How accurately can we use statistics to bet for football (soccer)?
Unless the teams are completely mismatched (one is so much better than the other it isn't funny, for example), this is hard for any particular game. Why? Because even in the complete mismatch game, there may be things that allow the "worse" team to win. Since the game is governed by human choices, nature, the referees (they could miss something or see something from the wrong angle, etc.), and a whole host of other things, we can have an idea of past behaviour through statistics of past matches and still be totally off.Even if we knew everything about the match before it began: confidence, wellness, etc. of each player (if we could somehow measure these things), and had enough data to make some sort of model well-defined, the "randomness" of the other elements could still throw us off, although we'd be much better off than just using the statistics about past matches. (I put the word randomness in quotes because such things may be completely deterministic, but we would be unable to predict them due to the fact that we cannot predict exactly how the game will happen.)Thus, especially just using statistics from past matches, we can only win if we bet consistently on many matches. The problem with this is that the bookies set odds so their long-run gain (that is, over multiple matches) is minimized. I'm sure they also adjust for things that come up "randomly" as well, such as a star player tearing a muscle before the bet is made.TL;DR: if you really want to make money by betting on a football/soccer match (or anything, for that matter), be a bookie and make sure you have the relevant information. In the long run, you'll make money, because you're setting the odds.
How do I drop data values using "not equal to" in Stata?
the specifics will depend on how exactly your data are coded and arranged, but the basic syntax for dropping cases not equal to some value is drop if X != Y where X is the name of the variable and Y is the value you want to keep, assuming all 40 observations have the same value on some sorting variable. If they're a continuous sequence of cases you can specify them using the / operator; for example, to drop all but the first 40 cases you would use keep in 1/40 if you provide more detail on exactly how you need to identify the cases you want to keep, I can provide a more specific answer.
How can one interpret a negative t-value in a student test (p-value is very low)?
When doing a t-test, the alternative hypothesis can either be one-sided (for example, [math]\mu < 10[/math] or [math]\mu>8[/math]) or two-sided (for example, [math]\mu \ne 0[/math]).If you are doing a two-sided test, then finding that the sample mean is larger or smaller than the hypothesized mean should be taken as evidence against the null hypothesis and in favor of the alternative. (Then the p-value is computed to quantify the strength of the evidence.) But what matters in the context of your question is that the sample mean can be smaller OR larger than the hypothesized mean and still be interpreted as evidence against the null hypothesis. If it is smaller than the hypothesized value, then the t-statistic will be negative. If it is larger, the t-statistic will be positive. But it really makes no difference which sign it has because both are signs are interpreted the same way - as evidence against the null hypothesis.However, if you are doing a one-sided test, this isn't true anymore. For example, suppose the alternative hypothesis is [math]\mu>0[/math]. In this case, if the sample mean is negative, that is NOT evidence against the null in favor of the alternative. (You wouldn't reject the null that the true mean is zero in favor of the alternative that it is positive if the data suggests that the mean is negative.) In the case of a one-sided alternative, the sign of the t-statistic matters A LOT. A negative sign implies that the sample mean is less than the hypothesized mean. This would be evidence against the null hypothesis IF (and only if) the alternative was that the true mean is LESS than the hypothesized value. A positive sign implies that the sample mean is larger than the hypothesized mean. This would be evidence against the null hypothesis IF (and only if) the alternative was that the true mean is GREATER than the hypothesized value.Depending on the software you are using, the p-value that is calculated may or may not take into account the alternative hypothesis. So be sure that you think about what your data tells you before you decide that the p-value really IS small. Because if the test is one-sided and the data points in the opposite direction of the alternative hypothesis, then the p-value CANNOT be small. It must be larger than 50% (even if your software tells you something else).
If we cross two pea plants each heterozygous for yellow seed color genes, the expected ratio of yellow: green among the offspring will be___?
There are two possible scenarios for this case, depending on allele dominance.1) If yellow is dominant:Concerning the phenotype, the ratio would be 3:1 for yellow. Again, assuming yellow is dominant and green is recessive, then both YY and Yy would be yellow and yy would be green. 2) If green is dominant:This is just an example picture, but basically, assuming green is dominant and yellow is recessive, the roles would reverse from above: There would be a 3:1 green-yellow phenotypic ratio. GG and Gg would be green, and gg would be yellow.
How long is someone conscious after being beheaded?
The brain loses enough blood to lose consciousness in just a few seconds after decapitation. This sudden draining of blood from the brain, though much faster and dramatic, is similar to someone with low blood pressure who rises suddenly from a lying or sitting position, stands up and faints. The blood pressure is not high enough to hold blood in the brain with the sudden change in gravity. This is called vaso-vagal syncope. In this condition the subject is unconscious but has electrical brain activity. Shock experienced instantaneously upon impact of the blade lasts a couple of seconds. Between these two events there may be a couple of seconds of consciousness, not long enough to verbalize anything.During this time the subject would experience intense vertigo as the head drops and rolls. This can be confirmed by observations of nystagmus and eye flutter from the severed head that can last a few seconds. But these scientific observations don't tell us what the actual experience of the subject is. During vaso-vagal syncope, for instance, subjects can report vivid dreams. Many accounts of NDEs of subjects surviving very traumatic accidents involving near death talk about the subject "leaving the body," and watching the accident from a point above or adjacent to themselves.
Why is KYC important for banks?
KYC means Know your customer guidelinesThis is antimoney laundering deviceMoney laundering means bringing illegal money into the banking channel by opening accounts in benami namesIn order to avoid such accounts, KYC guidelines have been prescribed and according to this banks have to obtain the following documents from the customer at the time of opening accounts with the banks:Copy of one document for address proof - AADHAAR card/Passport/VOTER id card/Driving licenceCopy of one document for identity proof - AADHAAR card/Passport/VOTER id card/Driving licence; PAN card
What should I study or learn if I want to be a data analyst for a software company like Quora, Zynga, Airbnb, etc.?
When I was on the analytics team at Airbnb (2011-2012), we looked for the following:1. Research design/methodology - Ability to set up experiments properly, with careful attention to control groups and confounding variables- Knowledge of basic statistics techniques and concepts (regressions, ttests, significance, etc)- Ability to delve into open ended problems and find trends in huge sets of data- Understanding of all the caveats and complications of research without getting so bogged down in them that it takes months to get results2. Tools to manipulate data (programming ability, sql, statistics tools, etc)- Python, Ruby, or another similar programming language- R, STATA, SAS, or some other statistical programming language for analyzing data- SQL or similar querying/manipulation language, understanding of fairly complex joins, nested queries, etc- Excel can be useful but details can probably be learned as needed (personally, I don't think I've ever used a pivot table in my job because I use other tools to combine data)- Hive, Hadoop, etc. are really useful, albeit not essential for getting hired (but would mean a lot more than detailed knowledge of Excel, which I would assume any smart person could pick up as needed)3. Ability to interpret and summarize results broadly for technical and non-technical audiences4. Any other special skills, such as data visualization, machine learning, advanced statistical techniques, etc.At ClassDojo, we are at an earlier stage, and the data is in JSON instead of SQL, so there is even more need for everyone to program. I spend 90% of my time getting data in place in Python before I can do any actual analysis or visualization (in Python or R).Edit: For a much more complete answer, you can view this curriculum that I wrote using free online materials: Data Analysis Learning Path by Claudia Gold | SlideRule