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Final

Stats study notes

4 Pages
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Department
Kinesiology & Health Science
Course Code
KINE 2050
Professor
Merv Mosher

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Description
-1- Stats Study Notes Statistics- Data used to make judgment (decision) about a situation 1. Descriptive - Polls and Survay - Correlation 2. Inferential -Cause and effect relationships Population > Parameter Sample> Statistic 4 Levels of measurement 1. Nominal- (catigories, can only measure frequency) 2. Ordinal- Data is ranked (1 2 3 ) Don’t know how far they are from each other, first might be 4 seconds and second might be 4.1 seconds or 10seconds we don’t know we only know where it is in relation to other scores) 3. Interval- Equal points or units of measurement assigned but 0 is a value, so therefore things cant be proportional. Ex tempature 4. Ratio- Measures the distance from 0 (zero means nothing) therefore comparisons can be made, ex 50kg is half of 100kg. Weight, distance Independent Variable- The one that is controlled or manipulated Dependant- The one we see how it is effected Frequency Distribution- Ungrouped- each value has its own group (frequency of 1, 5 times/ frequency of 2 3 times/ frequency of 3 9 times) Grouped- Values are put into intervals (frequency of 1-3, 17 times. Frequency of 4-6 10 times) Making intervals - Find N of scores - Look up #of intervals for that N on the chart - i=(biggest value- smallest value)/ # of i Shapes of Curves Normal Distribution = Bell curve (mean = median= mode) Bimodal= 2 modes (usally when you have 2 characteristics ) -1- -2- Leptokurtic- Not enough intervals (too narrow) Platykurtic- Too many intervals (too wide) Positively skewed- someone grabbed the positive side and stretched it (an extreme big score) Measures of central Tendency – Meant to tell us where bulk of score lies Mean- Interval and Ratio (unless badly skewed –because mean moves towards skew) Median-Ordinal always Mode- (frequency/Which one happens most)- used with nominal always Measures of Variance Range- Highest- lowest (gives range between where 100% of values fall) Standard Deviation- SD= √Variance (measures deviation of all scores from mean) Formula >> Variance= same thing but no sqrt +/-1SD tells you where 68% of the scores lie So if SD is 15 and the mean is 100 68% of scores are from 85-115 The bigger the SD the more spread out the data If mean and median differ by more then 1SD use median Z-scores- shows raw score in relation to group (how many SDs from the mean the score is) Percentile-Percentage of people that got same ore worse then you Tscore- Keeps everything positive numbers (T=10Z+50) Sampling error- Is the error when choosing a sample from a population (maybe you choose all the tall kids) Bigger SD = Bigger SEM Bigger N= Smaller SEM SEM= SD/√N 1SD- where 68% of scores fall 1SEM- Where 68% of means fall If we want 95% interval multiply SEM by 1.96 99% 2.58 -2- -3- If N<30 then look up the value that you have to multiply SEM with on the t-table using the right amout of DF Probability- Chance that an event will occur P(A)
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