In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line (or hyperplane) that minimizes the sum of squared differences between the true data and that line (or hyperplane). For specific mathematical reasons (see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters (e.g., quantile regression or Necessary Condition Analysis) or estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression).
Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to infer causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal relationships between a dependent variable and a collection of independent variables in a fixed dataset. To use regressions for prediction or to infer causal relationships, respectively, a researcher must carefully justify why existing relationships have predictive power for a new context or why a relationship between two variables has a causal interpretation. The latter is especially important when researchers hope to estimate causal relationships using observational data.
Hi, I am having some difficulty with this problem:
what would be Y^h^a^t if s_y_/_x = 439, n = 24 and 95% confidence interval estimate for the average Y given a particular value of X is 1125 and 1695.
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I know Y^h^a^t = b_o + b_1x but I am not sure how I can use the...
Say i have a set of points that aren't necessarily linear, but are planar, and all follow a 'general' trend in the same direction. Say, something like this:
http://img343.imageshack.us/img343/2218/pointdistribution1jo.jpg
This is an entirely random example, but hopefully it'll help you...
If the regression equation is y=2.3-1(x) and r^2 = 0.78, what is the value of the coefficient of correlation?
my answer was 0.88 and i got it wrong, is it -0.88 because of the negative slope in the equation? please help fast.. thnx
I'm writing a philosophy essay and surfing around the internet and talking to my teacher has led me to believe that some poeple believe that there is some inherent inconsistency in the idea of an infinite regression of causes. I can not think of anything concrete, so does anyone have any ideas...
Hi,
Complicated stats question, but maybe someone out there knows how to proceed. I am trying to perform regression on two variables, the samples of which have significant, but known error components. Ordinary least squares regression cannot be used as it is assumed that measurements are made...
Why according to statisticians should a regression line (line of best fit) pass through the mean of x and mean of y? (ie - x bar and y bar) Why does this make the regression line more accurate? Thanks in advance. :-)
I have been trying to figure this out for 2 weeks.
A helicopter is begins its horizontal motion 2500 ft from the center of a targe 30 ft above the target traveling at 40mph. The target ( a pile of boxes) is stacked 15 ft high off the ground. At the appropriate time, a person is to jump out...
Hello there. I have just finished a biological experiment on "effect of trypsin concentration on rate of casein hydrolysis"
I have already obtained a graph, and I used a program called "Graphpad Prism" to analyse the data usin nonlinear regression (3rd degree polynomial). I have got all the...
Past life regression
Has anyone else ever tried this? What did you think.
Tsunami and I both tried this. A local author and hypnotist offered free hypnosis and regressions so that he could use the data for his book. Since I had practiced self hypnosis for many years I was sure that I could...
Concrete road pavement gains strength over time as it cures. Highway builders use regression lines to predict the strength after 28 days (when curving is complete) from measurements made after 7 days. Let x be strenth after 7 days (in pounds per square inch) and y the strength after 28 days...