Products related to Conditional:
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Conditional Love
What surprises might life have in store for you?A takeaway, TV and tea with two sugars is about as exciting as it gets for thirty-something Sophie Stone.Sophie's life is safe and predictable, which is just the way she likes it, thank you very much. But when a mysterious benefactor leaves her an inheritance, Sophie has to accept that change is afoot.There is one big catch: in order to inherit, Sophie must agree to meet the father she has never seen. Saying 'yes' means the chance to build her own dream home, but she'll also have to face the past and hear some uncomfortable truths... With interference from an evil boss, warring parents, an unreliable boyfriend and an architect who puts his foot in it every time he opens his mouth, will Sophie be able to build a future on her own terms - and maybe even find love along the way?A totally charming, modern love story for fans of Katie Fforde, Carole Matthews and Trisha Ashley.Praise for Cathy Bramley:'Delightfully warm and with plenty of twists and turns' Trisha Ashley'A witty, laugh-out-loud romantic comedy . . . I highly recommend it!' Miranda Dickinson
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Notes On a Conditional Form
The 1975's follow up to BRIT Award Album of the Year winner 'A Brief Inquiry Into Online Relationships'. The album features the lead single 'People' and the Greta Thunberg call-to-arms album opener, 'The 1975'. The band worked to use as little plastic as possible on the manufacture of the product; as such the albums will not be shrink-wrapped.
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Notes On A Conditional Form CD
The 1975s follow up to BRIT Award Album of the Year winner A Brief Inquiry Into Online Relationships. The album features the lead single People, Greta Thunberg calltoarms album opener, The 1975 alongside singles lsquoMe amp You Together Songrsquo amp lsquoFrail State Of Mind.rsquo The band worked to use as little plastic as possible on the manufacture of the product the softpack CD is housed in a recyclable sleeve amp artwork is printed on 100 recycled Kraftpak card.
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Understanding Regression Analysis : A Conditional Distribution Approach
Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model.It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong.Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations.Hence, the emphasis is to model Nature’s processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways. Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed "just-in-time" within chapters Simple mathematical explanations ("baby proofs") of key concepts Clear explanations and applications of statistical significance (p-values), incorporating the American Statistical Association guidelines Use of "data-generating process" terminology rather than "population" Random-X framework is assumed throughout (the fixed-X case is presented as a special case of the random-X case) Clear explanations of probabilistic modelling, including likelihood-based methods Use of simulations throughout to explain concepts and to perform data analysesThis book has a strong orientation towards science in general, as well as chapter-review and self-study questions, so it can be used as a textbook for research-oriented students in the social, biological and medical, and physical and engineering sciences.As well, its mathematical emphasis makes it ideal for a text in mathematics and statistics courses.With its numerous worked examples, it is also ideally suited to be a reference book for all scientists.
Price: 39.99 £ | Shipping*: 0.00 £
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What is the difference between conditional and conditional?
The question seems to have a typo. It appears to be asking for the difference between "conditional" and "condition." A "conditional" typically refers to something that is dependent on a certain condition or situation, such as an "if-then" statement in programming or a requirement that must be met. On the other hand, a "condition" is the specific circumstance or state that must be satisfied for the conditional to be true or for something to happen. In essence, a condition sets the criteria for a conditional to be applicable or valid.
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What are signal words for Conditional 1 and Conditional 2?
Signal words for Conditional 1 include "if," "when," "unless," and "provided that." These words indicate a possible future condition and its likely result. Signal words for Conditional 2 include "would," "could," "might," and "should." These words suggest a hypothetical or unlikely condition and its potential outcome.
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Is the conditional 1 the same as the conditional present?
Yes, the conditional 1 is the same as the conditional present. In English grammar, the conditional 1 is used to talk about hypothetical situations and their possible outcomes in the present or future. It is formed by using the auxiliary verb "would" followed by the base form of the main verb. This is the same structure used for the conditional present tense, making them essentially the same tense with different names.
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What is conditional formatting?
Conditional formatting is a feature in spreadsheet applications that allows users to apply formatting rules to cells based on specific conditions. This feature helps users to visually highlight important information, trends, or outliers in their data. For example, users can set up rules to automatically change the color of cells that meet certain criteria, such as values above a certain threshold. Conditional formatting helps to make data analysis easier and more efficient by drawing attention to key insights.
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Conditional Design : An introduction to elemental architecture
Conditional design is the sequel to Operative Design.This book will further explore the operative in a more detailed, intentional, and perhaps functional manner.Spatially, the conditional is the result of the operative.It is not a blind result however. Both terms work together to satisfy a formal manipulation through a set of opportunities for elements such as connections and apertures.
Price: 12.99 £ | Shipping*: 3.99 £ -
The International Monetary Fund (IMF) : Politics of Conditional Lending
This is a clear and concise introduction to the International Monetary Fund (IMF) and an overview of its debates and controversies.Where did the IMF come from? What does it do? Why do so many governments participate in its programs and what are their effects?How can we best reform this key global institution? These are some of the key questions addressed. In our age of thinking global, the IMF is a crucial institution and central to understanding international relations and current affairs.Founded in the aftermath of the Second World War, its basic purposes were to facilitate world trade and promote national prosperity.The founders hoped that never again would the world experience the trade policies that led up to the Great Depression.This book outlines and questions these targets and assesses actual achievements.It also details how despite originally focusing on Europe, the Fund has gradually shifted to involvement with poorer developing countries, but to what ends and with how much success?This study both poses and tackles the tough questions facing our global community today.
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Conditional Belonging : The Racialization of Iranians in the Wake of Anti-Muslim Politics
A compelling account of how race and politics have affected Iranian immigrants in the United Statesand GermanyIranians have a complex and contradictory relationship with race.Though categorized as “white” by the US census, many Iranian Americans remain marginalized, and experience racial and political stigma daily.On the other hand, Iranian Germans who have been in Germany for decades, and are typically regarded as 'good foreigners,' continue to experience marginality and discrimination illustrating the limitations of integration and citizenship.Conditional Belonging explores these apparent contradictions through a comparative analysis of the Iranian diasporic experience in the United States and Germany, focusing particularly on the different processes of racialization of the immigrants. Drawing from eighty-eight interviews with first- and second-generation Iranians living in California and Hamburg, Sahar Sadeghi illuminates how international events, global political policy, and national social climates influence the extent to which Iranians define themselves as members of their adopted nations.All these factors lead to radically different experiences of belonging, or more specifically “conditional belonging,” for Iranians living in Western nations—while those in America might have situational access to whiteness, this is not always available to Iranians in Germany.The combination of these experiences results in perceptions, narrations, and experiences of what the author calls “being but not belonging.” Conditional Belonging is an important and timely book that broadens our understanding of how unpredictable and fluid a sense of belonging to a country can be.
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Sparse Graphical Modeling for High Dimensional Data : A Paradigm of Conditional Independence Tests
This book provides a general framework for learning sparse graphical models with conditional independence tests.It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference.The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture.This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines. Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selectionEffective methods of high-dimensional inference
Price: 91.99 £ | Shipping*: 0.00 £
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Conditional, subjunctive or indicative?
The choice between conditional, subjunctive, or indicative mood depends on the context of the sentence. The conditional mood is used to express a hypothetical situation or a condition that is dependent on something else happening. The subjunctive mood is used to express wishes, recommendations, or possibilities. The indicative mood is used to state facts or ask questions.
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Is the conditional probability correct?
To determine if the conditional probability is correct, we need to ensure that it satisfies the definition of conditional probability. This means that the conditional probability P(A|B) should be the probability of event A occurring given that event B has already occurred, and it should be calculated as P(A and B) divided by P(B). We should also check that the values used in the calculation are accurate and that the events A and B are properly defined. If the conditional probability satisfies these conditions, then it can be considered correct.
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What is a conditional probability?
A conditional probability is the probability of an event occurring given that another event has already occurred. It is denoted as P(A|B), where A is the event we are interested in and B is the event that has already occurred. The conditional probability is calculated by dividing the probability of both events occurring by the probability of the given event occurring. Conditional probability is used to update our beliefs about the likelihood of an event based on new information or prior knowledge.
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How is the conditional formed?
The conditional in English is formed by using the modal verb "would" followed by the base form of the main verb. For example, "I would go" or "She would study." The conditional is used to talk about hypothetical situations, future possibilities, or polite requests. It is important to note that the conditional is often used with an "if" clause to show the condition that must be met for the action to take place.
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