KIN250-Quiz1Review.pdf KIN 250
Popular in Measurments in Kinesiology
Popular in Kinesiology
verified elite notetaker
This 3 page Study Guide was uploaded by Brittany Ballog on Sunday September 27, 2015. The Study Guide belongs to KIN 250 at Michigan State University taught by Larissa True in Summer 2015. Since its upload, it has received 28 views. For similar materials see Measurments in Kinesiology in Kinesiology at Michigan State University.
Reviews for KIN250-Quiz1Review.pdf
Report this Material
What is Karma?
Karma is the currency of StudySoup.
You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!
Date Created: 09/27/15
KIN 250 Fall 2013 Quiz Review Outline Levels of Measurement 0 Nominal qualitative classi cation simplest least precise Example gender 0 female 1 male 0 Ordinal rank order the items of which has less and which has more of the quality represented by the variable but still don t say quothow much morequot Example health status 1 very poor 2 poor 3 fair 4 good 5 excellent Interval rank order but also quantify and compare the sizes of differences between them Example temperature can still be 0 but it is still a quantity or also 30 degrees 0 Ratio identi able absolute zero point where zero indicates nothing is present Example salary 100000 one year then next year it is 0 Measures of Central Tendency Mean add up all numbers then divide by n Pros summarizes data in an easy understandable way uses all the data used in many stat applications most common and trustworthy Cons affected by outliers not good for skewed data 0 Median middle score if odd number middle value if even number then take the two middle values and divide by 2 Pros not in uenced by outliers useful with skewed data Cons doesn t use all the data 0 Mode most frequency most common value Pros good for rough estimates calling for attention to values that cluster Cons doesn t use all the data Unimodal distribution where a single score is most frequent has one mode 0 Bimodal where there are ties for the most frequent score with two scores 0 Multimodal where there are ties for the most frequent score with more than two scores tie The normal curve Leptokurtic K gt 0 long tall curve upward o Platykurtic K lt 0 no curve really like a plateau o Mesokurtic K 0 normal curve wave 0 Positive skew shifts to the left 0 Negative skew shifts to the right Measures of Variabilitv Range difference between the largest and the smallest observation or score 0 use range when we d like to know the worst case scenario Pros simple measure Cons very rough measure unstable sensitive to extreme values 0 Variance amp Standard Deviation how spread out the data is around the mean Variance average deviation of individual values with respect to the mean of a data set 0 Calculated as the average of the squared differences of the mean V sum of squarestotal N 0 Standard Deviation positive square root of the variance most common way of measuring variability Small SD observations are clustered around the mean Large SD observations are scattered widely from the mean Note You don t need to know equations but you should understand what each of these mean and be able to apply them to numerical values How do they work together Once you have the variance what must you do to calculate the standard deviation What is the purpose of this calculation Variance is the standard deviation squared 2 scores Zscores expresses a score in terms of how many standard deviations it is away from the mean in a normal curve Zscore data pointmeanSD Zscale always expressed with a mean of 0 and a SD of 1 0 Relative frequency Use the zscore to determine how many observations fall abovebelow a certain point on the normal curve 0 Use a zscore to nd raw scores must have the mean and SD can be calculated in the form of probabilities percentages actual raw score Correlation Correlation stats that depict the strength of a relationship between two or more variables or X amp Y o What information do we need to conduct a correlation Strength weak or strong Direction positive or negative Shape of relationship Pearson r measure of linear association between two or more variables 0 use with ratio or interval data only 0 Shape is shown by a scatter gram x and y with a bunch of olots rquot2 shared variance square of a correlation coef cient
Are you sure you want to buy this material for
You're already Subscribed!
Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'