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## Exam 1 Study Guide

by: Cheyenne Hunt

45

0

4

# Exam 1 Study Guide Stat 2510

Marketplace > Auburn University > Statistics > Stat 2510 > Exam 1 Study Guide
Cheyenne Hunt
AU
GPA 3.33

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These notes cover what's going to be on the first exam: introduction to statistics, graphing, descriptive statistics, and etc.
COURSE
Statistics for Health Sciences
PROF.
Dr. Regina Halpert
TYPE
Study Guide
PAGES
4
WORDS
CONCEPTS
Statistics, standard deviation, inferential, Descriptive, observational, experimental, qualitative, quantitative, Bar Graphs, mean, median, mode, variance, Quartiles
KARMA
50 ?

## Popular in Statistics

This 4 page Study Guide was uploaded by Cheyenne Hunt on Tuesday September 6, 2016. The Study Guide belongs to Stat 2510 at Auburn University taught by Dr. Regina Halpert in Fall 2015. Since its upload, it has received 45 views. For similar materials see Statistics for Health Sciences in Statistics at Auburn University.

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Date Created: 09/06/16
STAT 2510 Exam 1 Study Guide  Statistics: the collection, organization, summarization, analysis, and drawing conclusion of data o Descriptive: collecting, organizing, and summarizing information  Measures of Central Tendency: mean, median, and mode  Measures of Variation: standard deviation, variance, range, and interquartile range  Measures of Position: quartiles, percentiles, and z-scores o Inferential: analyzing data and drawing conclusions about population based on the sample  Data: information collected about sample o Rows: cases; data about WHOM we record (respondents, subjects, …) o Columns: variables; characteristics collected; WHAT information you collect  Categorical (Qualitative): not mathematical, choices, worded responses, categories, etc.  Quantitative: data that can be counted, measured, and mathematically operated  Continuous: decimals and fractions; temperature, weight, time, etc.  Discrete: whole numbers o Identifiers: the first or first few columns; identifies each case and is unique to it o 5 Ws and How- Guides to Data Collection  Who: the cases  What: variables  Where: samples and population must be representative of what you want to know  When: data must be current to be applicable  Why: reason, problem, inquiry  How: tells the data collection methods  Data mining: finding and using existing data  Experiment: manipulating a group to compare to a non-manipulated group  Observation: observing a group, no manipulation at all  Survey: responses  Graphing o Categorical (Qualitative) Data  Bar Graph: shows frequency of each categorical variable Student Political Affiliations 23% 33% 45% Democratic Republican Other  Student Political Affiliations 18 15 13 9 10 0 Frequency Political Affiliation Pie Chart: shows relative frequencies (percentages) of each categorical variable Political Frequen Relative Party cy Frequency Democratic 13 13/40= 33% Republican 18 18/40= 45% Other 9 9/40= 23% o Quantitative Data  Histogram: shows frequency of each quantitative value based on groups (bins)  Stem & Leaf Plot: shows frequency of each quantitative value based on groups (stem); used to preserve data 4, 9, 9, 15, 20, 27, 37, 41 0 4 9 9 Bin Frequenc 1 5 y 2 0 7 0-9 3 10- 1  Skewed Data 3 o 7If the data is skewed, the median and IQR should be reported 19 4 o 1If the data is unimodal (has only 1 mode) and symmetric, 20- 2 then the mean and standard deviation should be reported 29 30- 1 39 40- 1 49  Quartiles, IQR, and Outliers o Outlier: any values less than Q1-(1.5 IQR) or greater than Q3+(1.5 IQR) o Q1: median of the lower half of the distribution o Q2: median of the distribution o Q3: median of the upper half of the distribution o IQR: Q3-Q1 3, 24, 25, 27, 28, 29, 32, 33, 85 Q1= 24.5 Q2= 28 Q3= 32.5 IQR= 8 Outliers= # < 24.5 – (1.5 (8)) = 12.5; 3 # > 32.5 + (1.5 (8)) = 42.5; 85  5 # Summary and Box Plots o Minimum, Q1, Q2, Q3, Maximum o Outliers are plotted as separate points; If the min or max is an outlier, use the next value as an endpoint 3, 24, 25, 27, 28, 29, 32, 33, 85 Min= 24 Q1= 24.5 Q2= 28 Q3= 32.5 Max= 33 Outliers= 3, 85  Standard Deviation o Measures how far each data point deviates from the mean o 2 s= √∑ (x−x )/(n−1) 85, 87, 92, 94, 96, 100 x x−´x 2 (x−´ ) 85 -7.3 53.73 87 -5.3 28.41 92 -.3 .09 94 1.7 2.89 96 3.7 13.69 100 ❑ 7.7 59.29 Sum= 554, n= 6, ( ) = Sum always equals 0 Sum= 158.1, s=5.6 92.3

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