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Third in a series of four seminars presented to University of North Texas librarians. This presentation focuses on using basic tests that determine the association of two sets of data based on measures of central tendency and variation.

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Research method ch07 statistical methods 1

This document provides an overview of statistical methods used in health research. It discusses descriptive statistics such as mean, median and mode that are used to describe data. It also covers inferential statistics that are used to infer characteristics of populations based on samples. Specific statistical tests covered include t-tests, which are used to test differences between means, and F-tests, which are used to compare variances. The document explains key concepts in hypothesis testing such as null and alternative hypotheses, type I and type II errors, and statistical power. Parametric tests covered assume the data meet certain statistical assumptions like normality.

Medical Statistics Part-II:Inferential statistics

This document provides an overview of key concepts in inferential statistics. Inferential statistics allows researchers to make inferences about populations based on samples. It includes techniques like hypothesis testing, t-tests, analysis of variance (ANOVA), regression analysis, and more. The goal is to determine if observed differences are statistically significant rather than due to chance. Inferential statistics helps estimate parameters and analyze variability using statistical models and software.

The Chi-Square Statistic: Tests for Goodness of Fit and Independence

Chapter 15 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed

Variable inferential statistics

This document discusses hypothesis testing and inferential statistics. It covers topics like hypothesis testing process, types of errors, differentiating between critical value method and probability value method, tests for one and two populations including z-test, t-test, Wilcoxon test and binomial test. It also discusses assumptions and procedures for tests like pooled t-test, paired t-test, Mann-Whitney test and paired Wilcoxon test. Examples of applying these tests on quantitative and qualitative data are provided.

Nonparametric tests

This document discusses non-parametric statistical tests, which make few assumptions about the distribution of the underlying population. It provides examples of non-parametric tests like the sign test, Wilcoxon rank sum test, and Kruskal-Wallis test. These tests involve ranking all observations from different groups together and applying statistical tests to the ranks rather than the original values. Non-parametric tests are useful when assumptions of parametric tests may not hold but lack power with small samples.

Parametric tests

This document discusses parametric tests used for statistical analysis. It introduces t-tests, ANOVA, Pearson's correlation coefficient, and Z-tests. T-tests are used to compare means of small samples and include one-sample, unpaired two-sample, and paired two-sample t-tests. ANOVA compares multiple population means and includes one-way and two-way ANOVA. Pearson's correlation measures the strength of association between two continuous variables. Z-tests compare means or proportions of large samples. Key assumptions and calculations for each test are provided along with examples. The document emphasizes the importance of choosing the appropriate statistical test for research.

Inferential Statistics

This document provides an overview of various statistical analysis techniques used in inferential statistics, including t-tests, ANOVA, ANCOVA, chi-square, regression analysis, and interpreting null hypotheses. It defines key terms like alpha levels, effect sizes, and interpreting graphs. The overall purpose is to explain common statistical methods for analyzing data and determining the probability that results occurred by chance or were statistically significant.

Basic stat analysis using excel

This ppt includes basic concepts about data types, levels of measurements. It also explains which descriptive measure, graph and tests should be used for different types of data. A brief of Pivot tables and charts is also included.

Research method ch07 statistical methods 1

This document provides an overview of statistical methods used in health research. It discusses descriptive statistics such as mean, median and mode that are used to describe data. It also covers inferential statistics that are used to infer characteristics of populations based on samples. Specific statistical tests covered include t-tests, which are used to test differences between means, and F-tests, which are used to compare variances. The document explains key concepts in hypothesis testing such as null and alternative hypotheses, type I and type II errors, and statistical power. Parametric tests covered assume the data meet certain statistical assumptions like normality.

Medical Statistics Part-II:Inferential statistics

This document provides an overview of key concepts in inferential statistics. Inferential statistics allows researchers to make inferences about populations based on samples. It includes techniques like hypothesis testing, t-tests, analysis of variance (ANOVA), regression analysis, and more. The goal is to determine if observed differences are statistically significant rather than due to chance. Inferential statistics helps estimate parameters and analyze variability using statistical models and software.

The Chi-Square Statistic: Tests for Goodness of Fit and Independence

Chapter 15 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed

Variable inferential statistics

This document discusses hypothesis testing and inferential statistics. It covers topics like hypothesis testing process, types of errors, differentiating between critical value method and probability value method, tests for one and two populations including z-test, t-test, Wilcoxon test and binomial test. It also discusses assumptions and procedures for tests like pooled t-test, paired t-test, Mann-Whitney test and paired Wilcoxon test. Examples of applying these tests on quantitative and qualitative data are provided.

Nonparametric tests

This document discusses non-parametric statistical tests, which make few assumptions about the distribution of the underlying population. It provides examples of non-parametric tests like the sign test, Wilcoxon rank sum test, and Kruskal-Wallis test. These tests involve ranking all observations from different groups together and applying statistical tests to the ranks rather than the original values. Non-parametric tests are useful when assumptions of parametric tests may not hold but lack power with small samples.

Parametric tests

This document discusses parametric tests used for statistical analysis. It introduces t-tests, ANOVA, Pearson's correlation coefficient, and Z-tests. T-tests are used to compare means of small samples and include one-sample, unpaired two-sample, and paired two-sample t-tests. ANOVA compares multiple population means and includes one-way and two-way ANOVA. Pearson's correlation measures the strength of association between two continuous variables. Z-tests compare means or proportions of large samples. Key assumptions and calculations for each test are provided along with examples. The document emphasizes the importance of choosing the appropriate statistical test for research.

Inferential Statistics

This document provides an overview of various statistical analysis techniques used in inferential statistics, including t-tests, ANOVA, ANCOVA, chi-square, regression analysis, and interpreting null hypotheses. It defines key terms like alpha levels, effect sizes, and interpreting graphs. The overall purpose is to explain common statistical methods for analyzing data and determining the probability that results occurred by chance or were statistically significant.

Basic stat analysis using excel

This ppt includes basic concepts about data types, levels of measurements. It also explains which descriptive measure, graph and tests should be used for different types of data. A brief of Pivot tables and charts is also included.

Inferential statistics

The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.

Emil Pulido on Quantitative Research: Inferential Statistics

What do you need to consider when you will be doing Quantitative Research? You will need to consider your data- statistics.

Advanced statistics

This document outlines topics related to statistics that will be covered. It is divided into 6 parts. Part 1 discusses the role of statistics in research, descriptive statistics, sampling procedures, sample size, and inferential statistics. Part 2 covers choice of statistical tests, defining variables, scales of measurements, and number of samples. Parts 3 and 4 discuss parametric and non-parametric tests. Part 5 is about goodness of fit tests. Part 6 covers choosing correct statistical tests and introduction to multiple regression. The document also provides examples and definitions of key statistical concepts like mean, median, mode, range, and different sampling methods.

Chi square

The document provides an overview of chi-square tests, including chi-square tests for goodness of fit and tests of independence. It explains that chi-square tests are used with categorical or classified data rather than numerical data. For a chi-square test of goodness of fit, the null hypothesis specifies the expected proportions in different categories. Observed and expected frequencies are calculated and compared using the chi-square statistic. A chi-square test of independence examines whether two categorical variables are related by comparing observed and expected joint frequencies.

Parametric & non parametric

This document provides an overview of parametric and non-parametric statistical tests. Parametric tests assume the data follows a known distribution (e.g. normal) while non-parametric tests make no assumptions. Common non-parametric tests covered include chi-square, sign, Mann-Whitney U, and Spearman's rank correlation. The chi-square test is described in more detail, including how to calculate chi-square values, degrees of freedom, and testing for independence and goodness of fit.

INFERENTIAL STATISTICS: AN INTRODUCTION

For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.

Non parametric test

This document discusses non-parametric tests, which are statistical tests that make fewer assumptions about the population distribution compared to parametric tests. Some key points:
1) Non-parametric tests like the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U-test, and Kruskal-Wallis test are used when the population is not normally distributed or sample sizes are small.
2) They are applied in situations where data is on an ordinal scale rather than a continuous scale, the population is not well defined, or the distribution is unknown.
3) Advantages are that they are easier to compute and make fewer assumptions than parametric tests,

Data analysis powerpoint

This document discusses descriptive and inferential statistics used in nursing research. It defines key statistical concepts like levels of measurement, measures of central tendency, descriptive versus inferential statistics, and commonly used statistical tests. Nominal, ordinal, interval and ratio are the four levels of measurement, with ratio allowing the most data manipulation. Descriptive statistics describe sample data while inferential statistics allow estimating population parameters and testing hypotheses. Common descriptive statistics include mean, median and mode, while common inferential tests are t-tests, ANOVA, chi-square and correlation. Type I errors incorrectly reject the null hypothesis.

t distribution, paired and unpaired t-test

1) The t-test is a statistical test used to determine if there are any statistically significant differences between the means of two groups, and was developed by William Gosset under the pseudonym "Student".
2) The t-distribution is used for calculating t-tests when sample sizes are small and/or variances are unknown. It has a mean of zero and variance greater than one.
3) Paired t-tests are used to compare the means of two related groups when samples are paired, while unpaired t-tests are used to compare unrelated groups or independent samples.

Parametric and non parametric test in biostatistics

This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test

Presentation chi-square test & Anova

The document discusses hypothesis testing using parametric and non-parametric tests. It defines key concepts like the null and alternative hypotheses, type I and type II errors, and p-values. Parametric tests like the t-test, ANOVA, and Pearson's correlation assume the data follows a particular distribution like normal. Non-parametric tests like the Wilcoxon, Mann-Whitney, and chi-square tests make fewer assumptions and can be used when sample sizes are small or the data violates assumptions of parametric tests. Examples are provided of when to use parametric or non-parametric tests depending on the type of data and statistical test being performed.

Basics of Hypothesis testing for Pharmacy

This presentation will clarify all basic concepts and terms of hypothesis testing. It will also help you to decide correct Parametric & Non-Parametric test for your data

Small sample test

Testing of Hypothesis for small sample. t test for testing single mean, two means for independent samples and two means for dependent samples.

Significance test

The document discusses significance tests and their role in hypothesis testing. It defines key terms like p-value, significance level, confidence level, rejection region, and classification of significance tests. The p-value represents the probability of observing the results by chance if the null hypothesis is true. The significance level is set before data collection and represents the probability of incorrectly rejecting the null hypothesis. A p-value less than the significance level leads to rejecting the null hypothesis.

One-Sample Hypothesis Tests

A researcher tested the effectiveness of an herbal supplement on physical fitness using the Marine Physical Fitness Test. 25 college students took the supplement for 6 weeks and averaged a score of 38.68 on the test, compared to the average population score of 35. Using a t-test with α=.05 and df=24, the researcher found the average score of 38.68 was not significantly different than the population mean of 35 (t=2.041, p=.052). Therefore, there is not enough evidence to conclude the supplement had an effect on fitness levels, as the higher average score could be due to chance for this small sample.

Non parametric tests by meenu

This document provides an overview of non-parametric statistical tests. It discusses tests such as the chi-square test, Wilcoxon signed-rank test, Mann-Whitney test, Friedman test, and median test. These tests can be used with ordinal or nominal data when the assumptions of parametric tests are not met. The document explains the appropriate uses and procedures for each non-parametric test.

Analysing a hypothetical data with t-test in excel

This document provides steps for analyzing data using a t-test in Excel. It introduces the topic of hypothesis testing and the specific problem of comparing the daily study hours of female and male students. The null and alternative hypotheses are defined, with the null being that the mean study hours are equal between groups. The document then outlines the 5 steps to perform a t-test in Excel: 1) inputting the data, 2) accessing the data analysis tool, 3) selecting the t-test option and inputting the data ranges and hypothesized mean difference, 4) viewing the output, and 5) analyzing the results. The t-test results fail to reject the null hypothesis, indicating the mean study hours are not significantly different between female

Introduction to Hypothesis Testing

This document provides an overview of hypothesis testing including:
1) The four steps of hypothesis testing - stating hypotheses, setting criteria, collecting data, and making a decision. It also discusses types of errors.
2) Factors that influence the outcome like effect size, sample size, and variability. Larger effects, samples, and less variability make rejecting the null hypothesis more likely.
3) Direction hypotheses tests where the alternative predicts a direction of the effect. This allows rejecting the null with smaller differences but in the predicted direction.
4) Effect size measures like Cohen's d provide information beyond just significance. Statistical power is the probability of correctly rejecting a false null hypothesis.

Inferential Statistics

This document provides an overview of inferential statistics and statistical tests that can be used, including correlation tests, t-tests, and how to determine which tests are appropriate. It discusses the assumptions of parametric tests like Pearson's correlation and t-tests, and how to check assumptions graphically and using statistical tests. Specific procedures for conducting correlation analyses in Excel and SPSS are outlined, along with how to interpret and report the results.

The effect birth date has on choosing to study a sports related course at an ...

The Relative Age Effect (RAE) in the academic study of sport. The aim of this study was to investigate the relationship of those students being born on a certain date will indeed influence the decision to study a sports related course at an educational institution.

Ch12 (1)

This chapter discusses inferential statistics and the concepts underlying them. It covers key topics like types of inferential statistics (parametric vs nonparametric), important perspectives like generalizing from samples to populations, underlying concepts like null/alternative hypotheses and types of errors. Specific statistical techniques are explained like t-tests, ANOVA, regression, along with key ideas like sampling distributions, standard error, degrees of freedom, and the steps to conduct statistical tests. Different types of samples and issues with gain scores are also addressed.

L10 confidence intervals

This document discusses confidence intervals for estimating population means from sample data. It begins by explaining how to calculate point estimates and confidence intervals when the sample size is large (n ≥ 30) using the normal distribution. It then covers calculating confidence intervals when the sample size is small (n < 30) using the t-distribution. The key steps covered are determining the appropriate distribution to use based on sample size and knowledge of the population standard deviation, finding the critical values and margin of error, and calculating the confidence interval. Examples are provided to demonstrate how to construct confidence intervals in different situations.

Inferential statistics

The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.

Emil Pulido on Quantitative Research: Inferential Statistics

What do you need to consider when you will be doing Quantitative Research? You will need to consider your data- statistics.

Advanced statistics

This document outlines topics related to statistics that will be covered. It is divided into 6 parts. Part 1 discusses the role of statistics in research, descriptive statistics, sampling procedures, sample size, and inferential statistics. Part 2 covers choice of statistical tests, defining variables, scales of measurements, and number of samples. Parts 3 and 4 discuss parametric and non-parametric tests. Part 5 is about goodness of fit tests. Part 6 covers choosing correct statistical tests and introduction to multiple regression. The document also provides examples and definitions of key statistical concepts like mean, median, mode, range, and different sampling methods.

Chi square

The document provides an overview of chi-square tests, including chi-square tests for goodness of fit and tests of independence. It explains that chi-square tests are used with categorical or classified data rather than numerical data. For a chi-square test of goodness of fit, the null hypothesis specifies the expected proportions in different categories. Observed and expected frequencies are calculated and compared using the chi-square statistic. A chi-square test of independence examines whether two categorical variables are related by comparing observed and expected joint frequencies.

Parametric & non parametric

This document provides an overview of parametric and non-parametric statistical tests. Parametric tests assume the data follows a known distribution (e.g. normal) while non-parametric tests make no assumptions. Common non-parametric tests covered include chi-square, sign, Mann-Whitney U, and Spearman's rank correlation. The chi-square test is described in more detail, including how to calculate chi-square values, degrees of freedom, and testing for independence and goodness of fit.

INFERENTIAL STATISTICS: AN INTRODUCTION

For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.

Non parametric test

This document discusses non-parametric tests, which are statistical tests that make fewer assumptions about the population distribution compared to parametric tests. Some key points:
1) Non-parametric tests like the chi-square test, sign test, Wilcoxon signed-rank test, Mann-Whitney U-test, and Kruskal-Wallis test are used when the population is not normally distributed or sample sizes are small.
2) They are applied in situations where data is on an ordinal scale rather than a continuous scale, the population is not well defined, or the distribution is unknown.
3) Advantages are that they are easier to compute and make fewer assumptions than parametric tests,

Data analysis powerpoint

This document discusses descriptive and inferential statistics used in nursing research. It defines key statistical concepts like levels of measurement, measures of central tendency, descriptive versus inferential statistics, and commonly used statistical tests. Nominal, ordinal, interval and ratio are the four levels of measurement, with ratio allowing the most data manipulation. Descriptive statistics describe sample data while inferential statistics allow estimating population parameters and testing hypotheses. Common descriptive statistics include mean, median and mode, while common inferential tests are t-tests, ANOVA, chi-square and correlation. Type I errors incorrectly reject the null hypothesis.

t distribution, paired and unpaired t-test

1) The t-test is a statistical test used to determine if there are any statistically significant differences between the means of two groups, and was developed by William Gosset under the pseudonym "Student".
2) The t-distribution is used for calculating t-tests when sample sizes are small and/or variances are unknown. It has a mean of zero and variance greater than one.
3) Paired t-tests are used to compare the means of two related groups when samples are paired, while unpaired t-tests are used to compare unrelated groups or independent samples.

Parametric and non parametric test in biostatistics

This ppt will helpful for optometrist where and when to use biostatistic formula along with different examples
- it contains all test on parametric or non-parametric test

Presentation chi-square test & Anova

The document discusses hypothesis testing using parametric and non-parametric tests. It defines key concepts like the null and alternative hypotheses, type I and type II errors, and p-values. Parametric tests like the t-test, ANOVA, and Pearson's correlation assume the data follows a particular distribution like normal. Non-parametric tests like the Wilcoxon, Mann-Whitney, and chi-square tests make fewer assumptions and can be used when sample sizes are small or the data violates assumptions of parametric tests. Examples are provided of when to use parametric or non-parametric tests depending on the type of data and statistical test being performed.

Basics of Hypothesis testing for Pharmacy

This presentation will clarify all basic concepts and terms of hypothesis testing. It will also help you to decide correct Parametric & Non-Parametric test for your data

Small sample test

Testing of Hypothesis for small sample. t test for testing single mean, two means for independent samples and two means for dependent samples.

Significance test

The document discusses significance tests and their role in hypothesis testing. It defines key terms like p-value, significance level, confidence level, rejection region, and classification of significance tests. The p-value represents the probability of observing the results by chance if the null hypothesis is true. The significance level is set before data collection and represents the probability of incorrectly rejecting the null hypothesis. A p-value less than the significance level leads to rejecting the null hypothesis.

One-Sample Hypothesis Tests

A researcher tested the effectiveness of an herbal supplement on physical fitness using the Marine Physical Fitness Test. 25 college students took the supplement for 6 weeks and averaged a score of 38.68 on the test, compared to the average population score of 35. Using a t-test with α=.05 and df=24, the researcher found the average score of 38.68 was not significantly different than the population mean of 35 (t=2.041, p=.052). Therefore, there is not enough evidence to conclude the supplement had an effect on fitness levels, as the higher average score could be due to chance for this small sample.

Non parametric tests by meenu

This document provides an overview of non-parametric statistical tests. It discusses tests such as the chi-square test, Wilcoxon signed-rank test, Mann-Whitney test, Friedman test, and median test. These tests can be used with ordinal or nominal data when the assumptions of parametric tests are not met. The document explains the appropriate uses and procedures for each non-parametric test.

Analysing a hypothetical data with t-test in excel

This document provides steps for analyzing data using a t-test in Excel. It introduces the topic of hypothesis testing and the specific problem of comparing the daily study hours of female and male students. The null and alternative hypotheses are defined, with the null being that the mean study hours are equal between groups. The document then outlines the 5 steps to perform a t-test in Excel: 1) inputting the data, 2) accessing the data analysis tool, 3) selecting the t-test option and inputting the data ranges and hypothesized mean difference, 4) viewing the output, and 5) analyzing the results. The t-test results fail to reject the null hypothesis, indicating the mean study hours are not significantly different between female

Introduction to Hypothesis Testing

This document provides an overview of hypothesis testing including:
1) The four steps of hypothesis testing - stating hypotheses, setting criteria, collecting data, and making a decision. It also discusses types of errors.
2) Factors that influence the outcome like effect size, sample size, and variability. Larger effects, samples, and less variability make rejecting the null hypothesis more likely.
3) Direction hypotheses tests where the alternative predicts a direction of the effect. This allows rejecting the null with smaller differences but in the predicted direction.
4) Effect size measures like Cohen's d provide information beyond just significance. Statistical power is the probability of correctly rejecting a false null hypothesis.

Inferential statistics

Inferential statistics

Emil Pulido on Quantitative Research: Inferential Statistics

Emil Pulido on Quantitative Research: Inferential Statistics

Advanced statistics

Advanced statistics

Chi square

Chi square

Parametric & non parametric

Parametric & non parametric

INFERENTIAL STATISTICS: AN INTRODUCTION

INFERENTIAL STATISTICS: AN INTRODUCTION

Non parametric test

Non parametric test

Data analysis powerpoint

Data analysis powerpoint

t distribution, paired and unpaired t-test

t distribution, paired and unpaired t-test

Parametric and non parametric test in biostatistics

Parametric and non parametric test in biostatistics

Presentation chi-square test & Anova

Presentation chi-square test & Anova

Basics of Hypothesis testing for Pharmacy

Basics of Hypothesis testing for Pharmacy

Small sample test

Small sample test

Significance test

Significance test

One-Sample Hypothesis Tests

One-Sample Hypothesis Tests

Non parametric tests by meenu

Non parametric tests by meenu

Analysing a hypothetical data with t-test in excel

Analysing a hypothetical data with t-test in excel

Introduction to Hypothesis Testing

Introduction to Hypothesis Testing

Inferential Statistics

This document provides an overview of inferential statistics and statistical tests that can be used, including correlation tests, t-tests, and how to determine which tests are appropriate. It discusses the assumptions of parametric tests like Pearson's correlation and t-tests, and how to check assumptions graphically and using statistical tests. Specific procedures for conducting correlation analyses in Excel and SPSS are outlined, along with how to interpret and report the results.

The effect birth date has on choosing to study a sports related course at an ...

The Relative Age Effect (RAE) in the academic study of sport. The aim of this study was to investigate the relationship of those students being born on a certain date will indeed influence the decision to study a sports related course at an educational institution.

Ch12 (1)

This chapter discusses inferential statistics and the concepts underlying them. It covers key topics like types of inferential statistics (parametric vs nonparametric), important perspectives like generalizing from samples to populations, underlying concepts like null/alternative hypotheses and types of errors. Specific statistical techniques are explained like t-tests, ANOVA, regression, along with key ideas like sampling distributions, standard error, degrees of freedom, and the steps to conduct statistical tests. Different types of samples and issues with gain scores are also addressed.

L10 confidence intervals

This document discusses confidence intervals for estimating population means from sample data. It begins by explaining how to calculate point estimates and confidence intervals when the sample size is large (n ≥ 30) using the normal distribution. It then covers calculating confidence intervals when the sample size is small (n < 30) using the t-distribution. The key steps covered are determining the appropriate distribution to use based on sample size and knowledge of the population standard deviation, finding the critical values and margin of error, and calculating the confidence interval. Examples are provided to demonstrate how to construct confidence intervals in different situations.

Statistic formulas

This document defines statistical formulas for descriptive statistics like mean, variance, and standard deviation. It also defines formulas for hypothesis testing including z-scores, t-tests, confidence intervals, and proportions. Additionally, it outlines formulas for regression analysis like the least squares regression line, residuals, coefficient of determination, standard errors, confidence levels, and prediction intervals.

Median

The median is the middlemost score when values are arranged from lowest to highest. It divides the data set into two equal groups, with scores above and below the median. The median is not affected by extreme values and can be used when the mean would be skewed. To find the median of ungrouped data, arrange values from highest to lowest and take the middle value. For grouped data, use the formula Median = Ll + cfb/f, where Ll is the lower limit of the class containing N/2, cfb is the cumulative frequency below the assumed median, and f is the corresponding frequency.

Inferential statistics

Inferential statistics allow researchers to make generalizations about populations based on samples. Some key inferential statistical techniques discussed in the document include hypothesis testing using t-tests, chi-square tests, and regression analysis. The document provides a brief history of inferential statistics and outlines the process for hypothesis testing, including defining the null and alternative hypotheses, determining the level of significance, calculating test statistics, and drawing conclusions. It also discusses types of errors that can occur in sampling and hypothesis testing.

numerical method in statistics (MEAN AND MEDIAN)

This document provides formulas and examples for calculating the mean and median of data sets. It defines the mean as the sum of all values divided by the total number of data points. For grouped data, the mean is calculated as the sum of the frequency multiplied by the midpoint of each class, divided by the total number of data points. Examples are provided to demonstrate calculating the mean of both ungrouped and grouped data sets. The median is defined for grouped data as the lower boundary of the class containing the median plus the class width times the amount the cumulative frequency is less than the halfway point, divided by the total number of data points. An example is given to demonstrate calculating the median of a grouped data set.

Inferential statistics (2)

This document discusses various statistical techniques used for inferential statistics, including parametric and non-parametric techniques. Parametric techniques make assumptions about the population and can determine relationships, while non-parametric techniques make few assumptions and are useful for nominal and ordinal data. Commonly used parametric tests are t-tests, ANOVA, MANOVA, and correlation analysis. Non-parametric tests mentioned include Chi-square, Wilcoxon, and Friedman tests. Examples are provided to illustrate the appropriate uses of each technique.

Inferential statistics powerpoint

This document provides an introduction to inferential statistics, including key terms like test statistic, critical value, degrees of freedom, p-value, and significance. It explains that inferential statistics allow inferences to be made about populations based on samples through probability and significance testing. Different levels of measurement are discussed, including nominal, ordinal, and interval data. Common inferential tests like the Mann-Whitney U, Chi-squared, and Wilcoxon T tests are mentioned. The process of conducting inferential tests is outlined, from collecting and analyzing data to comparing test statistics to critical values to determine significance. Type 1 and Type 2 errors in significance testing are also defined.

Introduction to Statistics

This presentation includes an introduction to statistics, introduction to sampling methods, collection of data, classification and tabulation, frequency distribution, graphs and measures of central tendency.

Statistics lesson 1

Statistics involves collecting, organizing, analyzing, and interpreting data. Descriptive statistics describe characteristics of a data set through measures like central tendency and variability. Inferential statistics draw conclusions about a population based on a sample. Key terms include population, sample, parameter, statistic, data types, levels of measurement, and sampling techniques like simple random sampling. Common data gathering methods are interviews, questionnaires, and registration records. Data can be presented textually, in tables, or graphically through charts, graphs, and maps.

Inferential Statistics

Inferential Statistics

The effect birth date has on choosing to study a sports related course at an ...

The effect birth date has on choosing to study a sports related course at an ...

Ch12 (1)

Ch12 (1)

L10 confidence intervals

L10 confidence intervals

Statistic formulas

Statistic formulas

Median

Median

Inferential statistics

Inferential statistics

numerical method in statistics (MEAN AND MEDIAN)

numerical method in statistics (MEAN AND MEDIAN)

Inferential statistics (2)

Inferential statistics (2)

Inferential statistics powerpoint

Inferential statistics powerpoint

Introduction to Statistics

Introduction to Statistics

Statistics lesson 1

Statistics lesson 1

Stats - Intro to Quantitative

This document provides an overview of descriptive and inferential statistics concepts. It discusses parameters versus statistics, descriptive versus inferential statistics, measures of central tendency (mean, median, mode), variability (standard deviation, range), distributions (normal, positively/negatively skewed), z-scores, correlations, hypothesis testing, t-tests, ANOVA, chi-square tests, and presenting results. Key terms like alpha levels, degrees of freedom, effect sizes, and probabilities are also introduced at a high level.

Statistical analysis

The document defines various statistical measures and types of statistical analysis. It discusses descriptive statistical measures like mean, median, mode, and interquartile range. It also covers inferential statistical tests like the t-test, z-test, ANOVA, chi-square test, Wilcoxon signed rank test, Mann-Whitney U test, and Kruskal-Wallis test. It explains their purposes, assumptions, formulas, and examples of their applications in statistical analysis.

Test of significance in Statistics

This document provides an overview of statistical tests of significance used to analyze data and determine whether observed differences could reasonably be due to chance. It defines key terms like population, sample, parameters, statistics, and hypotheses. It then describes several common tests including z-tests, t-tests, F-tests, chi-square tests, and ANOVA. For each test, it outlines the assumptions, calculation steps, and how to interpret the results to evaluate the null hypothesis. The goal of these tests is to determine if an observed difference is statistically significant or could reasonably be expected due to random chance alone.

Statistical analysis.pptx

1. Statistical analysis involves collecting, organizing, analyzing data, and drawing inferences about populations based on samples. It includes both descriptive and inferential statistics.
2. The document defines key terms used in statistical analysis like population, sample, statistical analysis, and discusses various statistical measures like mean, median, mode, interquartile range, and standard deviation.
3. The purposes of statistical analysis are outlined as measuring relationships, making predictions, testing hypotheses, and summarizing results. Both parametric and non-parametric statistical analyses are discussed.

Comparing means

This document discusses statistical methods for comparing means, including t-tests and analysis of variance (ANOVA). It explains how t-tests can be used to compare two means or paired samples, and how ANOVA can compare two or more means. Key assumptions and procedures are outlined for one-sample t-tests, paired t-tests, independent t-tests with equal and unequal variances, and one-way between-subjects ANOVAs.

Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf

This document discusses various statistical tests used to analyze dental research data, including parametric and non-parametric tests. It provides information on tests of significance such as the t-test, Z-test, analysis of variance (ANOVA), and non-parametric equivalents. Key points covered include the differences between parametric and non-parametric tests, assumptions and applications of the t-test, Z-test, ANOVA, and non-parametric alternatives like the Mann-Whitney U test and Kruskal-Wallis test. Examples are provided to illustrate how to perform and interpret common statistical analyses used in dental research.

The t Test for Two Related Samples

Chapter 11 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed

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The document discusses different types of t-tests used to compare means:
- One-sample t-test compares a sample mean to a predefined value.
- Paired (dependent) t-test compares means of two conditions with the same participants.
- Independent t-test compares means of two unrelated groups.
It explains how to choose the appropriate t-test based on research design, number of means being compared, and data distribution. Formulas are provided for calculating each t-test statistic. Examples are given to demonstrate applying the one-sample and paired t-tests.

Statistics

This document provides an overview of common statistical tests used in dentistry research. It first describes descriptive statistics like measures of central tendency, dispersion, position, and outliers. It then discusses inferential statistics including parametric tests like t-tests and ANOVA that assume normal distributions, and non-parametric tests that make fewer assumptions. Specific parametric tests covered are the independent and paired t-tests and ANOVA. Non-parametric tests discussed include the chi-square, Wilcoxon, Mann-Whitney U, and Kruskal-Wallis tests. The document also briefly explains correlation/regression and measures of effect size like relative risk and odds ratios.

Measure of Variability Report.pptx

The document discusses measures of variability in statistics including range, interquartile range, standard deviation, and variance. It provides examples of calculating each measure using sample data sets. The range is the difference between the highest and lowest values, while the interquartile range is the difference between the third and first quartiles. The standard deviation represents the average amount of dispersion from the mean, and variance is the average of the squared deviations from the mean. Both standard deviation and variance increase with greater variability in the data set.

Statistic and orthodontic by almuzian

This document discusses different types of data and statistical tests used in orthodontics. It outlines categorical (qualitative) and numerical (quantitative) data, including nominal, ordinal, discrete, and continuous variables. Appropriate statistical tests are described for each data type, such as chi square tests for categorical data and t-tests or ANOVA for numerical data. Key concepts in data summarization are also covered, including measures of central tendency, variability, normal distribution, correlation, and hypothesis testing. The importance of selecting the right analysis method based on data type is emphasized.

Stats-Review-Maie-St-John-5-20-2009.ppt

I do not have enough information to determine what percentage of residents are asleep now versus at the beginning of this talk. As an AI assistant without direct observation of the audience, I do not have data on individual residents' states of alertness over time.

Inferential Statistics.pptx

1. An independent samples t-test was conducted to determine if there were differences in anxiety scores between male and female participants before a major competition.
2. The results of the t-test showed no significant difference between the mean anxiety scores of males (M=17, SD=4.58) and females (M=18, SD=3.16), t(8)=0.41, p>0.05.
3. Therefore, the null hypothesis that there is no difference between male and female anxiety scores before a major competition was not rejected.

Stat2013

The document provides information about performing chi-square tests and choosing appropriate statistical tests. It discusses key concepts like the null hypothesis, degrees of freedom, and expected versus observed values. Examples are provided to illustrate chi-square tests for goodness of fit and comparison of proportions. The document also compares parametric and non-parametric tests, providing examples of when each would be used.

Non parametric-tests

The document discusses several non-parametric tests that can be used as alternatives to parametric tests when the assumptions of parametric tests are violated. Specifically, it discusses:
1. The sign test and one sample median test, which can be used instead of t-tests when the data is skewed or not normally distributed.
2. Mood's median test, which compares the medians of two independent samples and is the nonparametric version of a one-way ANOVA.
3. The Kruskal-Wallis test, which determines if there are differences in medians across three or more groups and is the nonparametric version of a one-way ANOVA.

NON-PARAMETRIC TESTS.pptx

• Non parametric tests are distribution free methods, which do not rely on assumptions that the data are drawn from a given probability distribution. As such it is the opposite of parametric statistics
• In non- parametric tests we do not assume that a particular distribution is applicable or that a certain value is attached to a parameter of the population.
When to use non parametric test???
1) Sample distribution is unknown.
2) When the population distribution is abnormal
Non-parametric tests focus on order or ranking
1) Data is changed from scores to ranks or signs
2) A parametric test focuses on the mean difference, and equivalent non-parametric test focuses on the difference between medians.
1) Chi – square test
• First formulated by Helmert and then it was developed by Karl Pearson
• It is both parametric and non-parametric test but more of non - parametric test.
• The test involves calculation of a quantity called Chi square.
• Follows specific distribution known as Chi square distribution
• It is used to test the significance of difference between 2 proportions and can be used when there are more than 2 groups to be compared.
Applications
1) Test of proportion
2) Test of association
3) Test of goodness of fit
Criteria for applying Chi- square test
• Groups: More than 2 independent
• Data: Qualitative
• Sample size: Small or Large, random sample
• Distribution: Non-Normal (Distribution free)
• Lowest expected frequency in any cell should be greater than 5
• No group should contain less than 10 items
Example: If there are two groups, one of which has received oral hygiene instructions and the other has not received any instructions and if it is desired to test if the occurrence of new cavities is associated with the instructions.
2) Fischer Exact Test
• Used when one or more of the expected counts in a 2×2 table is small.
• Used to calculate the exact probability of finding the observed numbers by using the fischer exact probability test.
3) Mc Nemar Test
• Used to compare before and after findings in the same individual or to compare findings in a matched analysis (for dichotomous variables).
Example: comparing the attitudes of medical students toward confidence in statistics analysis before and after the intensive statistics course.
4) Sign Test
• Sign test is used to find out the statistical significance of differences in matched pair comparisons.
• Its based on + or – signs of observations in a sample and not on their numerical magnitudes.
• For each subject, subtract the 2nd score from the 1st, and write down the sign of the difference.
It can be used
a. in place of a one-sample t-test
b. in place of a paired t-test or
c. for ordered categorial data where a numerical scale is inappropriate but where it is possible to rank the observations.
5) Wilcoxon signed rank test
• Analogous to paired ‘t’ test
6) Mann Whitney Test
• similar to the student’s t test
7) Spearman’s rank correlation - similar to pearson's correlation.

t Test- Thiyagu

This presentation describes the concept of One Sample t-test, Independent Sample t-test and Paired Sample t-test. This presentation also deals about the procedure to do the t-test through SPSS.

Statistics for Librarians, Session 2: Descriptive statistics

The second in a series of four seminars presented to University of North Texas librarians. This presentation focuses on organizing and presenting basic descriptive statistics, including measures of central tendency and variation.

Session 3&4.pptx

The document provides an overview of descriptive statistics techniques for summarizing data, including:
- Numerical summaries like mean, median, and standard deviation to describe variables.
- Frequency distributions and graphical displays like histograms and scatterplots to visualize the distribution of one or two variables.
- Crosstabulations and bar charts to summarize relationships between two categorical variables.
The document discusses choosing appropriate graphical displays and provides examples of common statistical concepts like measures of center, spread, and association.

Chi square test final

The document provides information about the Chi Square test, including:
- It is one of the most widely used statistical tests in research.
- It compares observed frequencies to expected frequencies to test hypotheses about categorical variables.
- The key steps are defining hypotheses, calculating the test statistic, determining the degrees of freedom, finding the critical value, and making a conclusion by comparing the test statistic to the critical value.
- It can be used for goodness of fit tests, tests of homogeneity of proportions, and tests of independence between categorical variables. Examples of applications in cohort studies, case-control studies, and matched case-control studies are provided.

Stats - Intro to Quantitative

Stats - Intro to Quantitative

Statistical analysis

Statistical analysis

Test of significance in Statistics

Test of significance in Statistics

Statistical analysis.pptx

Statistical analysis.pptx

Comparing means

Comparing means

Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf

Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf

The t Test for Two Related Samples

The t Test for Two Related Samples

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Statistics

Statistics

Measure of Variability Report.pptx

Measure of Variability Report.pptx

Statistic and orthodontic by almuzian

Statistic and orthodontic by almuzian

Stats-Review-Maie-St-John-5-20-2009.ppt

Stats-Review-Maie-St-John-5-20-2009.ppt

Inferential Statistics.pptx

Inferential Statistics.pptx

Stat2013

Stat2013

Non parametric-tests

Non parametric-tests

NON-PARAMETRIC TESTS.pptx

NON-PARAMETRIC TESTS.pptx

t Test- Thiyagu

t Test- Thiyagu

Statistics for Librarians, Session 2: Descriptive statistics

Statistics for Librarians, Session 2: Descriptive statistics

Session 3&4.pptx

Session 3&4.pptx

Chi square test final

Chi square test final

Learn SQL from basic queries to Advance queries

Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics

一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理

毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(Chester毕业证书)切斯特大学毕业证如何办理

毕业原版【微信:41543339】【(Chester毕业证书)切斯特大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

State of Artificial intelligence Report 2023

Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.

一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理

毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Natural Language Processing (NLP), RAG and its applications .pptx

1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.

一比一原版(UofS毕业证书)萨省大学毕业证如何办理

原版定制【微信:41543339】【(UofS毕业证书)萨省大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理

原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

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一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理

毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(UniSA毕业证书)南澳大学毕业证如何办理

原版定制【微信:41543339】【(UniSA毕业证书)南澳大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理

UIUC毕业证offer【微信95270640】☀《伊利诺伊大学|厄巴纳-香槟分校毕业证购买》GoogleQ微信95270640《UIUC毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围：
★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《伊利诺伊大学|厄巴纳-香槟分校大学毕业证》Q微信95270640《UIUC学位证书购买》
(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理伊利诺伊大学|厄巴纳-香槟分校伊利诺伊大学|厄巴纳-香槟分校毕业证offer流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气间

一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理

毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(UO毕业证)渥太华大学毕业证如何办理

UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多

06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...

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A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found

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Presentation

一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理

原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

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一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理

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- 3. SESSION OBJECTIVES Purpose of Inferential Statistics Probability Elements of Significance Testing Three key tests • T-test • Chi-squared • Correlation (or binomial) Effect Measures
- 4. PURPOSE OF INFERENTIAL STATISTICS • Infer results • Draw conclusions • Increase the Signal-Noise ratio Signal Noise
- 5. INFERENTIAL STATISTICS Tests of hypotheses • Expectations • Associations Accounts for uncertainty • Random error • Confidence interval
- 7. NOT TO PROVE, BUT TO FALSIFY H1 Difference H0 No Difference
- 8. NOT TO PROVE, BUT TO FALSIFY H1 >=10% Increase H0 <10% Increase
- 10. LEVELS OF MEASUREMENT (NOIR) Nominal •Counts by category •Binary (Yes/No) •No meaning between the categories (Blue is not better than Red) Ordinal •Ranks •Scales •Space between ranks is subjective Interval •Integers •Zero is just another value – doesn’t mean “absence of” •Space between values is equal and objective, but discrete Ratio •Interval data with a baseline •Zero (0) means “absence of” •Space between is continuous •Includes simple counts
- 12. CENTRAL TENDENCY BY LEVELS OF MEASUREMENT Interval or Ratio Mean Median Nominal or Rank Mode Median (rank only)
- 13. SPREAD Interval & Ratio •Range •Quantiles •Standard Deviation Nominal & Rank •Distribution Tables •Bar Graphs How variable is the data?
- 16. PROBABILITY WHAT’S PROBABILITY GOT TO DO WITH STATISTICS?
- 17. WHAT IS PROBABILITY? Chance of something happening (x) Expressed as P(x)=y Between 0 and 1 Based on distribution of events
- 18. STEM-AND-LEAF Stem Leaf 0 0111222222222222223333334444555666 6677788899 1 0000000011122223333356778899 2 00122234444799 3 0245 Groups Last digit Years at UNT 0 5 13 1 6 13 1 6 13 1 6 13 2 6 15 2 6 16 2 7 17 2 7 17 2 7 18 2 8 18 2 8 19 3 11 29 4 11 29 4 12 30 4 12 32 4 12 34 5 12 35 5 13
- 19. Stem Leaf Count 0 1122223334445555666666677777899 31 1 000011122222222333346677889 27 2 0122234468 10 3 1112355888 11 4 12 2 Range Count 0-9 31 10-19 27 20-29 10 30-39 11 40-49 2 0 10 20 30 40 0-9 10-19 20-29 30-39 40-49 Histogram of Years at UNT
- 22. Set the mean to 0 Standard Deviations above and below the mean
- 23. DEMONSTRATION OF DISTRIBUTIONS Distribution of the Population The “Truth” N is the # of samples n is the number of items in each sample Watch the cumulative mean & medians slowly merge to the population
- 24. ACTIVITIES
- 25. CASE STUDY • Background: Info-Lit course is meeting resistance from skeptical faculty. • Research Questions: • Does the IL course improve grades on final papers? • Can the IL course improve passing rates for the course? • Do students in different majors respond differently to the IL training? • Is the final score related to the number of credit hours enrolled for each student?
- 26. METHODOLOGY Selection • Two sections of same course with different instructors. • Random Assignment Outcome • Blinded scoring by 2 TAs • Scores range from 1-100 • Passing grade: 70
- 27. ACTIVITIES Table 1 • Distribution of scores Table 2 • Distribution of passing rates by major Table 3 • Correlation of scores with credit hours
- 28. DESCRIPTIVE STATISTICS OF CASE STUDY
- 29. DISTRIBUTION OF SCORES Table 1 •Distribution of scores Table 2 •Distribution of passing rates by broad field of major Table 3 •Correlation of scores & credit hours
- 31. SIGNIFICANCE TESTING • Groups against each other • A group against the population or standard Comparing significance of differences • Risk of being wrong • Alpha (α) • Set in advance What is “significant”? • The value that the statistic must meet or exceed to be statistically significant. • Based on statistic and α Critical Value
- 32. STEPS IN SIGNIFICANCE TESTING Which Test? Calculate Statistic Critical Value of Statistic? Probability (p-value)
- 33. KEY ELEMENTS OF SIGNIFICANCE TESTING Null Hypothesis Measure of Central Tendency Standard deviations Risk of being wrong (alpha) • Usually .05 or .025 or .01 or .001 Degrees of freedom (df)
- 34. DEGREES OF FREEDOM Number of values in the final calculation of a statistic that are free to vary.
- 35. DEGREES OF FREEDOM EXPLAINED • All these have a mean of 5: • 5, 5, 5 • 2, 8, 5 • 3, 2, 10 • 7, 4, & ? • If 2 values are known and the mean is known, then the 3rd value is also known. • Only 2 of the 3 values are free to vary.
- 36. CALCULATING DEGREES OF FREEDOM (DF) For a single sample: • Degrees of freedom (df) for t-test = n-1 For more than one group: • df=∑(n-1) for all groups (k) • OR, ∑ n-k For comparing proportions in categories (k): • df= ∑k-1 (# of categories minus 1)
- 38. T-TEST Used with interval or ratio data Based on normal distribution Four Decisions • Paired or un-paired samples? • Equal or unequal variances (standard deviations)? • Risk? • One- or two-tail? • Direction of expected difference • Best to bet on difference in both directions (2-tail)
- 40. T-TEST FORMULA FOR UNPAIRED SAMPLES 𝑡 = 𝑥1 − 𝑥2 𝑆 𝑥1−𝑥2 Signal Noise Difference Between Group Means 𝑉𝑎𝑟𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑜𝑓 𝐺𝑟𝑜𝑢𝑝𝑠
- 41. ELEMENTS OF T-TEST USING EXCEL DATA ANALYSIS TOOLPAK •UnpairedPaired or Unpaired samples? •Equal*Equal or Unequal Variances? •Data for intervention group •Data for control group Data •0Hypothesized difference •0.025 (for a 2-tail test) Alpha
- 42. T-TEST IN EXCEL
- 43. READING T-TEST RESULTS ∑(n-1) = (51-1)+(50-1) = 50+49=99 <=0.025?
- 44. IS THE DIFFERENCE SIGNIFICANT? p=0.0005
- 45. TESTING DISTRIBUTION OF NOMINAL DATA
- 46. PEARSON’S CHI-SQUARED (Χ2) GOODNESS OF FIT TEST Does an observed frequency distribution differ from an expected distribution • Observed is the sample or the intervention. • Expected is the population or the control or a theoretical distribution. • Will depend on your Null Hypothesis Nominal or categorical data • Counts by category
- 47. EXPECTED RATIOS FOR CASE STUDY Research Question: •Do students in different majors respond differently to the IL training? Null Hypothesis •The ratio of students who passed will be the same for all majors.
- 48. WHEN TO USE PEARSON’S CHI- SQUARED GOODNESS OF FIT TEST Nominal Data Sample Size • Not too large: • Sample is at most 1/10th of population • Not too small: • At least five in each of the categories for the expected group.
- 49. OBSERVED PASSING RATES BY MAJOR Major Passed Not Passed Grand Total Arts 6 7 13 Humanities 8 5 13 Social Sciences 17 10 27 STEM 20 5 25 Undeclared 16 7 23 Total 67 34 101
- 50. EXPECTED RATIOS OF PASSING RATES BY MAJOR • H0: Rates of passing will be the same for all majors. • Expected rates: 70% of class passes. • Expected ratios: 70% of each major passes. Major Passed Not Passed Grand Total Arts 11.2 (16*.7) 4.8 16 Humanities 11.2 (16*.7) 4.8 16 Social Sciences 18.2 (26*.7) 7.8 26 STEM 16.1 (23*.7) 6.9 23 Undeclared 14 (20*.7) 6 20
- 51. CHI-SQUARED GOF TEST FORMULA •χ2 = 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑−𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 2 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 • Critical value of Chi-squared depends on degrees of freedom. • Degrees of freedom • Based on the number of categories or table cells (k) • df=k-1
- 52. CHI-SQUARED IN EXCEL What is Null Hypothesis? There is no difference between the majors regarding passing rates. What is your alpha (risk)? 0.05 Data in a summary tables? Actual Ratios Expected Ratios Excel function: =CHISQ.TEST(actual range1,expected range2) Provides a p-value 0.0000172 Is p-value <= alpha? Yes
- 53. CORRELATION OF SCORE & SEMESTER HOURS ENROLLED
- 54. STATISTICAL CORRELATION Quantitative value of relationship of 2 variables •-1 represents a perfect indirect correlation •0 represents no correlation •+1 represents a perfect direct correlation Expressed in range of -1 to +1 •How much two variables change together Based on co-variance
- 55. PEARSON’S PRODUCT MOMENT CORRELATION COEFFICIENT Most commonly used statistic Normally distributed interval or ratio data only Labeled as r Multiplication = Interaction Signal Noise 𝑟𝑥𝑦 = 𝑥 − 𝑥 𝑦 − 𝑦 𝑛 − 1 𝑠 𝑥 𝑠 𝑦
- 56. CORRELATION IN EXCEL •No correlation Null Hypothesis? •=PEARSON(range1,range2) Coefficient function (r): Does NOT have a single function to test for significance Calculate Probability: n # in sample 101 df # in sample - 2 99 alpha 0.025 for 2-Tail Test 0.025 r =PEARSON(range1,range2) 0.362287 t =r*SQRT(alpha)/SQRT(1-r^2) 3.867434 p =T.DIST.2T(t,df) 0.000197
- 57. CORRELATIONS FOR ORDINAL DATA Spearman’s ϱ (rho) •Use if there are limited ties in rank. Kendall’s τ (tau) •Use if you have a number of ties.
- 59. KNOW THE TESTS Assumptions Limitations Appropriate data type What the test tests
- 60. FACTORS ASSOCIATED WITH CHOICE OF STATISTICAL METHOD Level of Measurement What is being compared Independence of units Underlying variance in the population Distribution Sample size Number of comparison groups
- 61. USE A FLOW CHART
- 62. GOING BEYOND THE P- VALUE EFFECT SIZES
- 63. AND THE P-VALUE SAYS… Much about the distributions More about the H0 than H1 Little about size of differences
- 64. MORE USEFUL STATISTICS Effect Sizes •Tell the real story Confidence Intervals •State your certainty
- 65. EFFECT SIZES OF QUANTITATIVE DATA Differences from the mean • Standardized • weighted against the pooled (average) standard deviation • Cohen’s d Correlations • Cohen’s guidelines for Pearson’s r • r = 0.362 Effect Size r> Small .10 Medium .30 Large .50 𝑑 = 𝑥1 − 𝑥2 𝑠 𝑥1,𝑥2 𝑑 = 79.47 − 69.56 11.8036 𝑑 = 0.8392
- 66. EFFECT SIZES OF QUALITATIVE DATA Based on Contingency table • Uses probabilities • 𝑅𝑅 = 𝑎 𝑎+𝑏 𝑐 𝑐+𝑑 Relative risk • 𝑅𝑅 = 41∗65 10∗36 •RR = 1.608 •The passing rate for the intervention group was 1.6 times the passing rate for control group. RR of Case Study Pass No Pass Total Intervention a (41) b (24) a+b (65) Control c (26) d (10) c+d (36) Totals a+c (67) b+d (34) a+b+c+d (101)
- 67. CONFIDENCE INTERVALS Point estimates Intervals Based on Expressed as: •Single value •Mean •Degree of uncertainty •Range of certainty around the point estimate •Point estimate (e.g. mean) •Confidence level (usually .95) •Standard deviation •The mean score of the students who had the IL training was 79.5 with a 95% CI of 76.4 and 82.5.
- 68. CASE STUDY CONCLUSIONS • Research Questions: • Could the IL course improve grades on final papers? • Could the IL course improve passing rates for the course? • Do students in different majors respond differently to the IL training? • Is the final score related to the number of credit hours enrolled for each student? • Control for external variables