Aron chpt 6 ed revised
•Download as PPT, PDF•
1 like•1,042 views
The document discusses distributions of sample means and how they relate to the population from which the samples are drawn. It states that the mean of a distribution of sample means is equal to the population mean. The variance of a distribution of sample means is equal to the population variance divided by the sample size. And the shape of a distribution of sample means approximates a normal distribution if each sample is at least 30 individuals or if the population is normally distributed.
Report
Share
Report
Share
![Hypothesis Tests with Means of Samples ,[object Object],Copyright © 2011 by Pearson Education, Inc. All rights reserved](https://image.slidesharecdn.com/aronchpt6edrevised-110208161424-phpapp02/85/Aron-chpt-6-ed-revised-1-320.jpg)
![The Distribution of Means ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2011 by Pearson Education, Inc. All rights reserved](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Aron chpt 6 ed
The document discusses distributions of sample means and how they relate to the population from which the samples are drawn. It states that the mean of a distribution of sample means is equal to the population mean. The variance of a distribution of sample means is equal to the population variance divided by the sample size. The distribution of sample means will be approximately normal in shape if each sample is large enough or if the population is normally distributed. It provides examples to illustrate these concepts.
Chapter3
This document provides examples and explanations of various graphical methods for describing data, including frequency distributions, bar charts, pie charts, stem-and-leaf diagrams, histograms, and cumulative relative frequency plots. It demonstrates how to construct these graphs using sample data on student weights, grades, ages, and other examples. The goal is to help readers understand different ways to visually represent data distributions and patterns.
Definition statistics -unknown
Statistics involves collecting, organizing, analyzing, and interpreting numerical data. Descriptive statistics describes data while inferential statistics draws conclusions from samples to populations. A study on coyotes concluded they were all dangerous based on ranchers' observations of those near ranches, but further study found most coyotes avoid sheep. Experiments aim to observe changes from treatments by comparing experimental and control groups, while observational studies make uncontrolled observations. Surveys can introduce biases from question wording, non-responses, and over-generalizing results.
Attempts to understand the results of machine learning models by Michael Tiernay
Abstract:- Sophisticated machine learning models (like GBMs and Neural Networks) produce better predictions than simpler models (like linear or logistic regression), but sophisticated models do not produce interpretable 'effects' that specify the relationship between predictors and and outcome. This is because sophisticated models can learn non-linear, interactive, or even higher level relationships between the predictors and outcome without being explicitly specified. In many settings it is often important to understand, as best as possible, how 'black box' models are producing because:1. If users do not understand how a prediction is being made, they may not trust the model/prediction enough to act upon the model's suggestions. Significant business value can be derived from understanding what drives an outcome of interest (e.g. purchase or churn) in order to make product changes to accentuate or minimize desired effects 3. Understanding how predictors relate to an outcome can inform subsequent feature generation that can improve a model's predictive power. This talk will discuss two methods that have been proposed to better understand machine learning models: simulating changes in input variables (the R ICEbox package) or building a simpler model locally around specific predictions (the Python LIME package).
Data Day 2012_Barboza_Data Storytelling
This document discusses reclaiming narratives around data through critical analysis of research methods, sources, and conclusions. It provides three examples of reclaiming stories: 1) Using the same data to show a disparity exists, despite conclusions of no disparity. 2) Presenting an alternative narrative, even if existing data seems reasonable. 3) Challenging conclusions that don't match one's intuition or experience. Specific strategies are discussed, like understanding different statistical measures, using multiple data sources, and addressing biases. Communities can reclaim their own narratives by gaining numeracy skills and understanding research limitations.
Binomial Probability Distributions
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 5: Discrete Probability Distribution
5.2 - Binomial Probability Distributions
presentation of data
1. The document discusses different methods for presenting data, including textual, tabular, and graphical methods.
2. It provides examples of how to prepare a stem-and-leaf plot, construct a frequency distribution table, and define key terms related to grouped and ungrouped data presentation.
3. The objectives are to describe how to prepare a stem-and-leaf plot, describe data textually, construct a frequency distribution table, create graphs, and interpret graphs and tables.
02 ch ken black solution
The chapter introduces various techniques for summarizing and depicting data through charts and graphs, including frequency distributions, histograms, frequency polygons, ogives, pie charts, stem-and-leaf plots, Pareto charts, and scatter plots. It emphasizes the importance of choosing graphical representations that clearly communicate trends in the data to intended audiences. Sample problems at the end of the chapter provide examples of constructing and interpreting various charts and graphs.
Recommended
Aron chpt 6 ed
The document discusses distributions of sample means and how they relate to the population from which the samples are drawn. It states that the mean of a distribution of sample means is equal to the population mean. The variance of a distribution of sample means is equal to the population variance divided by the sample size. The distribution of sample means will be approximately normal in shape if each sample is large enough or if the population is normally distributed. It provides examples to illustrate these concepts.
Chapter3
This document provides examples and explanations of various graphical methods for describing data, including frequency distributions, bar charts, pie charts, stem-and-leaf diagrams, histograms, and cumulative relative frequency plots. It demonstrates how to construct these graphs using sample data on student weights, grades, ages, and other examples. The goal is to help readers understand different ways to visually represent data distributions and patterns.
Definition statistics -unknown
Statistics involves collecting, organizing, analyzing, and interpreting numerical data. Descriptive statistics describes data while inferential statistics draws conclusions from samples to populations. A study on coyotes concluded they were all dangerous based on ranchers' observations of those near ranches, but further study found most coyotes avoid sheep. Experiments aim to observe changes from treatments by comparing experimental and control groups, while observational studies make uncontrolled observations. Surveys can introduce biases from question wording, non-responses, and over-generalizing results.
Attempts to understand the results of machine learning models by Michael Tiernay
Abstract:- Sophisticated machine learning models (like GBMs and Neural Networks) produce better predictions than simpler models (like linear or logistic regression), but sophisticated models do not produce interpretable 'effects' that specify the relationship between predictors and and outcome. This is because sophisticated models can learn non-linear, interactive, or even higher level relationships between the predictors and outcome without being explicitly specified. In many settings it is often important to understand, as best as possible, how 'black box' models are producing because:1. If users do not understand how a prediction is being made, they may not trust the model/prediction enough to act upon the model's suggestions. Significant business value can be derived from understanding what drives an outcome of interest (e.g. purchase or churn) in order to make product changes to accentuate or minimize desired effects 3. Understanding how predictors relate to an outcome can inform subsequent feature generation that can improve a model's predictive power. This talk will discuss two methods that have been proposed to better understand machine learning models: simulating changes in input variables (the R ICEbox package) or building a simpler model locally around specific predictions (the Python LIME package).
Data Day 2012_Barboza_Data Storytelling
This document discusses reclaiming narratives around data through critical analysis of research methods, sources, and conclusions. It provides three examples of reclaiming stories: 1) Using the same data to show a disparity exists, despite conclusions of no disparity. 2) Presenting an alternative narrative, even if existing data seems reasonable. 3) Challenging conclusions that don't match one's intuition or experience. Specific strategies are discussed, like understanding different statistical measures, using multiple data sources, and addressing biases. Communities can reclaim their own narratives by gaining numeracy skills and understanding research limitations.
Binomial Probability Distributions
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 5: Discrete Probability Distribution
5.2 - Binomial Probability Distributions
presentation of data
1. The document discusses different methods for presenting data, including textual, tabular, and graphical methods.
2. It provides examples of how to prepare a stem-and-leaf plot, construct a frequency distribution table, and define key terms related to grouped and ungrouped data presentation.
3. The objectives are to describe how to prepare a stem-and-leaf plot, describe data textually, construct a frequency distribution table, create graphs, and interpret graphs and tables.
02 ch ken black solution
The chapter introduces various techniques for summarizing and depicting data through charts and graphs, including frequency distributions, histograms, frequency polygons, ogives, pie charts, stem-and-leaf plots, Pareto charts, and scatter plots. It emphasizes the importance of choosing graphical representations that clearly communicate trends in the data to intended audiences. Sample problems at the end of the chapter provide examples of constructing and interpreting various charts and graphs.
19 ch ken black solution
This chapter discusses decision analysis and various techniques for decision making under certainty, uncertainty, and risk. It covers decision tables, decision trees, expected monetary value, utility theory, and revising probabilities based on sample information. The key techniques taught are maximax, maximin, Hurwicz criterion, minimax regret, expected value, and expected value of perfect and sample information. Decision analysis provides strategies to evaluate alternatives and make optimal decisions under different conditions.
Poisson Probability Distributions
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 5: Discrete Probability Distribution
5.3 - Poisson Probability Distributions
12 ch ken black solution
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.
2016-04-20-Moscow-EstimationError-V01
This document summarizes a study that used Monte Carlo simulations to assess the risk of obtaining biased estimates in regression analysis due to duplicated observations in survey data. The study found that the risk of obtaining wrong estimates increases with the number of duplicate records. Even a small number of duplicates can create considerable risk. Weighting duplicate observations by the inverse of their frequency was the most effective solution tested to minimize this risk. The study recommends that statistical agencies be aware of this problem and adopt solutions to correct for duplicates, though correcting data is not a trivial task.
04 ch ken black solution
This document provides an overview of the key concepts and objectives covered in Chapter 4 on probability. The chapter aims to help students understand the different ways of assigning probabilities and how to apply probability rules and laws to solve problems. It emphasizes that there are multiple valid approaches to probability problems. The chapter outlines includes topics like classical vs relative frequency vs subjective probabilities, probability rules like addition and multiplication, and conditional probability. It also provides sample problems and their solutions to illustrate the concepts.
Presentation of data mod 6
This document provides an overview of methods for presenting data, including textual, tabular, and graphical methods. It discusses topics such as ungrouped vs. grouped data, frequency distribution tables, stem-and-leaf plots, class boundaries, class midpoints, and class width. The objectives are to describe how to prepare a stem-and-leaf plot, describe data textually, construct a frequency distribution table, create graphs, and interpret graphs and tables. Examples are provided to illustrate these concepts and methods.
05 ch ken black solution
This document provides an outline and learning objectives for Chapter 5 of a statistics textbook on discrete distributions. The chapter will:
1. Distinguish between discrete and continuous random variables and distributions.
2. Explain how to calculate the mean and variance of discrete distributions.
3. Cover the binomial distribution and how to solve problems using it.
4. Cover the Poisson distribution and how to solve problems using it.
5. Explain how to approximate binomial problems with the Poisson distribution.
6. Cover the hypergeometric distribution and how to solve problems using it.
17 ch ken black solution
This chapter discusses nonparametric statistics including the runs test, Mann-Whitney U test, Wilcoxon matched-pairs signed rank test, Kruskal-Wallis test, Friedman test, and Spearman's rank correlation. These tests are nonparametric alternatives to common parametric tests that do not require the assumptions of normality or equal variances. The chapter provides examples of how to perform and interpret each test.
06 ch ken black solution
This chapter introduces three continuous probability distributions: the uniform, normal, and exponential distributions. It focuses on the normal distribution and how to solve various problems using it, including approximating binomial distributions with the normal. It also covers using the normal distribution to find probabilities, the correction for continuity when approximating binomials, and how to apply the exponential distribution to interarrival time problems. Examples are provided throughout to illustrate how to set up and solve different types of probability problems using these continuous distributions.
tesis_impunity_game_bracciaforte_viadrina
The document summarizes an experiment comparing behavior in the Impunity Game with complete versus incomplete information. In the Impunity Game, a dictator divides a sum between themselves and a recipient who can reject the offer. With incomplete information, the dictator does not know if their offer is rejected.
The experiment tests several hypotheses about dictator behavior. It finds large differences in dictator giving when recipients have incomplete information - dictators gave more. Two reasons are proposed: dictators cope with uncertainty by giving more; and the complete information Impunity Game is seen as a revenge game while the incomplete information version is not. The results provide insights into how information impacts strategic decision-making and social preferences.
Statistics project on comparison of two batsmen
Here you will find the top order batsmen performance comparison in different conditions like home and away matches by three special statistics.
03 ch ken black solution
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.
Sigma xi showcase_2013_draft0
The document summarizes a statistical analysis of voting patterns on the US Supreme Court. The analysis builds on previous models by developing a maximum entropy model called the Ising model to account for interactions between justices. The Ising model fits the data well, capturing 90% of the correlations between justices' votes. This suggests higher-level coordination emerges from pairwise interactions rather than being the dominant explanation. Analysis of the fitted couplings between justices shows they tend to be positive within ideological blocs and negative between blocs, with chief and swing justices having more moderate influence across blocs. However, the couplings do not directly correspond to behavioral interactions and must be interpreted cautiously.
02a one sample_t-test
The document discusses a one-sample t-test used to compare sample data to a standard value. It provides an example comparing intelligence scores of university students to the average score of 100. The sample of 6 students had a mean of 120. Running a one-tailed t-test in SPSS, the results showed the mean score was significantly higher than 100 with t(5)=3.15, p=.02. This allows the inference that the population mean intelligence at the university is greater than the standard score of 100.
Presentationofdatamod6b
This document discusses different methods for presenting data graphically. It defines bar charts, histograms, frequency polygons, pie charts, and ogives. Examples of each type of graph are provided using sample data on examination scores of 60 students. Bar charts and histograms use class marks and frequencies to show the distribution. Frequency polygons connect the points on a line graph. Pie charts show the proportion of each class. Ogives use class boundaries and cumulative frequencies to indicate less than and greater than distributions. Students are assigned an activity to practice constructing these various graphs using their own collected data.
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Mathematics, Statistics, Sampling Distributions for Counts and Proportions, Binomial Distributions for Sample Counts,
Binomial Distributions in Statistical Sampling, Binomial Mean and Standard Deviation, Sample Proportions, Normal Approximation for Counts and Proportions, Binomial Formula
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Full download : https://goo.gl/uAZVtV Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Edition by Levine
Aron chpt 5 ed revised
This document provides an overview of hypothesis testing, including:
1) It defines hypothesis testing theory and the hypothesis testing process involving null and research hypotheses.
2) It explains the core logic of hypothesis testing including determining characteristics of the comparison distribution, setting cutoff sample scores, and deciding whether to reject the null hypothesis.
3) It discusses one-tailed and two-tailed hypothesis tests and the types of decision errors that can occur in hypothesis testing.
Aron chpt 8 ed
The document provides an introduction to t-tests, including the t-test for a single sample and the t-test for dependent means. It explains that t-tests are used when the population variance is unknown, and compares t-tests to Z-tests. The key steps for conducting a t-test for a single sample and a t-test for dependent means are outlined. These include restating the hypotheses, determining characteristics of the comparison distribution such as the population mean and variance, finding the cutoff score, determining the sample's score, and deciding whether to reject the null hypothesis. Examples are provided to demonstrate how to perform each type of t-test.
Aron chpt 7 ed effect size f2011
This document discusses effect size, statistical power, and the relationship between statistical and practical significance in research studies. It defines effect size as a measure of the difference between populations due to an experimental manipulation. Statistical power is the probability that a study will produce a statistically significant result when the research hypothesis is true. A study's power depends on its effect size, sample size, significance level, and whether it uses a one-tailed or two-tailed test. While a result may be statistically significant, it still may not have practical significance if the effect size is too small to make a meaningful difference. Evaluating both statistical and practical significance is important, especially for studies with practical implications.
Chapters 2 & 4
The document discusses key concepts relating to the normal distribution including:
- Z-scores measure how many standard deviations a score is from the mean. Approximately 68% of scores fall within 1 standard deviation of the mean and 95% within 2 standard deviations.
- The normal curve is a bell-shaped distribution where rare scores fall more than 3 standard deviations from the mean. Many natural phenomena produce normal distributions.
- Normal curve tables show the percentage of scores associated with different z-scores and can be used to find percentiles for raw scores by first converting them to z-scores.
Using excel to for descriptive statistics
To generate descriptive statistics for stress inventory scores in Excel:
1) Load the Analysis ToolPak and select the DATA tab
2) Enter the scores from page 26 of the study guide and select them as the input range
3) Click Descriptive Statistics, select the cells, and choose cell B2 as the output range to display the results, which include an average stress level of 33.68 and a standard deviation of 11.43.
More Related Content
What's hot
19 ch ken black solution
This chapter discusses decision analysis and various techniques for decision making under certainty, uncertainty, and risk. It covers decision tables, decision trees, expected monetary value, utility theory, and revising probabilities based on sample information. The key techniques taught are maximax, maximin, Hurwicz criterion, minimax regret, expected value, and expected value of perfect and sample information. Decision analysis provides strategies to evaluate alternatives and make optimal decisions under different conditions.
Poisson Probability Distributions
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 5: Discrete Probability Distribution
5.3 - Poisson Probability Distributions
12 ch ken black solution
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.
2016-04-20-Moscow-EstimationError-V01
This document summarizes a study that used Monte Carlo simulations to assess the risk of obtaining biased estimates in regression analysis due to duplicated observations in survey data. The study found that the risk of obtaining wrong estimates increases with the number of duplicate records. Even a small number of duplicates can create considerable risk. Weighting duplicate observations by the inverse of their frequency was the most effective solution tested to minimize this risk. The study recommends that statistical agencies be aware of this problem and adopt solutions to correct for duplicates, though correcting data is not a trivial task.
04 ch ken black solution
This document provides an overview of the key concepts and objectives covered in Chapter 4 on probability. The chapter aims to help students understand the different ways of assigning probabilities and how to apply probability rules and laws to solve problems. It emphasizes that there are multiple valid approaches to probability problems. The chapter outlines includes topics like classical vs relative frequency vs subjective probabilities, probability rules like addition and multiplication, and conditional probability. It also provides sample problems and their solutions to illustrate the concepts.
Presentation of data mod 6
This document provides an overview of methods for presenting data, including textual, tabular, and graphical methods. It discusses topics such as ungrouped vs. grouped data, frequency distribution tables, stem-and-leaf plots, class boundaries, class midpoints, and class width. The objectives are to describe how to prepare a stem-and-leaf plot, describe data textually, construct a frequency distribution table, create graphs, and interpret graphs and tables. Examples are provided to illustrate these concepts and methods.
05 ch ken black solution
This document provides an outline and learning objectives for Chapter 5 of a statistics textbook on discrete distributions. The chapter will:
1. Distinguish between discrete and continuous random variables and distributions.
2. Explain how to calculate the mean and variance of discrete distributions.
3. Cover the binomial distribution and how to solve problems using it.
4. Cover the Poisson distribution and how to solve problems using it.
5. Explain how to approximate binomial problems with the Poisson distribution.
6. Cover the hypergeometric distribution and how to solve problems using it.
17 ch ken black solution
This chapter discusses nonparametric statistics including the runs test, Mann-Whitney U test, Wilcoxon matched-pairs signed rank test, Kruskal-Wallis test, Friedman test, and Spearman's rank correlation. These tests are nonparametric alternatives to common parametric tests that do not require the assumptions of normality or equal variances. The chapter provides examples of how to perform and interpret each test.
06 ch ken black solution
This chapter introduces three continuous probability distributions: the uniform, normal, and exponential distributions. It focuses on the normal distribution and how to solve various problems using it, including approximating binomial distributions with the normal. It also covers using the normal distribution to find probabilities, the correction for continuity when approximating binomials, and how to apply the exponential distribution to interarrival time problems. Examples are provided throughout to illustrate how to set up and solve different types of probability problems using these continuous distributions.
tesis_impunity_game_bracciaforte_viadrina
The document summarizes an experiment comparing behavior in the Impunity Game with complete versus incomplete information. In the Impunity Game, a dictator divides a sum between themselves and a recipient who can reject the offer. With incomplete information, the dictator does not know if their offer is rejected.
The experiment tests several hypotheses about dictator behavior. It finds large differences in dictator giving when recipients have incomplete information - dictators gave more. Two reasons are proposed: dictators cope with uncertainty by giving more; and the complete information Impunity Game is seen as a revenge game while the incomplete information version is not. The results provide insights into how information impacts strategic decision-making and social preferences.
Statistics project on comparison of two batsmen
Here you will find the top order batsmen performance comparison in different conditions like home and away matches by three special statistics.
03 ch ken black solution
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.
Sigma xi showcase_2013_draft0
The document summarizes a statistical analysis of voting patterns on the US Supreme Court. The analysis builds on previous models by developing a maximum entropy model called the Ising model to account for interactions between justices. The Ising model fits the data well, capturing 90% of the correlations between justices' votes. This suggests higher-level coordination emerges from pairwise interactions rather than being the dominant explanation. Analysis of the fitted couplings between justices shows they tend to be positive within ideological blocs and negative between blocs, with chief and swing justices having more moderate influence across blocs. However, the couplings do not directly correspond to behavioral interactions and must be interpreted cautiously.
02a one sample_t-test
The document discusses a one-sample t-test used to compare sample data to a standard value. It provides an example comparing intelligence scores of university students to the average score of 100. The sample of 6 students had a mean of 120. Running a one-tailed t-test in SPSS, the results showed the mean score was significantly higher than 100 with t(5)=3.15, p=.02. This allows the inference that the population mean intelligence at the university is greater than the standard score of 100.
Presentationofdatamod6b
This document discusses different methods for presenting data graphically. It defines bar charts, histograms, frequency polygons, pie charts, and ogives. Examples of each type of graph are provided using sample data on examination scores of 60 students. Bar charts and histograms use class marks and frequencies to show the distribution. Frequency polygons connect the points on a line graph. Pie charts show the proportion of each class. Ogives use class boundaries and cumulative frequencies to indicate less than and greater than distributions. Students are assigned an activity to practice constructing these various graphs using their own collected data.
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Mathematics, Statistics, Sampling Distributions for Counts and Proportions, Binomial Distributions for Sample Counts,
Binomial Distributions in Statistical Sampling, Binomial Mean and Standard Deviation, Sample Proportions, Normal Approximation for Counts and Proportions, Binomial Formula
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Full download : https://goo.gl/uAZVtV Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Edition by Levine
What's hot (17)
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Solutions Manual for Statistics For Managers Using Microsoft Excel 7th Editio...
Viewers also liked
Aron chpt 5 ed revised
This document provides an overview of hypothesis testing, including:
1) It defines hypothesis testing theory and the hypothesis testing process involving null and research hypotheses.
2) It explains the core logic of hypothesis testing including determining characteristics of the comparison distribution, setting cutoff sample scores, and deciding whether to reject the null hypothesis.
3) It discusses one-tailed and two-tailed hypothesis tests and the types of decision errors that can occur in hypothesis testing.
Aron chpt 8 ed
The document provides an introduction to t-tests, including the t-test for a single sample and the t-test for dependent means. It explains that t-tests are used when the population variance is unknown, and compares t-tests to Z-tests. The key steps for conducting a t-test for a single sample and a t-test for dependent means are outlined. These include restating the hypotheses, determining characteristics of the comparison distribution such as the population mean and variance, finding the cutoff score, determining the sample's score, and deciding whether to reject the null hypothesis. Examples are provided to demonstrate how to perform each type of t-test.
Aron chpt 7 ed effect size f2011
This document discusses effect size, statistical power, and the relationship between statistical and practical significance in research studies. It defines effect size as a measure of the difference between populations due to an experimental manipulation. Statistical power is the probability that a study will produce a statistically significant result when the research hypothesis is true. A study's power depends on its effect size, sample size, significance level, and whether it uses a one-tailed or two-tailed test. While a result may be statistically significant, it still may not have practical significance if the effect size is too small to make a meaningful difference. Evaluating both statistical and practical significance is important, especially for studies with practical implications.
Chapters 2 & 4
The document discusses key concepts relating to the normal distribution including:
- Z-scores measure how many standard deviations a score is from the mean. Approximately 68% of scores fall within 1 standard deviation of the mean and 95% within 2 standard deviations.
- The normal curve is a bell-shaped distribution where rare scores fall more than 3 standard deviations from the mean. Many natural phenomena produce normal distributions.
- Normal curve tables show the percentage of scores associated with different z-scores and can be used to find percentiles for raw scores by first converting them to z-scores.
Using excel to for descriptive statistics
To generate descriptive statistics for stress inventory scores in Excel:
1) Load the Analysis ToolPak and select the DATA tab
2) Enter the scores from page 26 of the study guide and select them as the input range
3) Click Descriptive Statistics, select the cells, and choose cell B2 as the output range to display the results, which include an average stress level of 33.68 and a standard deviation of 11.43.
Chapters 2 4
The document discusses the normal distribution and z-scores. It provides examples of how to calculate z-scores from raw scores using formulas that involve subtracting the mean and dividing by the standard deviation. It also shows how to convert z-scores back to raw scores. The normal curve is illustrated and it is explained that approximately 68%, 95%, and 99% of scores fall within 1, 2, and 3 standard deviations of the mean, respectively. A normal curve table is introduced for determining percentages associated with particular z-scores.
Loading analysis tool pak on your computer
This document provides instructions for loading the Excel Analysis Toolpak add-in. The steps are to open the Excel Options menu, go to the Add-Ins section, select Excel Add-Ins from the Manage drop-down and click Go. Then select the Analysis Toolpak checkbox and click OK to install the add-in. Once installed, the Analysis Toolpak will appear under the Data tab.
Descriptive statistics
This document provides instructions for obtaining descriptive statistics in Microsoft Excel. It outlines the steps to load the Analysis ToolPak, select the descriptive statistics tool, highlight the input data range, and output the results. The summary generated includes the average profit from car sales data, which is $1843.17.
Newletter v 2.1
The newsletter welcomes over 150 new students and 20 new faculty to the ADP program at Belmont Abbey College for the fall 2012 semester. It highlights the expansion of the Mercedes Hall building to provide more classrooms and study spaces for students. It provides upcoming dates and announces a giveaway contest for students who display an ADP decal on their cars. The student services feature promotes tutoring services available through the Academic Resource Center.
Histograms
1. The document provides steps to create a histogram in Excel using profit and frequency data.
2. Step one involves entering the profit midpoints and frequencies in columns A and B.
3. Step two is inserting a column chart and formatting it by setting the gap width to 0%.
Ncicu 2010 presentation
This document discusses many free online tools that can be used to enhance assessment practices while saving money, including survey tools, test and quiz tools, statistical analysis software, collaboration tools, and document management software. It provides details on several specific free tools, such as SurveyGizmo for surveys, Google Docs for collaboration and document creation, and PSPP and SOFA for statistical analysis. While some low-cost paid options are also mentioned, like ProProfs Quiz Maker, the document emphasizes that there are many high quality free resources available.
Aron chpt 2
This document provides an overview of key concepts in descriptive statistics, including measures of central tendency (mean, median, mode), variability (range, variance, standard deviation), and how to calculate them. It explains that the mean is the average, median is the middle value, and mode is the most frequent score. Variance and standard deviation measure how spread out scores are, with variance being the average of squared deviations from the mean and standard deviation being the positive square root of the variance. Formulas for calculating these measures are also presented.
Aron chpt 9 ed f2011
The document provides an overview of how to conduct a t-test for independent means. It explains that this test is used to compare the means of two independent groups and determines if any difference observed could have been due to chance. It outlines the steps for this test, including calculating the pooled variance estimate, figuring the variance of each group's distribution of means, determining the variance and standard deviation of the distribution of differences between means, and computing the t-score to compare to critical values from the t-table. An example is also provided to demonstrate how to perform a t-test for independent means on sample data.
Calculating binomial probabilities using excel
This document provides instructions for using the BINOMDIST function in Excel to calculate the probability of a certain number of successes in a fixed number of trials when the probability of success is known. Specifically, it shows how to calculate the probability that exactly 2 out of 9 individuals surveyed have used a discount broker, given that the survey found the probability of an individual investor using a discount broker to be 30%. The answer provided is that there is a 27% chance of exactly 2 of the 9 individuals having used a discount broker.
Loading analysis tool pak on your computer
This document provides instructions for loading the Excel Analysis Toolpak add-in. The steps are to open the Excel Options menu, go to the Add-Ins section, select Excel Add-Ins from the Manage drop-down and click Go. Then select the Analysis Toolpak checkbox and click OK to install the add-in. Once installed, the Analysis Toolpak will appear under the Data tab.
Chap004
This document discusses methods for describing and exploring data including dot plots, stem-and-leaf displays, measures of position like percentiles and quartiles, and box plots. It provides examples of each method and how to interpret the results. Key learning objectives covered are constructing and interpreting dot plots, stem-and-leaf displays, computing measures of position like percentiles and quartiles, and developing and analyzing box plots from given data.
Chap006
The document discusses various probability distributions including discrete, binomial, hypergeometric, and Poisson distributions. It provides learning objectives on identifying characteristics of probability distributions, distinguishing between discrete and continuous random variables, computing means, variances and probabilities. Examples are given to illustrate computing probabilities and distributions for binomial, hypergeometric and Poisson distributions. The key characteristics, formulas, and applications of each distribution are defined.
Chap005
This document discusses probability concepts covered in Chapter 5. It begins by defining key terms like probability, experiment, outcome, and event. It then explains the three approaches to assigning probabilities: classical, empirical, and subjective. Next, it covers calculating probabilities using the rules of addition, multiplication, and the complement rule. It also defines joint probability and discusses calculating probabilities using Venn diagrams and contingency tables. The learning objectives cover all these essential probability topics.
Aron chpt 1 ed
This document provides an overview of key concepts in descriptive statistics, including frequency tables, histograms, and distributions. It explains that frequency tables and histograms summarize and describe patterns in a set of scores or values. A frequency table shows the counts of observations at each value, while a histogram uses bar heights to represent frequencies, making distributions visually apparent. An example shows how to construct a frequency table and histogram from a set of job happiness ratings ranging from 0 to 10.
Viewers also liked (20)
Similar to Aron chpt 6 ed revised
Gravetter10e_PPT_Ch07_student.pptx
This document provides an overview of key concepts regarding the distribution of sample means. It discusses how the distribution of sample means approaches a normal shape as sample size increases based on the central limit theorem. The mean of this distribution is equal to the population mean, making the sample mean an unbiased statistic. The variability of the distribution is measured by the standard error, which decreases as sample size increases. The document shows how to calculate a z-score for a sample mean based on the standard error in order to determine the probability of obtaining a sample with a given mean. It previews how these concepts will be applied in inferential statistics.
Sampling in Statistical Inference
The document discusses key concepts related to sampling including the differences between populations and samples, probability and non-probability sampling, and the central limit theorem. It notes that samples are used to make inferences about populations and discusses factors like time, cost, and consumption that necessitate sampling rather than examining entire populations. The central limit theorem states that the distribution of sample means will approach a normal distribution as the sample size increases.
Sampling 1231243290208505 1
The document discusses key concepts related to sampling including populations, samples, probability and non-probability sampling, variables, sampling error, sampling distributions, and the central limit theorem. It provides examples to illustrate these concepts and implications of the central limit theorem including how it allows inferring properties of the population from a sample.
Hypotheses.pptx
This document discusses concepts related to statistics and probability, including:
- The sampling distribution of a sample mean is the frequency distribution of sample means taken from a population.
- The mean of the sampling distribution of sample means is equal to the population mean.
- The standard error of the mean is the standard deviation of the sampling distribution of the sample mean.
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
This document discusses finding the mean and variance of the sampling distribution of means. It provides examples of computing the mean and variance of the sampling distribution when random samples are drawn from a population. It also explains the Central Limit Theorem - that as the sample size increases, the sampling distribution of the mean approaches a normal distribution, regardless of the population distribution. Exercises are provided to practice calculating the mean and variance of sampling distributions based on population parameters and sample sizes.
Sampling_Distribution_stat_of_Mean_New.pptx
This document discusses sampling distributions and related statistical concepts. It defines descriptive and inferential statistics, and explains that inferential statistics uses samples to draw conclusions about populations. Key concepts covered include sampling, probability distributions, sampling distributions, and the central limit theorem. The sampling distribution of the sample mean is examined in depth. For a sample mean, the expected value is equal to the population mean, while the standard error depends on factors like the population standard deviation and sample size. Examples are provided to illustrate these statistical properties.
Sampling
The document discusses sampling and how samples can be used to represent populations. It explains the difference between parameters and statistics, and introduces the central limit theorem which states that the distribution of sample means will approach a normal distribution as the sample size increases. Several examples are provided to illustrate concepts like determining the probability that a sample mean differs from the population mean based on the standard error.
Probability and Samples: The Distribution of Sample Means
Chapter 2 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
Chapter one on sampling distributions.ppt
The document discusses sampling distributions and their properties. It introduces key concepts like population parameters, sample statistics, estimators, and the central limit theorem. It explains that as sample size increases, the sampling distribution of the sample mean approaches a normal distribution with a mean equal to the population mean and standard deviation equal to the population standard deviation divided by the square root of the sample size. The sampling distribution tells us how close sample statistics are likely to be to the corresponding population parameters.
Common sampling techniques
this is a presentation guide for teachers which shows the different sampling techniques to select appropriate number of samples.
Sampling distribution
This chapter discusses sampling and sampling distributions. It defines key terms like population, parameter, sample, and statistic. It also differentiates between a population and a sample. The chapter covers different sampling methods like simple random sampling, stratified random sampling, and cluster sampling. It describes the properties of the sampling distribution of the sample mean, including its expected value and standard deviation. The chapter also explains the central limit theorem.
Sampling distribution
This chapter discusses sampling and sampling distributions. It defines key terms like population, parameter, sample, and statistic. It also differentiates between a population and a sample. The chapter covers different sampling methods like simple random sampling, stratified random sampling, and cluster sampling. It describes the properties of the sampling distribution of the sample mean, including its expected value and standard deviation. The chapter also explains the central limit theorem.
Aron chpt 8 ed
The document provides an introduction to t-tests, including the t-test for a single sample and the t-test for dependent means. It explains that t-tests are used when the population variance is unknown, and compares t-tests to Z-tests. The key steps for conducting a t-test for a single sample and a t-test for dependent means are outlined. These include restating the hypotheses, determining characteristics of the comparison distribution such as the population mean and variance, finding the cutoff score, determining the sample's score, and deciding whether to reject the null hypothesis. Examples are provided to demonstrate how to perform each type of t-test.
Chp11 - Research Methods for Business By Authors Uma Sekaran and Roger Bougie
This document discusses sampling and sampling distributions. It defines key concepts like population, sample, probability distributions, sampling distributions, and the central limit theorem. It explains that as sample size increases, the sampling distribution approximates a normal distribution according to the central limit theorem. It also discusses different types of sampling methods like simple random sampling, systematic random sampling, and stratified random sampling.
Sampling and sampling distributions
Name Shakeel Nouman
Religion Christian
Domicile Punjab (Lahore)
Contact # 0332-4462527. 0321-9898767
E.Mail sn_gcu@yahoo.com
sn_gcu@hotmail.com
The sampling distribution
The document defines a sampling distribution of sample means as a distribution of means from random samples of a population. The mean of sample means equals the population mean, and the standard deviation of sample means is smaller than the population standard deviation, equaling it divided by the square root of the sample size. As sample size increases, the distribution of sample means approaches a normal distribution according to the Central Limit Theorem.
5_lectureslides.pptx
This document provides an overview of Module 5 on sampling distributions. It discusses key concepts like parameters vs statistics, sampling variability, and sampling distributions. It explains that the sampling distribution of a sample mean is a normal distribution with a mean equal to the population mean and standard deviation equal to the population standard deviation divided by the square root of the sample size. The central limit theorem states that as the sample size increases, the distribution of sample means will approach a normal distribution regardless of the shape of the population distribution. The module also covers binomial distributions for sample counts and proportions.
Chapter 11 Psrm
This document provides an overview of key concepts in descriptive statistics including measures of central tendency (mode, median, mean), measures of dispersion (range, variance, standard deviation), the normal distribution, z-scores, hypothesis testing, and the t-distribution. It defines each concept and provides examples of calculating and interpreting common statistics.
Statistics by DURGESH JHARIYA OF jnv,bn,jbp
mathematics presentation for ix class
my teacher is mr. n.k.nayak sir
BY-----DURGESH JHARIYA
ONLY FOR jawahar navodaya students
vdocuments.mx_chapter-5-probability-distributions-56a36d9fddc1e.ppt
This document discusses key concepts related to probability distributions and the binomial distribution. It defines discrete and continuous random variables and provides examples. It also defines mean, variance, and expectation for discrete random variables and provides examples of calculating each. The document then defines the requirements for a binomial experiment and the binomial probability formula. It provides examples of calculating probabilities using the binomial distribution. Finally, it defines the mean, variance, and standard deviation formulas for the binomial distribution and provides an example.
Similar to Aron chpt 6 ed revised (20)
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
PSUnit_III_Lesson_2_Finding_the_Mean _and_Variance_of_the_Sampling_Distributi...
Probability and Samples: The Distribution of Sample Means
Probability and Samples: The Distribution of Sample Means
Chp11 - Research Methods for Business By Authors Uma Sekaran and Roger Bougie
Chp11 - Research Methods for Business By Authors Uma Sekaran and Roger Bougie
vdocuments.mx_chapter-5-probability-distributions-56a36d9fddc1e.ppt
vdocuments.mx_chapter-5-probability-distributions-56a36d9fddc1e.ppt
More from Sandra Nicks
Newletter v 2.1
The newsletter welcomes over 150 new students and 20 new faculty to the ADP program at Belmont Abbey College for the fall 2012 semester. It highlights the expansion of the ADP space with the renovation of Mercedes Hall, including a new student lounge, chapel, and upcoming computer lab. It provides upcoming important dates and encourages students to get involved with tutoring services available through the Academic Resource Center.
Night owl
The newsletter provides information about Bellmont Abbey College's Adult Degree Program (ADP). It highlights an upcoming basketball game on February 24th that ADP students can attend for free. It also features a student profile, the program's calendar of events including a music performance and play, and tips on nutrition, exercise and note-taking.
Simple random sample using excel
To generate a simple random sample using Excel, enter data into a sheet, click Data Analysis under the Data tab, select Sampling, highlight the input range and number of samples, click OK, and the random sample will appear in the selected output range.
Chap015
This document provides an overview of index numbers and how to compute different types of indexes. It defines an index number as a number that measures relative change from one time period to another in items like price, quantity, or value. It describes simple, unweighted indexes and weighted indexes like the Laspeyres and Paasche indexes. It provides examples of how to calculate and interpret simple indexes, Laspeyres indexes, Paasche indexes, Fisher's ideal index, and value indexes. The learning objectives cover computing and interpreting these different index types.
Chap010
This document discusses hypothesis testing and outlines the key steps in conducting hypothesis tests. It defines key terms like the null hypothesis, alternate hypothesis, Type I and Type II errors. It also distinguishes between one-tailed and two-tailed tests. Examples are provided to demonstrate how to set up and conduct hypothesis tests to analyze population means when the population standard deviation is both known and unknown. The learning objectives covered include defining hypotheses, explaining the hypothesis testing procedure, describing types of errors, using test statistics, and conducting tests on population means and proportions.
Chap009
Here are the steps to solve this problem:
1) Given: n = 10, x = 0.32, s = 0.09
2) The degrees of freedom is n - 1 = 10 - 1 = 9
3) The t-value for a 95% CI with 9 df is t0.025,9 = 2.262 (from t-table)
4) The CI is: x ± t*s/√n = 0.32 ± 2.262*(0.09/√10) = 0.32 ± 0.029
5) The 95% CI is 0.291 to 0.349 inches
6) 0.30 inches is within the CI, so it would be
Chap007
This chapter discusses several key continuous probability distributions including the uniform, normal, and exponential distributions. It covers the characteristics and formulas for computing probabilities for each distribution. For the uniform distribution, it discusses how to calculate the mean, find probabilities, and graph the distribution. For the normal distribution, it outlines the characteristics of the bell curve shape and covers converting to the standard normal distribution and using the empirical rule. It also provides examples of finding probabilities for observations within given ranges of a normal distribution.
Finding areas for z using excel
This document provides instructions for using the NORMDIST and NORMINV functions in Excel to find areas under the normal curve and find x-values given areas. For NORMDIST, the user enters values for x, mean, standard deviation and selects true for cumulative to find the area from the left of the distribution to x. For NORMINV, the user enters the probability, mean and standard deviation to find the x-value corresponding to that area. Both note that results are for the cumulative area and adjustments may be needed for the area between the mean and x.
Chap008
This document provides an overview of sampling methods and the central limit theorem. It defines key learning objectives around sampling, describes common probability sampling methods like simple random sampling and stratified random sampling. It introduces the concept of sampling distribution of the sample mean and explains how the central limit theorem states that the sampling distribution of the sample mean approximates a normal distribution, even if the population is not normally distributed.
Chap004
This document describes chapter 4 of a statistics textbook. It provides 7 learning objectives related to describing and exploring data through various statistical techniques. These include constructing and interpreting dot plots, stem-and-leaf displays, box plots, and scatterplots. It also covers measures of central tendency, percentiles, skewness, and contingency tables. Examples are provided for each statistical technique to help illustrate how to compute and interpret the various descriptive statistics.
Creating frequency distribution table, histograms and polygons using excel an...
This document provides instructions for creating frequency distribution tables, histograms, and polygons using Excel's Analysis ToolPak. It guides the user through downloading sample sales data, determining the number of classes and class intervals, setting up bins and frequency tables, and generating histograms, frequency polygons, and cumulative frequency polygons. Key steps include using formulas to calculate the number of classes and class interval size, setting up the frequency table and bins, and using Excel's Data Analysis tool and charting features to generate the graphs.
Creating frequency distribution tables and histograms using excel analysis to...
This document provides instructions for creating a frequency distribution table and histogram in Excel using the Analysis ToolPak. It explains how to determine the number of classes and class intervals using formulas. It then walks through downloading sample sales data, setting up the class intervals or "bins", generating the frequency table and histogram using the Data Analysis tool, and formatting the output properly with labels and resized charts.
Chap003
This document provides an overview of key concepts for describing numerical data, including measures of central tendency (such as the mean, median, mode, weighted mean, and geometric mean) and measures of dispersion (such as the range, mean deviation, variance and standard deviation). It defines each measure and provides examples to demonstrate how to calculate and interpret the measures. The learning objectives cover explaining the concept of central tendency, identifying and computing various measures of central tendency and dispersion, and applying the measures to analyze datasets.
Chap002
This document discusses various methods for organizing and presenting data visually, including frequency tables, bar charts, pie charts, frequency distributions, histograms, and frequency polygons. It provides examples and learning objectives for how to create each of these and use them to summarize key characteristics of a data set such as profits from vehicle sales.
Aron chpt 1 ed (1)
Here are the steps to complete the frequency table:
9 II 2
10 0 0
Total 20 20
Copyright © 2011 by Pearson Education, Inc. All rights reserved.
Aronchpt3correlation
This document discusses correlation and prediction. It defines correlation as a descriptive statistic that describes the relationship between two equal-interval numeric variables. A scatter diagram can be used to graphically depict the relationship and identify if it is linear, curvilinear, or no correlation. The correlation coefficient (r) is a number that indicates the strength and direction of the linear relationship, ranging from -1 to 1. However, a correlation does not necessarily imply causation. Statistical significance testing and longitudinal or experimental studies help determine if correlations reflect a causal relationship.
Calculating a correlation coefficient and scatter plot using excel
This document provides instructions for calculating the correlation coefficient between depression and anxiety scores for 12 clients using Excel, and creating a scatter plot to visualize the relationship. The correlation coefficient calculated was 0.625, indicating a moderate positive relationship. The scatter plot was formatted to change the axis labels and title to focus on the correlation between depression and anxiety.
Using excel to convert raw score to z score
The document provides instructions for converting raw scores to z-scores in Excel. It describes entering raw scores in a column, using the Data Analysis tool to calculate the mean and standard deviation of the raw scores. It then explains the formula to convert each raw score to a z-score by taking the raw score minus the mean and dividing by the standard deviation. The user is guided to enter this formula in a cell and copy it down to convert all raw scores to z-scores.
Chapters 2 & 4
The document discusses key concepts relating to the normal distribution including:
- The normal distribution is bell-shaped and approximately 68% of scores fall within one standard deviation of the mean.
- Z-scores describe the location of a raw score in terms of standard deviations from the mean and allow comparison of scores from different distributions.
- The normal curve table lists the percentages of scores associated with different z-scores and can be used to find percentages of scores above or below a given z-score or raw score.
Chap001
This document discusses key concepts in statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting data to assist decision making. Descriptive statistics are used to summarize and present data, while inferential statistics allow generalization from a sample to a population. A population is the total group being studied, while a sample is a subset. Variables can be qualitative, involving categories, or quantitative, involving numbers. Quantitative variables can be discrete, taking certain values, or continuous. The level of measurement for data, such as nominal, ordinal, interval, or ratio, determines what analyses can be done.
More from Sandra Nicks (20)
Creating frequency distribution table, histograms and polygons using excel an...
Creating frequency distribution table, histograms and polygons using excel an...
Creating frequency distribution tables and histograms using excel analysis to...
Creating frequency distribution tables and histograms using excel analysis to...
Calculating a correlation coefficient and scatter plot using excel
Calculating a correlation coefficient and scatter plot using excel
Recently uploaded
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Skybuffer SAM4U tool for SAP license adoption
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Gursev Pirge, PhD
Senior Data Scientist - JohnSnowLabs
Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Leveraging the Graph for Clinical Trials and Standards
Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
Principal Consultant - Neo4j
Principle of conventional tomography-Bibash Shahi ppt..pptx
before the computed tomography, it had been widely used.
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Pitangent Analytics & Technology Solutions Pvt. Ltd
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Recently uploaded (20)
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Harnessing the Power of NLP and Knowledge Graphs for Opioid Research
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
Aron chpt 6 ed revised
- 6. Example 2 Copyright © 2011 by Pearson Education, Inc. All rights reserved
- 7. die 1 die 2 Ave. die 1 die 2 Ave. die 1 die 2 Ave. 1 1 1 1 13 3 1 2 25 5 1 3 2 1 2 1.5 14 3 2 2.5 26 5 2 3.5 3 1 3 2 15 3 3 3 27 5 3 4 4 1 4 2.5 16 3 4 3.5 28 5 4 4.5 5 1 5 3 17 3 5 4 29 5 5 5 6 1 6 3.5 18 3 6 4.5 30 5 6 5.5 7 2 1 1.5 19 4 1 2.5 31 6 1 3.5 8 2 2 2 20 4 2 3 32 6 2 4 9 2 3 2.5 21 4 3 3.5 33 6 3 4.5 10 2 4 3 22 4 4 4 34 6 4 5 11 2 5 3.5 23 4 5 4.5 35 6 5 5.5 12 2 6 4 24 4 6 5 36 6 6 6
- 8. 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 36 samples mean f 1 1 1.5 2 2 3 2.5 4 3 5 3.5 6 4 5 4.5 4 5 3 5.5 2 6 1
- 9. 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 72 samples mean f 1 2 1.5 4 2 6 2.5 8 3 10 3.5 12 4 10 4.5 8 5 6 5.5 4 6 2
- 10. 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 108 samples mean f 1 3 1.5 6 2 9 2.5 12 3 15 3.5 18 4 15 4.5 12 5 9 5.5 6 6 3
- 11. 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 33 32 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 288 samples mean f 1 8 1.5 16 2 24 2.5 32 3 40 3.5 48 4 40 4.5 32 5 24 5.5 16 6 8
- 17. Standard Deviation of a Distribution of Means Copyright © 2011 by Pearson Education, Inc. All rights reserved Pop. SD = 2.5 in.
- 20. die 1 f 1 1 1 2 2 1 3 3 1 4 4 1 5 5 1 6 6 1 6 5 4 3 2 1 1 2 3 4 5 6
- 21. 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 36 samples mean f 1 1 1.5 2 2 3 2.5 4 3 5 3.5 6 4 5 4.5 4 5 3 5.5 2 6 1