data to construct several graphs, such as a boxplot, histogram, stem and leaf plot and a scatter plot to determine if or how the data is skewed and if there are any outliers. If we identify any outliers we will determine whether these are errors. Third we will use a t-test, Chi squares and binomial approximation to construct a hypothesis about the data. Fourth, we will construct a linear regression and look for correlation. The analyzed data from these
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© Orangetree Business Solutions Private Limited, 2012 No part of this book should be referenced or copied without the prior permission of the company. A FEW WORDS TO THE STUDENTS Analytics is becoming a popular tool for managerial decision making. It‘s still not so widespread in countries like India, but in the west it has become a standard practice. Previously studying analytics involved an in depth knowledge of statistics and programming languages. But widespread availability of statistical
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2. When a scatter plot's (x,y) points are all pretty close to the "line of best fit", then how would you describe the relationship between the points? 3. True or False: Cause and effect relationships cannot be determined using the coefficient of determination. 4. True or False: Correlations are used to help identify the relationship between two variables. For example, the amount of rain in July (x) and the size of tomatoes in August (y) could be analyzed using correlation and regression
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In our efforts to choose two of the top five companies in Consumer, Cyclical (IYC) we have chosen to evaluate Wal-Mart (WMT) which was ranked first and Home Depot (HD) ranked third in Consumer, Cyclical. You have asked our team to follow six steps based on our selection of WMT, HD, and IYC for a complete history of this sector. There appears to be many observations which contain Walmart having a large effect on the entire sector thus creating the entire market to shift. However, Home Depot has
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Multiple Regression Model | Case # 28 House Prices | | A group of statistic student’s objective is to provide a business solution using statistical calculations and tools on a sample data. | | Upaiwan Porndumrongkit Ana Sanchez George Satiah Kritchapon Sopawatjirarich | 10/16/2010 | Executive Summary Summary:
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Before we begin with the data summary, descriptive statistics is defined as mathematical methods (such as mean, median, standard deviation) that summarize and interpret some of the properties of a set of data (sample) but do not infer the properties of the population from which the sample was drawn. Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive
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Tobacco and Pollution cause Lung Cancer cases Many people who suffer from Cancer have lived in the environment with lost of toxins around them. There are many sources of such toxins, and some of them are Tobacco and Car emissions. The government has recently tired to prevent people from smoking, In 2009 President Obama came out with an act called Tobacco control Act of 2009 and said, “ The legislation takes measures to prohibit tobacco companies from marketing to children such as making candy
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like a negative correlation. When we visited the EHMNNWR this year, we observed a noticeable difference from the previous years; there were less deer fecal pellet groups. The lowest mean pellet count occured when there was a gust wind speed of 33.6 km per hour. However, when looking at Figure 2, there were more pellet groups when the gust wind speed was the lowest, 30.4 kph, including the outlier of 658. Using the data collected, we conducted an ANOVA and two Pearson R Correlation Coefficient tests
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Keller Graduate School of Management Applied Managerial Statistics (GM533) Course Project Case Study: Grocery Bags data gathered and written by ME Applied Managerial Statistics GM533 Instructor: XX I. Executive Summary For this research I decided to develop my own case study and collect all the data myself. The data file named “Grocery Bag Study” (separate attachment), contains observations on 33 sample groups with a variation of 8 different characteristics (see table below). These
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are adventurous, risk takers, and daredevils and all of them have tattoos. I have a strong feeling that the correlation between the two will have a strong positive relationship. X= Tattoos Y= Thrill Seeking The scatter plot shows an extremely rough linear pattern but there is an upward sloping. Line of best fit: y = 0.9148x +25.505 Analysis: 1. r = .14 little or no correlation 2. R^2 = 2% 2% of the variance in thrill seeking is accounted by tattoos. 3. Slope = 0.0196(m)
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