I don't understand statistics Watch

Anna Sun
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#1
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People always told me that statistics is easy, or at least easier than mechanics and I've just been trying to revise it for the first time properly since I finished learning it and I have no idea what I am doing. My exam is in a month and I just can't get my head around any of it. I also feel like I started revising properly way too late and I'll probably fail. Anyone have any tips or techniques I can use so that I can do it easier? and how to manage my time better because I'm a mess.
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WEASEL espinoza
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I'm exactly the same. I find s2 the hardest compared to the core maths modules which I'm naturally better at.
For me its the paragraphs they give you for each question, makes the questions so confusing

I can only advise wacking them past papers
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Bradley00
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Hey,

I'm currently studying the new spec for A Level Maths (And by the way you talk about it being easier than Mechanics I am assuming you are also) and having Examiners throughout Year 7 to Year 12, their top tip has always been practice, practice, practice...

Maths has always been one of those subjects where no matter how confident you think you are about a specific topic, there is always room for improvement, because one question you could be like 'OMG, this is SOOOOOOOO EASY', and then next time round you think 'Jheeeez.... What is wrong with me... I cant answer this.... Its too hard...'

Leading onto managing your time, there is actually a thread on TSR that provides set hours for revision where you post what you want to accomplish beforehand, and then you post what you managed to do. The link will be at the end of my Essay (Apologies for the length of my post).

If you want to talk about anything to do with Stats, Time Management, or just chat in general, feel free to PM me and ask questions. Good Luck with your adventure in Maths and your other subjects

https://www.thestudentroom.co.uk/sho....php?t=5213722
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brainmaster
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(Original post by Anna Sun)
People always told me that statistics is easy, or at least easier than mechanics and I've just been trying to revise it for the first time properly since I finished learning it and I have no idea what I am doing. My exam is in a month and I just can't get my head around any of it. I also feel like I started revising properly way too late and I'll probably fail. Anyone have any tips or techniques I can use so that I can do it easier? and how to manage my time better because I'm a mess.
I'm exactly here.....I can do Pure math and other math so well but this one simple statistics get down all the time.....most of the time it doesnt just make sense to me lol
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bleepyboop
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Start with the basics, and get them down. What is the aim of statistics?

Well, suppose you have a set of data, say for example, you measured a bunch of people's heights, or you rolled a dice a number of times and recorded the outcome. Given the set of raw data, could you describe it to me without giving me the set of data? Furthermore, if I measured a random person's height, would you be able to tell me if this person's height was a "normal" height? Would you be able to tell me if the dice was not fair?

These fundamental questions are what statistics is all about - answering questions about sets of data. Forget the purist way of looking at statistics, because unless it comes naturally to you, it unnecessarily complicates the picture.

Here are a few basic things to think about to get you started.

  1. The most basic descriptive statistics you can give, are the "average" i.e., the height, or dice number I'd expect to find if I took a random measurement
  2. The next thing you could tell me is, how far away I'd expect a random measurement to be away from this average. In (other words, I'd want to know about my spread of data away from the average (this can be given as a standard deviation, or an interquartile range, for example)
  3. Developing on this idea then, you could tell me about the "distribution" of the data, i.e., whether it follows a standard model of data, e.g. normal distribution, Poisson distribution, binomial distribution, uniform distribution. All of these distributions have different properties, and are used in different situations to describe different sets of data.
  4. If you know the distribution of the data, you could tell me what was the probability/likelihood of getting a certain value like rolling a 2 (or range of values like measuring a height between 1.5m-1.75m).
  5. Introducing the idea of confidence intervals. This would typically be a range of values that I'd expect 95% of all my measurements to be within, 2.5% to be above my range, and 2.5% to be below my range (two-tailed).

Hopefully this helps, if you have more specific questions, just ask.
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ihatePE
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loooool same, S1 makes no sense to me, I mean who the hell thought of probability AND integration together? I don't understand what the decimals and fractions even mean in context of the question most of the time but if you memorise the methods and realise each paper follows the same standard format then you'll get at least 40/75
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RDKGames
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(Original post by ihatePE)
loooool same, S1 makes no sense to me, I mean who the hell thought of probability AND integration together?
Well, it comes down to the basic principle that for the probability of a continuous r.v. X to happen between two bounds a and b in context, you need to determine the area under its distribution graph between a and b. This is why we there's integration involved, since we usually know the distribution function!

Integration is just summation, which is why for discrete r.v's the same principle applies but we use \displaystyle \sum rather than \displaystyle \int.
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ihatePE
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(Original post by RDKGames)
Well, it comes down to the basic principle that for the probability of a continuous r.v. X to happen between two bounds a and b in context, you need to determine the area under its distribution graph between a and b. This is why we there's integration involved, since we usually know the distribution function!

Integration is just summation, which is why for discrete r.v's the same principle applies but we use \displaystyle \sum rather than \displaystyle \int.
makes some kind of sense now
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