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Data analysis, Can you show what you believe? In a real empirical test?…

- - - - the survey data
        
        They take a random, representative sample
        of consumers and ask them a bunch of pertinent questions about how they feel
        
        What people say in surveys does not always fit with how they behave in reality, but it never hurts to ask people how they feel.
        
        Look at
        
        Do you notice any patterns?
        
        Is there anything that might explain to you why sales are down?
      - Observational study: A study where the people being described decide on their own which groups they belong to.
        
        Comparisons are key for observational data
        
        The more comparative the analysis is, the better.
        
        taking an inventory of observational data is often the first step to getting better data through experiments.
        
        observational studies aren’t that powerful when it comes to drawing causal conclusions.
        => need other tools to get those sorts of conclusions.
        
        Observational studies are full of confounders
        
        A confounder is a difference among the people in your study other than the factor you’re trying to compare that ends up making your results less sensible.
        
        Your job as the analyst is always to think about how confounding might be affecting your results
        
        it’s always really important that your conclusions make sense
        
        Manage confounders by breaking the data into chunks
        
        These smaller chunks are more homogenous
        
        they don’t have the internal variation that might skew your results and give you the wrong ideas.
        
        You need an experiment to say
        which strategy will work best
        
        If you want to draw conclusions about things
        that overlap with your data but aren’t completely described in the data, you need theory to make the connection.
        
        In order to get more clarity about which strategy is better, you’re going to need to run an experiment.
        
        Control group A group of treatment subjects that represent the status quo, not receiving any new treatment.
        
        No control group means no comparison.
        No comparison means no idea what happened.
        
        Confounders also plague experiments
        
        2 more items...
        
        Assign the microregions randomly to control and experimental groups
        
        Ex:
        Control: maintain the status quo for a month
        Experimental group #1: drop prices for a month
        Experimental group #2: persuade customers that Starbuzz is a value for a month
        
        you don't know which cause
        Ex: when you’re starting to suspect that causes are going in one direction (like value perception decline causing sales decline), flip the theory around and see how it looks (like sales decline causes value perception decline).
- - - - Standard deviation can be used here to measure disagreement. The larger the standard deviation of subjective probabilities from the mean, the more disagreement there will be among analysts as to the likelihood that each hypothesis is true.
      - Use the STDEV formula in Excel to calculate the standard deviation.
        
        =STDEV(data range)
- - - - Basic process
        
        Define problems
        
        You need to get as much information as you can from business to define your problem.
        
        How can u use data to define problems and answer the questions?
        
        questions to ask
        
        Always ask “how much.” Make your goals and beliefs quantitative.
        
        competitors
        
        if sth curious -> ask
        
        customer target
        
        Understand business
        
        Disassemble
        breaking problems and data into smaller pieces
        
        You need to divide your problem into manageable, solvable chunks
        
        If the data you receive is a summary, you’ll want to know which elements are most important to you. -> Break down your summary data by searching for interesting comparisons.
        If your data comes in a raw form, you’ll want to summarize the elements to make that data more useful.
        
        Summarize what your client believes and your thoughts on the data you’ve received to do the analysis
        
        Evaluate
        Here’s the meat of the analysis, where you draw your conclusions about what you’ve learned in the first two steps.
        
        the key to evaluating the pieces you have isolated is comparison.
        
        Observations about the problem
        Observations about the data
        
        Analysis begins when you insert yourself
        
        Inserting yourself into your analysis means making your own assumptions explicit and betting your credibility on your conclusions.
        Whether you’re building complex models or making simple decisions, data analysis is all about you: your beliefs, your judgement, your credibility.
        
        Insert yourself
        
        Good for you
        
        1 more item...
        
        Good for your clients
        
        1 more item...
        
        Don’t insert yourself
        
        Bad for you
        
        1 more item...
        
        Bad for your clients
        
        1 more item...
        
        Decide
        Finally, you put it all back together and make (or recommend) a decision.
        
        The report you present to your client needs to be focused on making yourself understood and encouraging intelligent, data-based decision making.
        
        Context
        
        Interpretation of data
        
        Recommendation
    - - using empirical evidence to think carefully about problems.
      - all data analysis is designed to lead to better decisions
      - Data analysis is all about identifying problems and then solving them.
      - “exploratory data analysis,” where you explore the data for ideas you might want to evaluate further.
    - - If you’re aware of your mental model, you’re more likely to see what’s important and develop the most relevant and useful statistical models.
        
        Mental models should always include what you don’t know
        
        Ex: List some assumptions that would be true if MoisturePlus is actually the preferred lotion for tweens.
        List some assumptions that would be true if MoisturePlus was in serious danger of losing customers to their competition.
        
        Being clear about your knowledge gaps is essential.
- - - - đọc đề bài -> define problems
      - What data do you need to solve this problem?
      - You can divide those data needs into two
        categories: things you can’t control, and things you can.
      - define your actual constraints for this problem
        
        Constraints don’t tell you how to maximize profit; they only tell you what you can’t do to maximize profit.
        
        Decision variables, on the other hand, are the things you can control
      - When you want to get as much (or as little) of something as possible, and the way you’ll get it is by changing the values of other quantities, you have an optimization problem.
        
        To solve an optimization problem, you need to combine your decision variables, constraints, and the thing you want to maximize together into an objective function.
        
        There’s a lot more to reality than this model.
      - The objective is the thing you want to maximize or minimize, and you use the objective function to find the optimum result.
        
        Each “c” refers to a constraint.
        
        Each “x” refers to a decision variable.
        
        “P” is your objective: the thing you want to maximize.
        
        the space where product mixes are within the constraint lines is called the feasible region.
        
        Microsoft Excel and OpenOffice both have a handy little utility called Solver that can make short order of your optimization problems.
        
        Your models approximate reality and are never perfect,
        
        if your assumptions are accurate and your data’s good the tools can be pretty reliable.
        
        Your goal should be to create the most useful models you can
        
        Calibrate your assumptions
        to your analytical objectives
        
        Write down everything you think you know and everything you
        think you don’t know
        
        You can’t specify all your assumptions, but if you miss an important one it could ruin your analysis.
        You will always be asking yourself how far you need to go specifying assumptions.
        
        Don’t assume that two variables are independent of each other. Any time you create a model, make sure you specify your assumptions about how the variables relate to each other.
- - - - If you’ve got a lot of data and aren’t sure what
        to do with it, just remember your analytical objectives.
      - when looking at a new visualization
        => first question "What is the data behind the visualizations?”
      - To build good visualizations, first identify what are the fundamental comparisons that will address your client’s objectives.
      - Great visualizations
        
        Shows the data
        
        Makes a smart comparison
        
        Shows multiple variables
        
        A visualization is multivariate if it compares three or more variables.
        
        making your visualizations as multivariate as possible makes it most likely that you’ll make the best comparisons.
        
        One way of making your visualization more multivariate is just to show a bunch of similar scatterplots right next to each other,
      - Scatterplots are great tools for exploratory data analysis, which is the term statisticians use to describe looking around in a set of data for hypotheses to test.
        Analysts like to use scatterplots when searching for causal relationships, where one variable is affecting the other.
        As a general rule, the horizontal x-axis of the scatterplot represents the independent variable (the variable
        we imagine to be a cause), and the vertical y-axis of a scatterplot represents the dependent variable (which we imagine to be the effect).
        
        You don’t have to prove that the value of the independent variable causes the value of the dependent variable, because after all we’re exploring the data. But causes are what you’re looking for.
        
        This graphic was created with a open source software program called R
        
        use illustration programs like Adobe Illustrator and just draw visualizations, if you have visual ideas that other software tools don’t implement.
        
        If you want inspiration on designs, you should probably pick up some books by Edward Tufte
- - - - We’ll need some sort of insight into how they think about their releases, and we’ll need to know what kind of information they use in their decision.
        
        collect your evidence
        
        Some of information is publicly available, some of it is secret, and some of it is rumor.
        
        publicly: economy, industry, competitors, research, insights, google trends,..
        
        secret, rumor: thinking of CEO, of brand, strategy,.....
        
        Define your hypotheses related to problems
        Different answers to that question are your hypotheses for this analysis.
        
        put all this intelligence together and form a solid prediction. => connect variables (+), (-) relating to problems
        
        Using the relationships specified on the facing page, draw a network that incorporates all of them.
        
        in the real world are networked, not linear
        
        As an analyst, you need to see beyond simple models like linear and expect to see causal networks
        
        run a hypothesis test: Falsification is the heart
        of hypothesis testing
        
        Don’t try to pick the right hypothesis; just eliminate the disconfirmed hypotheses. This is the method of falsification, which is fundamental to hypothesis testing.
        Falsification enables you to have a more nimble perspective on your hypotheses and avoid a huge cognitive trap.
        Use falsification in hypothesis testing and avoid the danger
        of satisficing. The big problem with satisficing is that when people pick a hypothesis without thoroughly analyzing the alternatives, they often stick with it even as evidence piles up against it
        
        Diagnosticity: Use the evidence to rank hypotheses in the order of which has the fewest evidence-based knocks against it.
        
        Diagnosticity is the ability of evidence to help you assess the relative likelihood of the hypotheses you’re considering. If evidence is diagnostic, it helps you rank your hypotheses.
        The “+” symbol indicates that the evidence supports that hypothesis, while the “–” symbol indicates that the evidence counts against the hypothesis.
        Nondiagnostic evidence doesn’t get you anywhere.
      - You need to figure out how to compare the data you do have with your hypotheses about when PodPhone will release their new phone.
        
        When you are looking at data variables, it’s a good idea to ask whether they are positively linked, where more of one means more of the other (and vice versa), or negatively linked, where more of one means less of the other.