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1 Suppose that the average song length in America is 4 minutes with a standard deviation of 1.25 minutes. It is known that song length is not normally distributed. Find the probability that a single randomly selected song from the population will be longer than 4.25 minutes. Round to the nearest thousandth. Answer A. 0.579 B. 0.079 C. 0.421 D. This probability cannot be determined because we do not know the distribution of the population. 2 An outcome of an experiment or study that is large enough to have a real effect on people’s health or lifestyle is said to have clinical significance. Answer True False 3 Are average SAT scores higher in schools where a smaller percentage of graduating students take the test? To answer this question 10 schools are sampled and the average SAT and percentage of students taking the test were recorded. 2002 SAT results of regional high schools were sampled and the data is given below. Use that data to test if there is a relation between the proportion of seniors that take the test and the average SAT scores. At 95% confidence level. 2002 SAT results of a sample of Western North Carolina High Schools. Mean SAT scores 1106 1040 1013 1066 1061 1075 1058 997 1014 965 Percent tested 61 59 44 54 72 74 80 32 49 What is R2 for the equation? A. R2=0.793 B.B. R2= 0.429 C. C. R2=0.326 D. D. R2=0.357 4. Suppose the Acme Drug Company develops a new drug, designed to prevent colds. The company states that the drug is equally effective for men and women. To test this claim, they choose a simple random sample of 100 women and 200 men from a population of 100,000 volunteers. At the end of the study, 38% of the women caught a cold; and 51% of the men caught a cold. State the null and alternative hypothesis: Answer A. H0: p1 = p2 : HA: p1 = p2 : B. H0: p1 < p2 HA: p1< p2 : C. H0: p1 > p2 HA: p1> p2 : D. H0: p1 ≠ p2 HA: p1= p2 : 5 An insurance company is reviewing its current policy rates. When originally setting the rates they believed that the average claim amount was $1,800. They are concerned that the true mean is actually higher than this, because they could potentially lose a lot of money. They randomly select 40 claims, and calculate a sample mean of $1,950. Assuming that the standard deviation of claims is $500, and set α = 0.05, test to see if the insurance company should be concerned. What do we interpret from the problem? Answer A. P value < α so we reject Ho B. P value > α so we do not reject Ho C. P value < α so we do not reject Ho D. P value < α so we reject Ho 6 1. To achieve a significance level of α, if the p-value is less than (or equal to) α, then________________ Answer A. accept the null hypothesis B. do not reject the null hypothesis C. reject the null hypothesis D. accept the alternative hypothesis 7 1. Neuroscience researchers examined the impact of environment on rat development. Rats were.
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Hypothesis Testing Definitions: A statistical hypothesis is a guess about a population parameter. The guess may or not be true. The null hypothesis, written H0, is a statistical hypothesis that states that there is no difference between a parameter and a specific value, or that there is no difference between two parameters. The alternative hypothesis, written H1 or HA, is a statistical hypothesis that specifies a specific difference between a parameter and a specific value, or that there is a difference between two parameters. Example 1: A medical researcher is interested in finding out whether a new medication will have undesirable side effects. She is particularly concerned with the pulse rate of patients who take the medication. The research question is, will the pulse rate increase, decrease, or remain the same after a patient takes the medication? Since the researcher knows that the mean pulse rate for the population under study is 82 beats per minute, the hypotheses for this study are: H0: µ = 82 HA: µ ≠ 82 The null hypothesis specifies that the mean will remain unchanged and the alternative hypothesis states that it will be different. This test is called a two-tailed test since the possible side effects could be to raise or lower the pulse rate. Notice that this is a non directional hypothesis. The rejection region lies in both tails. We divide the alpha in two and place half in each tail. Example 2: An entrepreneur invents an additive to increase the life of an automobile battery. If the mean lifetime of the automobile battery is 36 months, then his hypotheses are: H0: µ ≤ 36 HA: µ > 36 Here, the entrepreneur is only interested in increasing the lifetime of the batteries, so his alternative hypothesis is that the mean is greater than 36 months. The null hypothesis is that the mean is less than or equal to 36 months. This test is one-tailed since the interest is only in an increased lifetime. Notice that the direction of the inequality in the alternate hypothesis points to the right, same as the area of the curve that forms the rejection region. Example 3: A landlord who wants to lower heating bills in a large apartment complex is considering using a new type of insulation. If the current average of the monthly heating bills is $78, his hypotheses about heating costs with the new insulation are: H0: µ ≥ 78 HA: µ < 78 This test is also a one-tailed test since the landlord is interested only in lowering heating costs. Notice that the direction of the inequality in the alternate hypothesis points to the left, same as the area of the curve that forms the rejection region. Study Design: After stating the hypotheses, the researcher’s next step is to design the study. In designing the study, the researcher selects an appropriate statistical test, chooses a level of significance, and formulates a plan for conducting the study..
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A hypothesis is the translation of the information that we are keen on. Utilizing Hypothesis Testing, we attempt to decipher or reach inferences about the populace utilizing test information. A Hypothesis assesses two totally unrelated articulations about a populace to figure out which explanation is best upheld by the example information.
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Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
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Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
JAM, the future of Polkadot.
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Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager
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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.
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Introduction to Multilingual Retrieval Augmented Generation (RAG)
Zilliz
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving. A report by Poten & Partners as part of the Hydrogen Asia 2024 Summit in Singapore. Copyright Poten & Partners 2024.
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Edi Saputra
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Jeffrey Haguewood
Terragrunt, Terraspace, Terramate, terra... whatever. What is wrong with Terraform so people keep on creating wrappers and solutions around it? How OpenTofu will affect this dynamic? In this presentation, we will look into the fundamental driving forces behind a zoo of wrappers. Moreover, we are going to put together a wrapper ourselves so you can make an educated decision if you need one.
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Chapter 8 – Hypothesis Testing
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