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picture1_Outline For Powerpoint Presentation Example 69272 | Chapter 9 Fpsr 2


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File: Outline For Powerpoint Presentation Example 69272 | Chapter 9 Fpsr 2
chapter 9 outline 1 modeling multivariate reality 2 the population regression function 3 from two variable to multiple regression 4 interpreting multiple regression 5 which effect is biggest 6 statistical ...

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       Chapter 9 Outline
  • 1. Modeling multivariate reality
  • 2. The population regression function
  • 3. From two-variable to multiple regression
  • 4. Interpreting multiple regression
  • 5. Which effect is “biggest”?
  • 6. Statistical and Substantive Signicance
  • 7. What happens when we fail to control for 
   Z?
      Crossing the fourth causal
           hurdle
  • In any observational study, how do we 
   “control for” the effects of other variables?
  • Multiple regression, this chapter's topic, is by 
   far the most common method in the social 
   sciences.
               The population regression function
       We can generalize the population regression model from Chapter 8:
         bivariate population regression model:
       to include more than one cause of Y , which we have been calling Z:
            multiple population regression model:
        The interpretation of the slope coefficients here is similar to interpreting
        bivariate coefficients, with one big difference. In both, the coefficient in
        front of X (     in the two-variable model,       in the multiple regression
        model) represents the “rise-over-run” effect of X on Y . In the multiple
        regression case,      represents the effect of X on Y while holding constant
        the effects of Z.
               Remember…
   Recall from Chapter 8 that the formula for a two-variable 
    regression line is the following (in a sample):
   And recall that, in order to understand the nature of the effect that X has on Y , the 
   estimated coefficient      tells us, on average, how many units of change in Y we should 
   expect given a one-unit increase in X. The formula for      in the two-variable model, as 
   we learned in Chapter 8, is:
                           From a line to a plane
       Given that our goal is to “control for” the effects of some third variable,
       Z, how exactly is that accomplished in regression equations? If a
       scatterplot in two dimensions (X and Y ) leaves the formula for a line,
       then adding a third dimension leaves the formula for a plane. And the
       formula for that plane is:
     That might seem deceptively simple. A formula representing a plane simply adds the 
     additional term        to the formula for a line.
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...Chapter outline modeling multivariate reality the population regression function from two variable to multiple interpreting which effect is biggest statistical and substantive signicance what happens when we fail control for z crossing fourth causal hurdle in any observational study how do effects of other variables this s topic by far most common method social sciences can generalize model bivariate include more than one cause y have been calling interpretation slope coefficients here similar with big difference both coefficient front x represents rise over run on case while holding constant remember recall that formula a line following sample order understand nature has estimated tells us average many units change should expect given unit increase as learned plane our goal some third exactly accomplished equations if scatterplot dimensions leaves then adding dimension might seem deceptively simple representing simply adds additional term...

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