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Lind Chapter 1-4 Outline

by: Morgan Anderson

Lind Chapter 1-4 Outline MGMT 276

Morgan Anderson
GPA 3.0
Statistical Inference in Management

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Complete outline on chapters 1-4 in "Business Statistics for Business Economics" by Lind
Statistical Inference in Management
Study Guide
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This 4 page Study Guide was uploaded by Morgan Anderson on Monday February 16, 2015. The Study Guide belongs to MGMT 276 at University of Arizona taught by Delaney in Spring2015. Since its upload, it has received 103 views. For similar materials see Statistical Inference in Management in Management And Operations at University of Arizona.

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Date Created: 02/16/15
ii iii ii i ii iii 97 gepvep Basic Statistics for Business and Economics by Lind Chapter 1 What is Statistics Why study statistics numerical information is everywhere statistics techniques are used to make decisions that affects our daily lives knowledge of statistical methods will help you understand how decisions are made and give you a better understanding of how they affect you In order to make an informed decision you need to be able to determine whether existing info is adequate or additional info is required gather additional info if needed in such a way that does not provide misleading results summarize info in useful and informative manner analyze available info draw conclusions and make inferences while assessing the risk of an incorrect conclusion 9 9 The science of collecting organizing presenting analyzing and interpreting data to assist in making more effective decisions a collection of numerical information types of statistics descriptive statistics inferential statistics 9quot pi 2 a b 9 93839 l 2 methods of organizing summarizing and presenting data in an informative way organizing data frequency distribution various charts specific measures of central location central tendency and dispersion mean median mode also known as statistical inference best guess of a population value based on sample information the entire set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest a portion or part of the population of interest types of variables variable or attribute categorical or nonnumeric interested in how many or what percent fall in each category indicates how many or how much can assume only certain variables there are gaps between values can assume any value within a specific range typically result from measuring levels of measurement dictates the calculations that can be done to summarize and present the data F WN i observations of qualitative variable can be classified and counted ii variable of interest is divided into categories or outcomes iii no natural order to the outcome iV ie gender jersey s ethnic group vehicle type i data classifications are represented by sets of labels or names high medium low that have relative values ii because of relative values data classified can be ranked or ordered iii ie grades ABCDE rating a song POOR GOOD i data classifications are ordered according to the amount of the characteristic they possess ii equal differences in the characteristic are represented by equal differences in the measurements iii ie temp FC GPA likert scale dress size i quantitative data is recorded on the ratio level of measurement ii data classifications are ordered according to the amount of the characteristics they possess iii equal differences in the characteristics are represented by equal differences in the numbers assigned to the classifications iV zero point is the absence of the characteristic and the ratio between two numbers is meaningful V ie number of patients seen number of sales calls made temp K price Chapter 2 Describing Data a grouping of qualitative data into mutually exclusive classes showing the number of observation in each 9 class i number of observations in each class relative class frequencies i shows the fraction of the total number of observations in each class 2 graphic presentation of qualitative data 0 i graph in which the classes are reported on the horizontal aXis and the class frequencies on the vertical aXis ii class frequencies are proportional to the height of the bars i chart that shows the proportion or percent that each class represents of the total number of frequencies constructing frequency distributions quantitative data i a grouping of data into mutually exclusive classes showing the number of observations in each class i halfway between the lower limits of two consecutive classes ii computed by adding the lower limits of consecutive classes and dividing by 2 i subtract the lower limit of the class from the lower limit of the next class graphic presentation of a frequency distribution P 9 i Graph where classes are marked on the horizontal aXis and the class frequencies on the vertical aXis class frequencies are represented by the heights of the bars on the bars are drawn adjacent to each other i shows the shape of a distribution and is similar to a histogram line segments connecting points formed by the intersections of the class midpoints and the class frequencies ii midpoint of class on XaXis iii class frequencies on yaXis Chapter 3 Describing Data 1 two numerical ways of describing quantitative data a measures of location or averages i measure of central tendency 1 every set of interval or ratio level data has a mean 2 all values are included in computing the mean 3 the mean in unique 4 sum of the deviations of each value from the mean is zero iii weighted mean 1 midpoint of the values after they have been ordered from the smallest to the largest or the largest to the smallest 2 is not affected by extremely large or small numbers 3 can be computed for ordinal interval and ratio level data 1 the value of the observations that appear most frequently 2 can be determined for all levels of data 1 characteristic of a population 1 characteristic of a sample b measures of dispersion or variation i positively skewed 1 largest to smallest ii negatively skewed 1 smallest to largest iii used to compare the spread in two or more distributions iV can be used to evaluate the reliability of two or more measurements of location V largest value smallest value 1 the arithmetic mean of the absolute values of the deviations from the arithmetic mean 2 advantages a uses all values in the computation b easy to understand it is the average amount by which values deviate from the mean 1 arithmetic mean of the squared deviations from the mean 1 square root of the variance 9 for any set of observations sample or population the proportion of the values that lie within k standards deviations of the mean is at least llkA2 where k is any constant greater than one 9 i ii iii For a symmetrical bell shaped frequency distribution approximately 68 of the observations will lie within plusminus one standard deviation of the mean approximately 95 will lie within plusminus two standard deviation of the mean approximately 997 will lie within plusminus 3 standards deviations of the mean Chapter 4 Describing Data a 2 a 3 a groups the data as little as possible and we do not lose the identity of an individual observation divide a set of observations into four equal parts divide a set of observations into 10 equal parts ms divide a set of observations into 100 equal parts skewness mean and median are equal and the data values are evenly spread around these values i ii iii single peak and values extend further to the right mean is larger than median more common i ii 1 i a 7 a 8 a 9 a b single peak and values extend further to the left mean is smaller than median unimodal bimodal trimodal multiple peaks when we study the relationship between a single variable bivariate when we study the relationship between 2 variables scatter diagram need to variables contingency tables a table used to classify observation according to two identifiable characteristics cross tabulation that simultaneously summarizes two variables of interest


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