mid 2 cheat sheet

# mid 2 cheat sheet - Z-scores-Z-scores are standardized...

This preview shows pages 1–2. Sign up to view the full content.

Z-scores - Z-scores are standardized scores that tell where a raw scores (x) is located in an entire distribution in terms of standard deviation (SD) units. Purpose of z-scores Z-scores enable you to: -determine the relative standing of a raw score with a distribution -compare scores that come from different distributions -standardize a distribution to have a mean of 0 and a standard deviation of 1, or any mean and standard deviation you specify. -determine probability values associated with a range of raw scores Z-score formula Components of z-score -sign tells you if the score is above (z>0), or at (z=0) the mean. -magnitude tells you how far away the score is from the mean in standard deviation units. Converting z-score to raw score Z-score Transformations: Comparing score from different distribution -to compare scores, need to put them on a “common metric” -to do this, raw scores within each distribution are transformed to z-scores Transforming raw scores : designating your own μ and σ -convert raw score to z score using parameters from original distribution: -calculate new raw score, substituting in the new μ and σ. Transforming Raw scores to z-scores: transforming a set of raw scores to z-scores: -does not change the shape of the distribution -does not change the location of individual scores within the distribution Probability and the Normal distribution Random Sampling -each individual in a population must have an equal chance of being selected -we use sampling with replacement: each sample must be replaced before the next selection is made. Assumption of standard normal curve problems -the variable is normally distributed -the mean and standard deviation for the population is known. -the SNC does not apply if these two assumptions are not met Sampling Distribution of the Mean Samples and Sampling Error -with inferential statistics, we use sample statistics (e.g. M) to estimate population parameters (e.g. μ). -different samples from the same population will produce different

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 2

mid 2 cheat sheet - Z-scores-Z-scores are standardized...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online