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# LSP 121 Midterm Study Guide LSP 121

DePaul

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This 3 page Study Guide was uploaded by Gillian Notetaker on Sunday January 31, 2016. The Study Guide belongs to LSP 121 at DePaul University taught by Mary Jo Davidson in Fall 2016. Since its upload, it has received 117 views. For similar materials see Quantitative Reasoning in Mathematics (M) at DePaul University.

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Date Created: 01/31/16

LSP 121 Midterm Notes Why do we measure things? - Compare - Fit - Visualize - Evaluate Next step – Determining where that thing stands in comparison with other things in a group they all have in common. Descriptive Statistics – The summary measures that allow us to evaluate an existing group of values including measures of central tendency and dispersion (spread). - Summary comparisons are calculated based on the values of existing data. - Values for the entire population are known. Statistics are calculated based on the values for the entire population. - No inferences can be made beyond the measured population. Inferential Statistics – Specific procedures used. - Resulting measures of an existing subset of data support us in predicting the values of the entire set of data. - Can only use those procedures if certain criteria are met. - Measure a sample, infer values for the entire population. Statistics – - Variables. Types and families. - Measurement of a single variable. - Analysis of single variables with SPSS. - Measurement of two variables. - Analysis of two variables with SPSS. - Deceptive statistics. Univariate Statistics - Measuring/evaluating only one variable for each member of a set. - Variable: Something measured for an item. Has a value associated with each item. - Frequency: Number of times a particular variable value occurs in a set. The count of that particular value. - Frequency Distribution: All of the various frequencies of values in a set, ordered by value. - Presented as a frequency distribution table or a histogram. o Value, frequency, percentage, cumulative percentage. Categories of Descriptive Statistics for a Single Variable Measures of central tendency. o Mean. Formulas – Other – Statistical – Average o Median. Where n is the number of items in the set. (n+1)/2 o Mode Measures of dispersion. o Standard Deviation. A measure of how data values are spread around the mean of a data set. Formulas – Other – Statistical – STDEV.P o Minimum o Maximum o Range. Difference between the minimum and maximum values. Max minus Min. o Quartile. Position of a data point in a data set. Compared to the range divided into 4 pieces. o Percentile. Position of a data point in a data set. Compared to the range divided into 100 equal pieces. Formulas – Other – Statistical – PERCENTRANK.INC. To find the percentile of values above a value, subtract the percentile of the value from 100. th th 76 st 100 pthcentile = Fourth Quartile 51 -> 75 percentile = Third Quartile 26 -> 50 percentile = Second Quartile st th 1 -> 25 percentile = First Quartile Q4 = Value at the top of the fourth quartile. Q3 Q2 Q1. Formulas – Other – Statistical – QUARTILE.INC - To find a quartile that a variable is in, compare the variable value to st the quartile numndrs. Is or below Q1, 1 quartile. Above Q1 and at/below Q2, 2 quartile. Etc. Histogram – - A graphical way to present a frequency distribution. - Presents the frequencies of groups of values. - We can tell group of values with the highest frequency, shape of distribution, and if there are outliers. - Created by defining a set of bins and counting how many data points lie in each. - Bars drawn with height proportional to the number of data points in each bin. - Normal distribution: Important distribution pattern, occurs in nature (blood pressure, height). Bell-shaped curve. o 68% of measurements within one SD of the mean. o 95% of measurements within two SD of the mean. o If distribution is normal, more powerful statistics for the variable can be calculated. Z-score – - Shows where the value lies in the distribution in comparison with the mean of the distribution. - A form of standardization/normalization. o Normalization – Converting values to a similar scale in order to allow comparison of the values. - Score of 0 means score is the same as the mean. - Can be positive or negative, indicating above or below the mean and by how many standard deviations. - (Raw score – mean) / standard deviation - more than 2 or less than -2 is an outlier. Two-Variable Correlation - “R” is the statistic of correlation. o Measures the strength and direction of the correlation. o You measure two variables for all set. o As one variable increases, what happens to the other? o No correlation, R = 0 o Weak = 0 – 0.3 o Moderate = 0.3 – 0.7 o Strong = 0.7 – 1.0 o Scatter plot data, with linear trend line o Correlation is not causation

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