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The following are the major assumptions made by standard linear regression models with standard estimation techniques (e.g. ordinary least squares): Weak exogeneity. This essentially means that the predictor variables x can be treated as fixed values, rather than random variables. This means, for example, that the predictor variables are ...
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
Statistical hypothesis testing is considered a mature area within statistics, [25] but a limited amount of development continues. An academic study states that the cookbook method of teaching introductory statistics leaves no time for history, philosophy or controversy. Hypothesis testing has been taught as received unified method.
Suppose that we have a random sample, of size n, from a population that is normally-distributed. Both the mean, μ, and the standard deviation, σ, of the population are unknown. We want to test whether the mean is equal to a given value, μ 0. Thus, our null hypothesis is H 0: μ = μ 0 and our alternative hypothesis is H 1: μ ≠ μ 0 . The ...
For instance, if estimating the effect of a drug on blood pressure with a 95% confidence interval that is six units wide, and the known standard deviation of blood pressure in the population is 15, the required sample size would be =, which would be rounded up to 97, since sample sizes must be integers and must meet or exceed the calculated ...
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...
The coefficients for the linear regression specify the slope and intercept of the line that joins the two group means, as illustrated in the graph. The intercept is 2 and the slope is 4. Compare the result from the linear regression to the result from the t-test. From the t-test, the difference between the group means is 6-2=4.
The "68–95–99.7 rule" is often used to quickly get a rough probability estimate of something, given its standard deviation, if the population is assumed to be normal. It is also used as a simple test for outliers if the population is assumed normal, and as a normality test if the population is potentially not normal.