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Multivariate regression is also known as a system of regression equations. We use it to identify and understand the relationship between multiple variables. This essential statistical method is useful when it comes to estimating housing prices, the effects of economic policies, or general market trends. Multivariate ...

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Basic Concepts

Statistics is an indispensable tool for any interested party, whether that means sports betting fans assessing the chances of their favorite team winning before making a bet, or businesses assessing the Biostatistics of an enterprise-wide initiative.

There are two primary areas of study within statistics, descriptive and inferential. Descriptive statistics seeks to describe properties of data such as its central tendency (mean) and variability (standard deviation), along with statistical measures like histogram and frequency distribution table as well as probability theory concepts such as skewness and kurtosis. Conversely, statistical inference draws conclusions about populations from information contained within a sample, using concepts like probability theory and hypothesis testing.

Inferential Statistics

Inferential statistics employ sample data from a larger population in order to draw inferences or draw conclusions about its entirety. While more complex than descriptive stats, inferential stats allow deeper insights into an entire group.

Say you are teaching a class full of students and want to determine if their average test scores compare favorably with state averages. Descriptive statistics may help provide this answer; however, inferential stats must also be utilized to ensure accurate results that apply across populations.

If the parties of your friend Sophia tend to draw a significant crowd but are less drunken than usual, the data can show whether there’s any correlation between party attendance and number of drinks served at them.

Time Series Analysis

Time series analysis plays a vital role in providing organizations with insights they can use to make more informed business decisions. From simple line charts depicting daily temperature readings, to advanced models predicting future trends and outcomes, time series analysis is utilized across numerous business sectors.

Time series analysis involves tracking relationships among data points over time at specific intervals, identifying trends, seasonality and cyclical variations to analyze its structure including autocorrelation and stationarity; differencing techniques may also be utilized to remove trends and seasonality while controlling autocorrelations and stationarity as well as impute missing data points depending on its nature.

Regression Analysis

Statistical regression involves exploring the relationships among variables to understand their relationships, explain a phenomenon or forecast items like costs or sales over time, as well as determine appropriate actions to take.

Retail businesses may want to know whether it is rain or a new promotion driving up sales, making sales increase harder to ascertain from correlation Data Analytics alone or even other methods such as trendlines. Unfortunately, it can be difficult to ascertain which factor has more of an effect than others on this change in sales numbers.

Regression analysis provides a useful method of disentangling complex relationships among variables. It also shows the relative significance of each independent variable – those with coefficients near zero may not contribute anything predictive to modelling dependent variables and are unlikely to have an influence.

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Statistics Assignment Examples

February 2, 2024

Introduction: Embarking on the intricate journey of statistics assignments can be both challenging and enlightening. To navigate this terrain successfully, gaining a deep understanding of statistical concepts is crucial. In this comprehensive exploration, we’ll delve into the world of statistics assignment examples, unraveling the complexities through real-world scenarios. Whether you’re a student seeking clarity for your assignments or an enthusiast eager to grasp the practical applications of statistics, join us on this illuminating journey with Statistics Homework Tutors. Together, we’ll dissect key statistical concepts using practical examples, providing you with the tools to master this field and transform challenges into triumphs.

The Essence of Descriptive Statistics: Shedding Light on Performance

Example 1: exam scores.

Imagine you’re a student in a class of 30, eagerly awaiting your math exam scores. Descriptive statistics come to the rescue, turning those numbers into meaningful insights:

Average (Mean): Calculating the sum of all scores and dividing by the number of students provides a snapshot of the overall performance.
Middle Value (Median): Arranging scores in ascending order and identifying the middle score offers a representation of central tendency.
Most Frequent Score (Mode): Spotting the score that appears most frequently gives a sense of the prevailing performance level.

This statistical approach transforms a seemingly random set of scores into a clear understanding of the class’s academic standing.

Harnessing the Power of Inferential Statistics: Predicting Outcomes

Example 2: opinion polls.

In the world of politics, predicting election outcomes is a task tailor-made for inferential statistics. Imagine conducting an opinion poll with 1,000 participants to gauge voter preferences:

Hypothesis Testing: Evaluating whether a particular candidate’s popularity significantly differs from others provides insights into voter sentiment.
Confidence Intervals: Estimating the range within which the true proportion of voters supporting a candidate lies adds a layer of certainty to predictions.

Inferential statistics empowers us to draw meaningful conclusions about a larger population based on a representative sample.

Business Optimization through Regression Analysis

Example 3: sales and advertising.

Enter the dynamic world of business, where regression analysis plays a pivotal role in optimizing advertising strategies. Consider a scenario where a company invests in advertising campaigns over several months:

Modeling Relationships: Establishing a mathematical relationship between advertising expenses and sales unveils the dynamics at play.
Predicting Outcomes: Anticipating the expected increase in sales for a given increase in advertising spending guides decision-making.

Regression analysis transforms data into actionable insights, aiding businesses in making informed choices to maximize their impact.

Quantifying Uncertainty with Probability Distributions

Example 4: coin toss.

Dive into the world of probability distributions with a simple coin toss. The unpredictability of this event is quantified through:

Likelihood of Outcomes: A fair coin offers a 50% chance of landing on heads or tails, illustrating the fundamental concept of probability.
Expected Value: The average outcome over many tosses converges to 0.5, showcasing the stability of probability predictions.

Understanding probability distributions is fundamental to making sense of uncertainties and predicting outcomes in various scenarios.

Real-World Impact: Statistics in Healthcare

Example 5: medical diagnosis.

In the critical realm of healthcare, statistics takes center stage in medical diagnosis. Consider a diagnostic test for a rare disease:

Sensitivity and Specificity: Evaluating the accuracy of the test in identifying true positives and true negatives helps in assessing its reliability.
False Positive Rate: Examining the likelihood of a healthy person receiving an incorrect diagnosis adds a layer of caution.

Statistics in healthcare guides medical professionals in interpreting test results, making accurate diagnoses, and ultimately improving patient outcomes.

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With these examples in mind, it’s crucial to acknowledge the role of Statistics Homework Tutors in enhancing your journey through the statistical landscape. As a student-centric platform, Statistics Homework Tutors provides:

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Conclusion: Your Journey to Statistical Mastery

As we conclude this comprehensive exploration of statistics and its real-world applications, it’s evident that mastering this field is not just about crunching numbers but understanding the stories they tell. Statistics Homework Tutors serves as a guiding light on your journey to statistical mastery, offering tailored support, expert guidance, and real-world examples that transform challenges into opportunities for growth.

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What Is SPSS? What Are Its Key Features And Uses/Applications

SPSS, short for the Statistical Package for the Social Sciences, is not just software; it's the secret decoder for the language of data. Crafted by IBM, SPSS is a tool preferred by researchers, statisticians, and analysts for its prowess in dissecting and making sense of data across various fields. Initially tailored for social science pursuits, SPSS finds its applications across diverse domains today.

Key Features:

SPSS, with its user-friendly interface, transforms complex data analysis into an accessible journey. Navigating data import, cleaning, and manipulation becomes a breeze, especially for those new to statistical analysis.
Its comprehensive set of statistical procedures, ranging from basic descriptive statistics to advanced analyses caters to users with varying expertise levels.
SPSS goes beyond historical data examination, delving into predictive analytics. Techniques like linear regression and decision trees allow users to construct and assess predictive models, adding a forward-looking dimension to data analysis.
Customization options and automation features bring flexibility, making SPSS a dynamic tool for tailored analyses and efficient workflows. Lastly, its robust reporting capabilities weave together statistical results, tables, and charts, simplifying findings presentation for clearer insights.

Uses/Applications:

In the world of psychology, sociology, and political science, SPSS aids researchers in deciphering nuanced data patterns and extracting meaningful insights.
Market researchers find SPSS a guiding light, unraveling consumer behavior, interpreting survey data, and guiding businesses with informed decision-making. Healthcare researchers lean on SPSS for clinical studies and epidemiological research.
Finance and banking sectors harness SPSS for robust financial analysis, risk assessment, and fraud detection, providing a data-driven compass for strategic decisions.
Government agencies rely on SPSS for evidence-backed policy decisions, demographic analyses, and a deeper understanding of societal trends.

SPSS is not just a tool; it's the companion guiding researchers and analysts enabling them to navigate the journey of analyzing data and turning it into meaningful insights.

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SPSS Assignment Helper - Learning Key Statistics Concepts With Us

Our experts at The Statistics Assignment Help cover a comprehensive array of topics to ensure a holistic understanding and mastery of SPSS for your assignments and homework.

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Regression Analysis : Application of regression models to explore relationships between variables and make predictions based on data patterns.
Factor Analysis: In-depth understanding and implementation of factor analysis to identify underlying factors influencing observed variables.
Correlation Analysis: Examination of relationships between variables through correlation analysis, utilizing tools within SPSS.
Chi-square Tests : Application of chi-square tests to analyze categorical data and assess associations between variables.
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Cluster Analysis: Exploration of cluster analysis techniques to identify natural groupings within datasets.
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How do I Input and Sort Data in SPSS? Many students find themselves scratching their heads when it comes to putting data into SPSS and making sure it's organized properly. They often seek clear guidance on data input methods, handling missing values, and ensuring accuracy for a smooth analysis.

I'm Stuck Choosing a Statistical Test. Can You Help Me Decide? Navigating through the array of statistical tests can be a daunting task, leaving students in a state of uncertainty about the most suitable choice. Frequently, they seek guidance to determine the optimal statistical test for their data – whether it involves t-tests, ANOVA, or regression – all with the goal of ensuring precision in their analysis.

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Decoding the Language of Sports: A Comprehensive Guide to Sports Statistics Assignments

Sports Statistics Uncovered: Analytical Solutions for Your Assignments

The marriage of sports and statistics is an intricate dance, a symbiotic relationship that transcends the mere realms of scoreboards and player performances. It is a fusion where data becomes the heartbeat of the game, pulsating with every swing of a bat, kick of a ball, or sprint down the track. This intricate web of numbers not only captures the essence of the game but also serves as a playground for analytical minds to unravel the stories concealed within the statistics. In the hallowed halls of academia, students often find themselves immersed in the world of sports statistics, grappling with assignments that demand a profound understanding of statistical analysis. It is in this arena that the beauty of numbers collides with the passion for sports, presenting a unique challenge and an opportunity for intellectual growth. The sheer volume of data generated in the sports domain can be overwhelming, ranging from player statistics and team performance to game strategies and historical trends. However, it is within this data deluge that the seeds of knowledge are sown, waiting to be nurtured into a comprehensive understanding of the game through statistical analysis.

The challenges that students face in comprehending sports statistics are manifold. The intricacies of statistical methods, the application of mathematical models, and the interpretation of results in the specific context of sports can create a formidable barrier. However, amidst the complexity, there is a guiding light. This comprehensive guide emerges as a beacon of clarity, promising to unravel the analytical solutions that will not only aid students in conquering their assignments but also cultivate a deeper appreciation for the intricate tapestry woven by sports and statistics. As students embark on this analytical journey, they are encouraged to shed any apprehensions. The guide seeks to demystify the complex world of sports statistics, offering a roadmap that transforms challenges into opportunities for learning and growth. It begins by laying a solid foundation, elucidating the fundamental role of statistics in defining the essence of sports. It is not just about the final score or the number of goals; it is about understanding the patterns, trends, and anomalies that make each game a unique narrative waiting to be told. So, whether you are struggling to complete your Statistics homework or aiming to deepen your understanding, this guide is designed to be your companion on this enlightening journey.

Exploring the Basics of Sports Statistics

Sports and statistics share an intrinsic relationship, with the latter serving as a powerful tool to dissect and comprehend the complexities of the former. Delving into the basics of sports statistics is akin to unlocking the door to a treasure trove of insights, where every number holds a story waiting to be told. In this exploration, we will navigate through the fundamental concepts that form the backbone of sports statistics, shedding light on how descriptive statistics and predictive modeling play pivotal roles in unraveling the narrative behind the numbers.

The Role of Descriptive Statistics in Sports

At the heart of statistical analysis in sports lies the indispensable realm of descriptive statistics. Whether you find yourself immersed in the world of baseball, dissecting batting averages, or navigating the intricacies of basketball, where shooting percentages reign supreme, descriptive statistics act as the guiding light. Picture them as the cartographers of sports data, sketching the landscape of central tendencies and variations within a dataset. When facing sports statistics assignments, the mastery of fundamental concepts such as mean, median, and mode becomes paramount. These statistical measures serve as the compass, directing analysts through the maze of numbers to uncover the underlying patterns. Imagine deciphering a team's performance over a season or understanding the consistency of a player's output – descriptive statistics offer a panoramic view of the terrain, enabling you to make sense of the apparent chaos within sports data.

To truly grasp the significance of descriptive statistics, one must dive into real-life examples. Imagine a baseball team's batting averages distributed across a season – the mean revealing the team's overall performance, the median offering a glimpse of the typical performance, and the mode pinpointing the most frequent performance level. Such examples not only illuminate the concepts but also showcase their application in making sense of the rich tapestry of sports statistics.

Predictive Modeling in Sports: An In-Depth Exploration

As we journey deeper into the world of sports statistics, the landscape expands to encompass the realm of predictive modeling. This facet of statistical analysis has undergone a revolution, transforming the way teams strategize and fans engage with their favorite sports. Predictive modeling isn't merely about foreseeing the outcome of a match; it extends to forecasting a player's performance, offering a glimpse into the future based on the historical tapestry of data. Regression analysis, machine learning, and an array of predictive modeling techniques stand as the architects of this foresight. Teams leverage these tools to craft strategies, optimize player line-ups, and anticipate the ebb and flow of a game. For sports statisticians, understanding the mechanics behind predictive models is akin to wielding a powerful instrument. It empowers analysts to make informed predictions, turning historical data into a crystal ball that reveals the potential trajectory of a team or player.

In the context of sports statistics assignments, this understanding becomes a valuable asset. Imagine tasked with predicting the win-loss record of a team for the upcoming season or forecasting the performance of a rookie player. Delving into the intricacies of predictive modeling equips students with the tools to navigate these challenges, bridging the gap between historical data and future outcomes. It's not just about numbers; it's about foreseeing the unfolding chapters in the story of sports through the lens of predictive analytics.

Advanced Concepts in Sports Analytics

In the ever-evolving landscape of sports analytics, advanced concepts have become the driving force behind strategic decisions, player evaluations, and overall game improvement. This section will explore two pivotal aspects of advanced sports analytics: Moneyball and Advanced Metrics in Baseball, and Player Tracking and Performance Analytics in Basketball.

Moneyball and Advanced Metrics in Baseball

The groundbreaking concept of Moneyball, made famous by Michael Lewis's book and subsequent film, "Moneyball," demonstrated how advanced metrics could reshape the traditional approach to baseball. This statistical revolution has redefined how teams assess player performance, moving beyond conventional statistics to delve into more nuanced and insightful metrics. Key metrics, such as Wins Above Replacement (WAR), have become instrumental in gauging a player's overall impact on the team. WAR measures a player's contribution in terms of wins, encompassing offensive and defensive aspects. On-base Plus Slugging (OPS) combines a player's on-base percentage with their slugging percentage, offering a comprehensive view of offensive prowess. Batting Average on Balls in Play (BABIP) quantifies the effectiveness of a batter's hits by considering only the balls put in play, excluding home runs and strikeouts.

For students navigating sports statistics assignments, understanding how these metrics operate is crucial. They not only provide a more detailed picture of player contributions but also serve as powerful tools for evaluating team dynamics. Whether dissecting the offensive capabilities of a star batter or assessing the defensive prowess of a pitcher, incorporating these advanced metrics adds depth and precision to statistical analyses.

Player Tracking and Performance Analytics in Basketball

Basketball, with its fast-paced nature and intricate player movements, has embraced the era of player tracking and performance analytics. This shift has ushered in a new dimension, allowing teams to leverage a wealth of data for strategic insights and player development. Player tracking involves monitoring and analyzing the movements of players on the court. This includes not only the traditional statistics like points, rebounds, and assists but also granular details such as the distance covered, speed, and changes in acceleration. By harnessing this information, teams gain a comprehensive understanding of player strengths and weaknesses.

Performance analytics in basketball extend beyond basic statistics, offering a nuanced perspective on player dynamics. Students delving into sports statistics assignments can explore how this data is harnessed to evaluate defensive capabilities, optimize offensive strategies, and even assess the impact of fatigue on player performance. For example, tracking a player's movements can unveil patterns in defensive strategies, helping teams refine their offensive plays. Additionally, understanding the correlation between workload and performance enables teams to manage player minutes more effectively, preventing injuries and maximizing overall team efficiency.

Practical Tips for Tackling Sports Statistics Assignments

As students venture into the intricate world of sports statistics, mastering the art of analysis is not merely about crunching numbers but also about effectively communicating the insights derived from these figures. In this section, we'll delve into practical tips that go beyond equations and formulas, offering a holistic approach to conquering sports statistics assignments.

Embrace Data Visualization Techniques

The journey into sports statistics often begins with a barrage of raw numbers, making it challenging to extract meaningful patterns and draw conclusive insights. This is where data visualization emerges as a powerful ally. It's not just about creating aesthetically pleasing charts; it's about transforming complex statistical findings into a visual language that speaks to your audience. Imagine dissecting batting averages or goal differentials without the aid of graphs or charts. The complexity of these numerical values can be daunting. Here's where the importance of data visualization shines. Through a variety of tools like Tableau and Excel, students can bring their data to life. These platforms provide dynamic features that enable the creation of insightful graphs, charts, and interactive dashboards.

Understanding how to leverage these tools allows students to craft compelling narratives out of raw data. Whether it's tracking the trajectory of a player's performance over the seasons or analyzing the impact of specific strategies on game outcomes, visual representations add a layer of clarity that traditional numerical formats often lack. This not only aids in personal comprehension but also ensures that the audience, be it teachers or peers, can grasp the essence of the statistical story being told. In the realm of sports statistics, a well-designed graph can speak volumes. It can highlight trends, showcase anomalies, and simplify complex relationships, making it an invaluable tool in your analytical arsenal. Embracing data visualization is not just a tip; it's a gateway to unlocking the true potential of your sports statistics assignments.

Collaborate and Engage with Real-World Data

Textbook examples provide a foundational understanding of statistical concepts, but real-world data adds a layer of complexity that can't be replicated in a controlled environment. To truly master sports statistics, students should step beyond the confines of theoretical exercises and immerse themselves in the dynamic landscape of actual sports data. One crucial aspect of this approach is collaboration with sports organizations. By establishing connections with teams, leagues, or analytics departments, students gain access to datasets that reflect the intricacies of professional sports. This firsthand exposure allows for a deeper understanding of the challenges statisticians face in the field, from dealing with data quality issues to adapting to the evolving nature of the sports industry.

Furthermore, leveraging open datasets provides a wealth of opportunities to explore diverse aspects of sports statistics. Whether it's analyzing the impact of weather conditions on game performance or studying the correlation between player demographics and success, real-world data introduces students to the unpredictable and multifaceted nature of sports analytics. This hands-on engagement not only enriches the learning experience but also equips students with practical skills that extend beyond the realm of assignments. The ability to navigate and derive insights from real-world data is a valuable asset in the professional landscape of sports analytics, making this tip a transformative step in the journey towards mastery.

The conclusion of our exploration into the fascinating world of sports statistics encapsulates the essence of this field – the harmonious union of analytical prowess and a genuine passion for the game. This marriage, we have discovered, serves as the key to unlocking a treasure trove of insights that transcends the mere numerical representation of sports data.

As we reflect on the journey through this comprehensive guide, it becomes evident that we've traversed the landscape of sports statistics from its foundational basics to advanced concepts, and finally, to practical tips that bridge the theoretical with the applicable. This journey isn't just about solving assignments; it's a holistic immersion into the multifaceted realm where numbers meet the heart and soul of sports.

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Choosing the Right Statistical Test | Types & Examples

Choosing the Right Statistical Test | Types & Examples

Published on January 28, 2020 by Rebecca Bevans . Revised on June 22, 2023.

Statistical tests are used in hypothesis testing . They can be used to:

determine whether a predictor variable has a statistically significant relationship with an outcome variable.
estimate the difference between two or more groups.

Statistical tests assume a null hypothesis of no relationship or no difference between groups. Then they determine whether the observed data fall outside of the range of values predicted by the null hypothesis.

If you already know what types of variables you’re dealing with, you can use the flowchart to choose the right statistical test for your data.

Statistical tests flowchart

What does a statistical test do, when to perform a statistical test, choosing a parametric test: regression, comparison, or correlation, choosing a nonparametric test, flowchart: choosing a statistical test, other interesting articles, frequently asked questions about statistical tests.

Statistical tests work by calculating a test statistic – a number that describes how much the relationship between variables in your test differs from the null hypothesis of no relationship.

It then calculates a p value (probability value). The p -value estimates how likely it is that you would see the difference described by the test statistic if the null hypothesis of no relationship were true.

If the value of the test statistic is more extreme than the statistic calculated from the null hypothesis, then you can infer a statistically significant relationship between the predictor and outcome variables.

If the value of the test statistic is less extreme than the one calculated from the null hypothesis, then you can infer no statistically significant relationship between the predictor and outcome variables.

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You can perform statistical tests on data that have been collected in a statistically valid manner – either through an experiment , or through observations made using probability sampling methods .

For a statistical test to be valid , your sample size needs to be large enough to approximate the true distribution of the population being studied.

To determine which statistical test to use, you need to know:

whether your data meets certain assumptions.
the types of variables that you’re dealing with.

Statistical assumptions

Statistical tests make some common assumptions about the data they are testing:

Independence of observations (a.k.a. no autocorrelation): The observations/variables you include in your test are not related (for example, multiple measurements of a single test subject are not independent, while measurements of multiple different test subjects are independent).
Homogeneity of variance : the variance within each group being compared is similar among all groups. If one group has much more variation than others, it will limit the test’s effectiveness.
Normality of data : the data follows a normal distribution (a.k.a. a bell curve). This assumption applies only to quantitative data .

If your data do not meet the assumptions of normality or homogeneity of variance, you may be able to perform a nonparametric statistical test , which allows you to make comparisons without any assumptions about the data distribution.

If your data do not meet the assumption of independence of observations, you may be able to use a test that accounts for structure in your data (repeated-measures tests or tests that include blocking variables).

Types of variables

The types of variables you have usually determine what type of statistical test you can use.

Quantitative variables represent amounts of things (e.g. the number of trees in a forest). Types of quantitative variables include:

Continuous (aka ratio variables): represent measures and can usually be divided into units smaller than one (e.g. 0.75 grams).
Discrete (aka integer variables): represent counts and usually can’t be divided into units smaller than one (e.g. 1 tree).

Categorical variables represent groupings of things (e.g. the different tree species in a forest). Types of categorical variables include:

Ordinal : represent data with an order (e.g. rankings).
Nominal : represent group names (e.g. brands or species names).
Binary : represent data with a yes/no or 1/0 outcome (e.g. win or lose).

Choose the test that fits the types of predictor and outcome variables you have collected (if you are doing an experiment , these are the independent and dependent variables ). Consult the tables below to see which test best matches your variables.

Parametric tests usually have stricter requirements than nonparametric tests, and are able to make stronger inferences from the data. They can only be conducted with data that adheres to the common assumptions of statistical tests.

The most common types of parametric test include regression tests, comparison tests, and correlation tests.

Regression tests

Regression tests look for cause-and-effect relationships . They can be used to estimate the effect of one or more continuous variables on another variable.

	Predictor variable	Outcome variable	Research question example
			What is the effect of income on longevity?
			What is the effect of income and minutes of exercise per day on longevity?
Logistic regression			What is the effect of drug dosage on the survival of a test subject?

Comparison tests

Comparison tests look for differences among group means . They can be used to test the effect of a categorical variable on the mean value of some other characteristic.

T-tests are used when comparing the means of precisely two groups (e.g., the average heights of men and women). ANOVA and MANOVA tests are used when comparing the means of more than two groups (e.g., the average heights of children, teenagers, and adults).

	Predictor variable	Outcome variable	Research question example
Paired t-test			What is the effect of two different test prep programs on the average exam scores for students from the same class?
Independent t-test			What is the difference in average exam scores for students from two different schools?
ANOVA			What is the difference in average pain levels among post-surgical patients given three different painkillers?
MANOVA			What is the effect of flower species on petal length, petal width, and stem length?

Correlation tests

Correlation tests check whether variables are related without hypothesizing a cause-and-effect relationship.

These can be used to test whether two variables you want to use in (for example) a multiple regression test are autocorrelated.

	Variables	Research question example
Pearson’s		How are latitude and temperature related?

Non-parametric tests don’t make as many assumptions about the data, and are useful when one or more of the common statistical assumptions are violated. However, the inferences they make aren’t as strong as with parametric tests.

	Predictor variable	Outcome variable	Use in place of…
Spearman’s
			Pearson’s
Sign test			One-sample -test
Kruskal–Wallis			ANOVA
ANOSIM			MANOVA
Wilcoxon Rank-Sum test			Independent t-test
Wilcoxon Signed-rank test			Paired t-test

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This flowchart helps you choose among parametric tests. For nonparametric alternatives, check the table above.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Descriptive statistics
Measures of central tendency
Correlation coefficient
Null hypothesis

Methodology

Cluster sampling
Stratified sampling
Types of interviews
Cohort study
Thematic analysis

Research bias

Implicit bias
Cognitive bias
Survivorship bias
Availability heuristic
Nonresponse bias
Regression to the mean

Statistical tests commonly assume that:

the data are normally distributed
the groups that are being compared have similar variance
the data are independent

If your data does not meet these assumptions you might still be able to use a nonparametric statistical test , which have fewer requirements but also make weaker inferences.

A test statistic is a number calculated by a statistical test . It describes how far your observed data is from the null hypothesis of no relationship between variables or no difference among sample groups.

The test statistic tells you how different two or more groups are from the overall population mean , or how different a linear slope is from the slope predicted by a null hypothesis . Different test statistics are used in different statistical tests.

Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test . Significance is usually denoted by a p -value , or probability value.

Statistical significance is arbitrary – it depends on the threshold, or alpha value, chosen by the researcher. The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis .

When the p -value falls below the chosen alpha value, then we say the result of the test is statistically significant.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Discrete and continuous variables are two types of quantitative variables :

Discrete variables represent counts (e.g. the number of objects in a collection).
Continuous variables represent measurable amounts (e.g. water volume or weight).

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How to Find The Best Statistics Assignment Helper

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Tips to Find Out The Best Statistics Assignment Helper

Table of Contents

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Why SPSS Homework Help Is An Important aspect for Students?

U.S. Embassy in Uganda

Social / search, statistician (data management and statistics team lead) | kampala-2024-030 ra1 | closing: july 12, 2024.

JOB VACANCY ANNOUNCEMENT

U.S. Mission Uganda Announcement Number: Kampala-2024-030 RA1 Position Title: Statistician (Data Management and Statistics Team Lead) Opening Period: 6/27/2024 – 7/12/2024 Series: 1735 Grade: FSN 11/FP-4 Salary: UGX 160,949,701 For More Info: [email protected] Who May Apply: All Interested Applicants/All Sources (Actual FS salary determined by Washington D.C.) Security Clearance Required: Local Security Certification or Public Trust Duration Appointment: Indefinite subject to successful completion of probationary period Marketing Statement: We encourage you to read and understand the Eight (8) Qualities of Overseas Employees before you apply. https://careers.state.gov/wp-content/uploads/2018/02/Eight-Qualities-of-Overseas-Employees.pdf

Summary: The U.S. Mission in Uganda is seeking eligible and qualified applicants for the position of Statistician (Data Management and Statistics Team Lead).

The work schedule for this position is:

Full Time, 40 hours per week

Start date : Candidate must be able to begin working within a reasonable period of time (4 weeks) following receipt of agency authorization and/or clearances/certifications or their candidacy may end.

Supervisory Position: Yes

Duties: The Data Management and Statistician position is in the Data Science and Informatics (DSI) Branch at the Centers for Disease Control and Prevention (CDC) Uganda country office and serves as the lead for the Data Management and Statistics (DMS) Team. The job holder is a senior statistician and key public health technical advisor and provides a broad range of statistical, data management, and analytic expertise and support to physicians, epidemiologists, behavioral scientists, public health specialists, and health service researchers working on USG funded projects and activities.

Download: Detailed Position Description | PD- Data Management Statistics Team Lead – Kampala

Qualifications and Evaluations

EDUCATION : Completion of a Master’s degree in public health, Biostatistics/Statistics, Epidemiology, Data Management, or Informatics is required.

Requirements:

EXPERIENCE: Four years of progressively responsible experience in statistics, data management, programming, monitoring and evaluation, or research in health is required. At least 3 of these years focused on advanced analysis using SPSS, SAS, STATA, R, or other statistical software, is required. Plus 1 year of experience supervising staff is required.

Evaluations: (This may be tested)

LANGUAGE: Fluent speaking/reading/writing English is required.

SKILLS AND ABILITIES: Excellent oral and written communications skills to develop and maintain effective working relationships with national and international health partners are required. Good working knowledge of team management techniques to plan, organize and direct multi-disciplinary project teams and activities and overall administrative requirements, budgeting, and fiscal management in support of cooperative agreements and contracts is required. The ability to lead results-driven project teams and workgroups is required. Advanced user level of word processing, spreadsheets, and databases is required. Numerical skills for data analysis are required. Must have statistical and research skills in HIV/AIDS, TB, or STIs as demonstrated by published, peer-reviewed articles in professional journals, national, or international presentations.

Qualifications: All applicants under consideration will be required to pass medical and security certifications.

EQUAL EMPLOYMENT OPPORTUNITY (EEO): The U.S. Mission provides equal opportunity and fair and equitable treatment in employment to all people without regard to race, color, religion, sex, national origin, age, disability, political affiliation, marital status, or sexual orientation.

Other information:

HIRING PREFERENCE SELECTION PROCESS: Applicants in the following hiring preference categories are extended a hiring preference in the order listed below. Therefore, it is essential that these applicants accurately describe their status on the application. Failure to do so may result in a determination that the applicant is not eligible for a hiring preference.

HIRING PREFERENCE ORDER:

(1) AEFM / USEFM who is a preference-eligible U.S. Veteran* (2) AEFM / USEFM (3) FS on LWOP and CS with reemployment rights **

* IMPORTANT: Applicants who claim status as a preference-eligible U.S. Veteran must submit a copy of their most recent DD-214 (“Certificate of Release or Discharge from Active Duty”), equivalent documentation, or certification. A “certification” is any written document from the armed forces that certifies the service member is expected to be discharged or released from active-duty service in the armed forces under honorable conditions within 120 days after the certification is submitted by the applicant. The certification letter should be on letterhead of the appropriate military branch of the service and contain (1) the military service dates including the expected discharge or release date; and (2) the character of service. Acceptable documentation must be submitted in order for the preference to be given.

** This level of preference applies to all Foreign Service employees on LWOP and CS with re-employment rights back to their agency or bureau.

For more information (i.e., what is an EFM, USEFM, AEFM, MOH, etc?) and for additional employment considerations, please visit the following link: https://careers.state.gov/wp-content/uploads/2018/04/VA-Definitions-and-Additional-Employment-Considerations.pdf

How to Apply:

All candidates must be able to obtain and hold a Local Security Certification or Public Trust clearance.

All applications must be submitted online. Applicants shall be required to register under the careers page for U.S. Embassy Kampala following instructions on the link below:

https://ug.usembassy.gov/embassy/jobs/

For further assistance call 0414-306001
3 . You will receive confirmation of registration and proceed to apply.
On completion of this process, you will receive an auto response via your email.

Required Documents:

In order to qualify based on education, you MUST submit the requested diploma and / or transcripts as verification of educational requirement by the closing date of this announcement. If you fail to provide requested information, or the information you submit is insufficient to verify your eligibility, you will not be considered for this position.

Copy of Orders/Assignment Notification (or equivalent)
Master’s Degree Certificate
DD-214 – Member Copy 4, Letter from Veterans’ Affairs, or other supporting documentation (if applicable)
SF-50 (if applicable)
Residence/Work permit

What to Expect Next: Applicants who are invited to take a language or skills test, or who are selected for an interview will be contacted via email.

For further information: the complete position description listing all of the duties, responsibilities, required qualifications, etc. may be obtained by contacting the Human Resources office.

Thank you for your application and your interest in working at the U.S. Mission in Uganda.

By U.S. Mission Uganda | 27 June, 2024 | Topics: Job Opportunities , Sections & Offices

Development Outreach and Communications (DOC) Specialist -two positions | 72061724R10015 | Closing: July 26, 2024, 4:45PM local time

2023 Report on International Religious Freedom: Uganda

Footer Disclaimer This is the official website of the U.S. Embassy in Uganda. External links to other Internet sites should not be construed as an endorsement of the views or privacy policies contained therein.

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Inferential statistics

Inferential statistics are used by researchers to reach conclusions that extend beyond the immediate data. For example, we might use inferential statistics to know from our sample data what the entire population might think. Most of the widely used inferential statistics are drawn from the general linear models. Some of the examples of these models include Analysis of Variance (ANOVA), regression analysis, t-tests, Analysis of Covariance (ANCOVA), and other multivariate methods like multidimensional scaling and discriminant function analysis. It is essential that any student who wants to carve a niche in social research, then you have to be familiar with the workings of inferential statistics. The use of inferential statistics greatly depends on the research design used. There are two main types of research designs, quasi-experimental and experimental analysis.

Descriptive statistics

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2024 U.S. Olympic Team Trials - Track & Field: All results – complete list

(L-R) Fred Kerley, Noah Lyles and Kenny Bednarek

The U.S. Olympic Team Trials - Track & Field are underway in Eugene, Oregon, at the famed Hayward Field on the campus of the University of Oregon. Set for 21-30 June, the event is split into two four-day chunks, with 25 and 26 June serving as a break from competition.

Reigning 100m world champions Noah Lyles and Sha'Carri Richardson have both secured their spots for Paris in the 100m. Richardson sealed her spot on the 100m team, leading training mates Melissa Jefferson and Twanisha Terry as the top three American women.

On Sunday (23 June), Lyles was the fastest in the men's 100 to make it official for Paris 2024 , taking first ahead of along with Kenny Bednarek and Fred Kerley , who will join him at the Games.

We have a full list of the event schedule - and how you can tune in from home - listed here .

At Trials, the top three in each event make the team if they have the Olympic qualifying standard or a high enough world ranking. Additionally, sprinters will make the team for 4x100m and 4x400m relay pools, with those teams to be decided on the ground in Paris.*

Below, find results from all finals, as well as a link to a full list of results from USA Track & Field.

Sha'Carri Richardson and her training mates set to take sisterhood to Paris
Day 4 wrap: Cole Hocker sets record to claim dramatic 1500m

* As National Olympic Committees have the exclusive authority for the representation of their respective countries at the Olympic Games, athletes' participation at the Paris Games depends on their NOC selecting them to represent their delegation at Paris 2024.

U.S. Olympic Team Trials - Track & Field 2024: All results – complete list

*The U.S. Olympic Team Trials for track & field are being held in Eugene, Oregon, at the historic Hayward Field. USATF provides results for all their races, including heats. You can find them here .

Friday, 27 June

Men's 110m hurdles - final.

1. Grant Holloway 12.86 2. Freddie Crittenden 12.93 3. Veronica Fraley 12.96

Day wrap: Holloway leads historic men's 110m hurdles final
Noah Lyles wows himself in 200m semi win | Gabby Thomas, Sha'Carri Richardson go sub-22 in women's

Thursday, 27 June

Women's discus throw - final.

1. Valerie Allman 70.73m 2. Jayden Ulrich 62.63m 3. Veronica Fraley 62.54m

Women's 3000m Steeplechase -Final

1. Valerie Constien 9:03.22 MR PB 2. Courtney Wayment 9:06.50 PB 3. Marisa Howard 9:07.14 PB

Sha'Carri Richardson, Gabby Thomas claim respective 200m round one heats

Monday, 24 June

Women's high jump - final.

1. Charity Hufnagel 1.94m PB 2. Rachel Glenn 1.94m 3. Vashti Cunningham 1.91m

Men's Long Jump - Final

1. Jeremiah Davis 8.20m SB 2. Malcolm Clemons 8.18m 3. Jarrion Lawson 8.18m SB

Men's 1500m - Final

1. Cole Hocker 3:30.59 MR PB 2. Yared Nuguse 3:30.86 SB 3. Hobbs Kessler 3:31.53 PB

Women's Heptathlon Overall

1. Anna Hal l 6614 points 2. Chari Hawkins 6456 points PB 3. Taliyah Brooks 6408 points

Men's 400m - Final

1. Qunicy Hall 44.17 PB 2. Michael Norman 44.41 3. Chris Bailey 44.42 =PB

Women's 5000m - Final

1. Elle St. Pierre 14:40.34 MR 2. Elise Cranny 14:40.36 SB 3. Karissa Schweizer 14:45.12 SB

Women's 800m - Final

1. Nia Akins 1:57.36 PB 2. Allie Wilson 1:58.32 SB 3. Juliette Whittaker 1:58.45 PB

World bronze medallist Quincy Hall clips Michael Norman in 400m final
Quincy Wilson breaks U-18 World Record Twice on way to 400m final

Sunday, 23 June

Women's hammer throw - final.

1. Annette Echikunwok e 74.68m SB 2. DeAnna Pric e 74.52m 3. Erin Reese 71.21m

Men's Pole Vault - Final

1. Sam Kendricks 5.92m MR 2. Chris Nilsen 5.87m 2. Jacob Wooten 5.87m

Men's Javelin Throw - Final

1. Curtis Thompson 83.04m SB 2. Capers Williamson 79.57m SB 3. Donavon Banks 79.19m

Women's 400m - Final

1. Kendall Ellis 49.46 PB 2. Aaliyah Butler 49.71 PB 3. Alexis Holmes 49.78 PB

Men's 3000m Steeplechase - Final

1. Kenneth Rooks 8:21.92 2. Mathew Wilkinson 8:23:00 3. James Corrigan 8:26.78

Men's 100m - Final

1. Noah Lyles 9.83 =PB 2. Kenny Bednarek 9.87 PB 3. Fred Kerley 9.88

Day 3 Wrap: Kendall Ellis wins women's 400m, Sam Kendricks tops pole vault final s
Noah Lyles claims U.S. Olympic Trials 100m in close-as-can-be final

Saturday, 22 June

Women's 100m - final.

1. Sha'Carri Richardson 10.71 SB 2. Melissa Jefferson 10.80 PB 3. Twanisha Terry 10.89 SB

Men's Shot Put - Final

1. Ryan Crouser 22.84m SB 2. Joe Kovacs 22.43m 3. Payton Otterdahl 22.26m

Women's Triple Jump - Final

1. Jasmine Moore 14.26m 2. Keturah Orji 14.22m 3. Tori Franklin 13.72m

Men's Decathlon

1. Heath Baldwin 8,625 points 2. Zach Ziemek 8,516 3. Harrison Williams 8,384

Day 2 wrap: Crouser beats Kovacs, Moore wins women's triple
Sha'Carri Richardson leads clean sweep for training camp in 100m
Noah Lyles, Christian Coleman go sub-10 seconds in men's 100m semi-finals

Friday, 21 June

Men’s 10,000m final.

1. Grant Fisher 27:49.47 2. Woody Kincaid 27:50.74 3. Nicolas Young 27:52.40

Day 1 wrap: Mu, Crouser advance | Sha'Carri Richarson cruises in 100m

U.S. Olympic Team Trials - Track & Field 2024: How to watch

For American fans, the action from Hayward Field will be broadcast across NBC's family of networks, including NBC, USA and the streaming platform Peacock.

You can find a full broadcast schedule here . Note times are listed in Eastern Daylight (EDT), which is three hours ahead of Eugene.

USATF provides further broadcast info here .

Mets Star Slugger Linked To Surprising Team In Possible Blockbuster Deal

Patrick mcavoy | jun 27, 2024.

Jun 14, 2024; New York City, New York, USA; New York Mets first baseman Pete Alonso (20) at bat against the San Diego Padres during the third inning at Citi Field. Mandatory Credit: Gregory Fisher-USA TODAY Sports

New York Mets
Boston Red Sox

The New York Mets should be considering all ways to improve the roster right now rather than a possible firesale.

New York struggled out of the gate this season but has turned things around of late. At one point the Mets had one of the worst records in the National League but now they are back at .500 and just swept the American League-leading New York Yankees in a short two-game series.

The Mets look to be fully back and are 1 1/2 games out of a National League Wild Card spot at this point. New York is loaded with talent and should get even better soon as Kodai Senga returns to the mound.

Things are looking up in New York and with the trade deadline approaching, it should be looking for ways to improve rather than sell. While this is the case, Mets superstar Pete Alonso surely will be mentioned in trade rumors until the deadline passes no matter how many games New York wins.

One team that surprisingly was mentioned as an option for Alonso was the Boston Red Sox by ESPN's Jeff Passan.

"If Boston continues this run and goes into July in full command of a playoff spot, the Red Sox absolutely should be placing a call to the Mets to gauge the possibility of getting Alonso," Passan said. "Even though first baseman Triston Casas is expected back in early July, the Red Sox have gotten a sub-.300 weighted on-base average from the DH position. Which, regardless of the size of Masataka Yoshida's contract, is worth addressing via upgrades like the one Alonso would provide."

Alonso would be a major pickup for the Red Sox -- and any other team -- but it's looking more and more likely that he will stick around with New York after the deadline.

More MLB: Mets Superstar Is 'Best Match' For A.L Contender Looking For Slugger

PATRICK MCAVOY

Patrick McAvoy's experiences include local and national sports coverage at the New England Sports Network with a focus on basketball and baseball.

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16-year-old Quincy Wilson to run men's 400m final tonight at U.S. Olympic trials

EUGENE, Oregon — A Hayward Field fan favorite has blossomed in the form of 16-year-old sensation Quincy Wilson .

Running against competitors nearly twice his age at the U.S. Olympic track and field trials , Wilson’s captured the support and attention from spectators at the stadium and watching from home as he raced into Monday night’s 400 meters final. The top three finishers in the final will make Team USA and go to the 2024 Paris Olympics.

Wilson broke the under-18 world record when he ran a 44.66 in the first round of the 400 meters on Friday. Then he broke the record again Sunday by running a 44.59 to advance to Monday’s final, which is scheduled for 9:59 p.m. ET on NBC and Peacock.

Wilson will be running out of Lane 2.

"It means a lot to me because it means my hard work has been paying off," Wilson said after qualifying for the final. "I'm just excited for myself."

16-year-old Quincy Wilson with a tremendous effort in the 400m semi! He has officially QUALIFIED for the final. 👏 #TrackFieldTrials24 pic.twitter.com/nL4Nj8m5BT — NBC Sports (@NBCSports) June 24, 2024

Wilson, who attends Bullis School in Maryland, is entering the 400 final with the fourth fastest qualifying time. He is behind Quincy Hall (44.42), Bryce Deadmon (44.44) and Vernon Norwood (44.50).

Not surprisingly, Wilson has earned the admiration of his competitors.

"It’s spectacular. A 16-year-old coming out here, competing like a true competitor, not letting the moment get too big but living in the moment," said Olympic gold medalist Michael Norman, who also advanced to the final. "It’s great to see young talents like him elevate and push us to run a little faster, and take us out of our comfort zone. I think he has a bright future."

Wilson said his race plan went out the window during the first rounds of the trials, and his ability to qualify was all heart.

“I’m just running for my life out there," Wilson said. "I have a lot of things I can do to improve myself. I’m in the world’s biggest final coming up. At 16 years old, I’m ecstatic right now. ...It’s one of the happiest days of my life."

Olympic hopeful doesn't yet have driver's license

In some ways, Wilson is like his fellow competitors: He’s repped by a major apparel company after signing an NIL deal with New Balance last September.

In other ways, he’s extremely different. He gets straight As, for example … because he’s still juggling full-time school with training. Also of note: He does not yet have his driver’s license, and isn't sure when he'll get it. He doesn't have time to study or practice driving, what with trying to make the Olympic team and all.

“I don’t know when I’m gonna get my driver’s license at this point.” – 16-year-old Quincy Wilson after his 44.59 to advance to the U.S. Olympic Trials 400m final 😂 pic.twitter.com/vaNU7ZGsrI — Chris Chavez (@ChrisChavez) June 24, 2024

Wilson is coached at Bullis by Joe Lee, a former youth pastor who’s been at the Potomac, Maryland, private school for 11 years. Before Wilson, Lee coached Wilson’s cousin, Shaniya Hall , a sprinter who starred at college track powerhouse Oregon the last four years, where she helped the Ducks to two Pac-12 relay championships.

After his blistering opening round Friday, Wilson was asked if he held back at all considering it was just a heat. He smiled.

“It’s a different game," he said. "I’m not in high school anymore, I’m running with the big dogs now, so I gotta give it my all.”

He brushed off concerns about if he’d be able to recover fully.

“I know my training and my coaching,” he said. “They have me in the right position.”

That goes for the mental side, too. Asked what his nerves were on a scale of 1-10 after his first race, he again gave off his megawatt smile before declaring, “Probably like a 2.”

“I’m just racing against bigger people,” Wilson said, a nod to both his competitors' physical stature and brand names. “But to me everybody puts their spikes on the same way I do, and I train just as hard as they do.”

He made it clear he’s not just here to enjoy the experience, either. He wants to win.

Could Wilson make the Olympic team?

Norman, who ran in his first trials at 18, seemed somewhat skeptical that the 16-year-old would claim a spot on the Olympic team in the 400.

Asked if he viewed Wilson as a contender, Norman was blunt.

“It’s tough,” he said. “There’s people out there fighting for money right now. He made the final. It’s very hard to say. This is probably his first time running three rounds. I remember (my first trials), running three rounds in the 200, I was cooked.”

But he wasn’t ready to write Wilson off entirely.

“Kids are different now, so it’s definitely possible,” Norman said. “He could definitely sneak into a relay spot for sure.”

High school track phenoms are rare at the Olympic Track & Field Trials, but not entirely unheard of: In 2016, 16-year-old Sydney McLaughlin-Levrone finished third in the 400 hurdles, and qualified for the Rio Games. She did not make the final in Rio. (McLaughlin-Levrone is favored to win the 400 hurdles later this week). McLaughlin-Levrone was the youngest athlete to make Team USA’s Olympic roster in 36 years.

Erriyon Knighton, one of the world’s top 200 runners, made the Tokyo roster as a high school junior, finishing fourth in Japan. Knighton won bronze at the 2022 World Championships. He’s entered in the 200 later this week.

Should Wilson qualify for Paris, he would be the youngest-ever male member of the American Olympic track team.

Follow USA TODAY Sports' Tyler Dragon on X @TheTylerDragon .

Email Lindsay Schnell at [email protected] and follow her on social media @Lindsay_Schnell

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Jun 14, 2024; New York City, New York, USA; New York Mets first baseman Pete Alonso (20) at bat against the San Diego Padres during the third inning at Citi Field.
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Quincy Wilson, 16, in men's 400 final tonight at Olympic track trials
The top three finishers in the final will make Team USA and go to the 2024 Paris Olympics. Wilson broke the under-18 world record when he ran a 44.66 in the first round of the 400 meters on Friday ...
Alex Morgan not selected to USWNT for Olympics
The roster is the first major tournament assignment for coach Emma Hayes. ... U.S. women's national team forward Alex Morgan is not on the team headed to Paris for the Olympics this summer ...

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