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COMM 2513 Statistics Week 2 notes

by: Rachel Notetaker

COMM 2513 Statistics Week 2 notes COMM 2513

Marketplace > University of Oklahoma > Communication > COMM 2513 > COMM 2513 Statistics Week 2 notes
Rachel Notetaker
GPA 3.0
Introduction to Statistics

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Introduction to Statistics
Class Notes
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Popular in Introduction to Statistics

Popular in Communication

This 3 page Class Notes was uploaded by Rachel Notetaker on Saturday September 12, 2015. The Class Notes belongs to COMM 2513 at University of Oklahoma taught by Johnson in Summer 2015. Since its upload, it has received 57 views. For similar materials see Introduction to Statistics in Communication at University of Oklahoma.


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Date Created: 09/12/15
COMM 2513 Statistics Week 2 notes Visual representation of data A By putting our ndings in visual form it helps us to 1 See patterns easier 2 Draw conclusions easier 3 Communicate our ndings to other individuals more easily Types of frequency distributions A Frequency distribution we group different values of the variable together pair each value with a variable with the number of times it occurs in the sample ex on pg 18 B Relative frequency distribution pairs each value with a variable with its percentage in the sample ex on pg 20 C Cumulative frequency distribution gives for each interval the accumulation of the frequency up to and including that interval tells us how many people are in that category or less ex on pg 19 1 Cumulative relative frequency distributions gives for each interval the accumulation of the percentages up to and including that interval Pictorial descriptions A Stem and leaf displays uses the rst digit or more as a base or stem and the last digit as an extension of that base 1 Backtoback stem and leaf display same as the normal stem and leaf displays except it compares two groups they share a common stem the leaves from one group go to the right and the leaves from the other go to the left 393 Bene t you can recover all the original data from the stem and leaf display B In bar graphs and histograms the horizontal axis shows the intervals categories and the vertical axis shows the frequency 1 Bar graph only use for nominal data bars don t touch separate NOT continuous 2 Histogram use for ordinal interval and ratio bars touch order makes a difference 3 Frequency polygon looks at quantitive data lets us look at shape of distribution draw on top of histogram NOT bar graph start at origin dot on middle of each bar connect the dots 4 Pie chart a graph drawn as a circle with segments representing percentage s or proportions of the distribution C Scatterplot plots scores from each subject on 2 variables one on horizontal axis and one on vertical axis visual representation of a correlation IV Four characteristics that can be displayed with graphs A Central tendencytells you what a typical or central score is in a distribution 3 measures mean median and mode B Variability how spread out the scores are range variance standard deviation C Skewness how your distribution compares to a normal distribution is our data symmetrical or not ex on pg 21 D Kurtosis how steep the data is ex on pg 21 1 Platykurtosis atter than normal distribution 2 Mesokurtosis normal distribution 3 Leptokurtosis steeper than normal distribution V Central Tendency A Measures of this show you where the central score is B Three measures from least to most used 1 Mode most frequent score if one exists Advantages best for nominal data easy to calculate Disadvantages can have more than one but if more than 2 or 3 loses its meaning as a typical score might not be in the middle of the distribution 2 Median middle value in the distribution once the data are rank ordered Way to calculate rank the data from lowest to highest if odd number of data points then median is score right in middle if even number of data points then median is average of the 2 scores in the middle Advantages shows the value at which fty percent of the data is below this value and fty percent above tends not to be affected as much by skewed data and extreme scores as the mean is Disadvantages not as many people know what it is 3 Mean average sum of the scores divided by the sample size Advantages most commonly used and easy to understand Disadvantages affected by extreme scores Vl Variability A Distributions can have the same central tendency score and still be very different 1 We also need to know something about the distribution of the scores the spread of the scores around the measure of central tendency such as the mean 2 Variability the spread of the scores the tendency of the scores to depart from some measure of central tendency B Measures of variability Range the highest score minus the lowest Advantages easy to calculate gives quick sense in spread of data Disadvantages very affected by extreme scores Variancetells you how far each score is from the mean in squared unitsaverage squared distance from the mean Advantages not as affected by extreme scores Disadvantages in squared units rather than original units so harder to interpret Standard Deviation square root of the sample variable average distance from the mean Advantages puts it back in the same units of measurement as the original data most common measure Disadvantages can be hard to interpret


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