observational studies
Cohort (prospective)
Case-Control (retrospective)
34. Case-Control Study – Smoking & Cancer
“Cases” have Lung Cancer
“Controls” could be other hospital patients (other disease) or “normals”
Matched Cases & Controls for age & gender
Option of 2 Controls per Case
Smoking years of Lung Cancer cases and controls
(matched for age and sex)
Cases Controls
n=456 n=456
F P
Smoking years 13.75 6.12 7.5 0.04
(± 1.5) (± 2.1)
35. Cohort Study: Methods
Volunteers in 2 groups e.g. exposed vs non-exposed
All complete health survey every 12 months
End point at 5 years: groups compared for Health Status
Comparison of general health between users and non-users of mobile
phones
ill healthy
mobile phone user 292 108 400
non-phone user 89 313 402
381 421 802
36. Randomized Controlled Trials in GP & Primary Care
90% consultations take place in GP surgery
50 years old
Potential problems
2 Key areas: Recruitment Bias
Randomisation Bias
Over-focus on failings of RCTs
37. RCT Deficiencies
Trials too small
Trials too short
Poor quality
Poorly presented
Address wrong question
Methodological inadequacies
Inadequate measures of quality of life (changing)
Cost-data poorly presented
Ethical neglect
Patients given limited understanding
Poor trial management
Politics
Marketeering
Why still the dominant model?
38. Quantitative Data Summary
• What data is needed to answer the larger-scale research question
• Combination of quantitative and qualitative ?
• Cleaning, re-scoring, re-scaling, or re-formatting
• Measurement of both IV’s and DV’s is complex but can be simplified
• Binary measurement makes analysis easier but less meaningful
• Binary data needs clear parameters e.g exposed vs controls
• Collecting good quality data at source is vital
39. Quantitative Data Summary
• Continuous & Discrete data can also be converted into Binary data
• Normal distribution of participants / data points desirable
• Means - age, height, weight, BMI, IQ, attitudes
• Frequencies / Classifications - job type, sick vs. healthy, dead vs alive
• Means must be followed by Standard Deviation (SD or ±)
• Presentation of data must enhance understanding or be redundant
40. If you or anyone you know has been
affected by any of the issues
covered in this lecture, you may
need a statistician’s help:
www.statistics.gov.uk
41. Further Reading
Abbott, P., & Sapsford, R.J. (1988). Research methods for nurses and the
caring professions. Buckingham: Open University Press.
Altman, D.G. (1991). Designing Research. In D.G. Altman (ed.), Practical
Statistics For Medical Research (pp. 74-106). London: Chapman and Hall.
Bland, M. (1995). The design of experiments. In M. Bland (ed.), An introduction
to medical statistics (pp5-25). Oxford: Oxford Medical Publications.
Bowling, A. (1994). Measuring Health. Milton Keynes: Open University Press.
Daly, L.E., & Bourke, G.J. (2000). Epidemiological and clinical research
methods. In L.E. Daly & G.J. Bourke (eds.), Interpretation and uses of medical
statistics (pp. 143-201). Oxford: Blackwell Science Ltd.
Jackson, C.A. (2002). Research Design. In F. Gao-Smith & J. Smith (eds.), Key
Topics in Clinical Research. (pp. 31-39). Oxford: BIOS scientific Publications.
42. Further Reading
Jackson, C.A. (2002). Planning Health and Safety Research Projects. Health
and Safety at Work Special Report 62, (pp 1-16).
Jackson, C.A. (2003). Analyzing Statistical Data in Occupational Health
Research. Management of Health Risks Special Report 81, (pp. 2-8).
Kumar, R. (1999). Research Methodology: a step by step guide for beginners.
London: Sage.
Polit, D., & Hungler, B. (2003). Nursing research: Principles and methods (7th
ed.). Philadelphia: Lippincott, Williams & Wilkins.