Many statistical models try to predict an outcome from one or more predictor variables. Statistics includes five things: Standard error (S), parameters (P), interval estimates (I), null hypothesis significance testing (N) and estimation (E), together making SPINE. Statistics often uses linear models, as this simplifies reality in an understandable way. All statistical models are based on the same thing:The data we observe can be predicted from the model we choose to fit plus some amount of error. The model will vary depending on the study design. The bigger a sample is, the more likely it is to reflect the whole population.PARAMETERA parameter is a value of the population. A statistic is a value of the sample. A parameter can be denoted by ‘b’. The outcome of a model uses the following formula:In this formula, ‘b’ denotes the parameter and ‘X’ denotes the predictor variable. This formula calculates the outcome from two predictors. Degrees of freedom relates to the number of observations that are free to vary. One parameter is hold constant and the degrees of freedom must be one fewer than the number of scores used to calculate that parameter because the last number is not free to vary.ESTIMATIONThe method of least squares is minimizing the sum of squared errors. The smaller the error, the better your estimate is. When estimating a parameter, we try to minimize the error in order to have a better estimate. STANDARD ERRORThe standard deviation tells us how well the mean represents the sample data. The difference in means across samples is called the sampling variation. Samples vary because they include different members of a population. A sampling distribution is the frequency distribution of sample means from the same population. The mean of the sampling distribution is equal to the population mean. A standard deviation of the sampling distribution tells us how the data is spread around the population mean, meaning that the standard...