...contains the data between the years 1974 and 1992. Graph 2 Contains the data between the years 1992 and 2012. Graph 1 Filtered This scatter plot shows the amount of beer sold per capita between the years 1975 and 1992. The Independent variable “Year” is graphed on the x-axis and represents the predictor. The scatter plot has a negative, non-linear relationship. It has a strong relationship, with constant scatter. There seems to be a trend of beer sales declining per capita at a rate of 1.23 litres per year. Graph 2 Filtered: This scatter plot shows the amount of beer sold per capita between the years 1992 and 2012. The Independent Variable “Year” represents the predictor and is graphed on the x-axis. The Dependent Variable “Beer Sales Per Capita” is graphed on the y-axis and represents the response. The scatter plot has a non-linear trend, with a negative association that shows as one variable gets smaller, so does the other. It has a weak relationship with constant scatter. This graph shows that there is a trend of Beer Sales Per Capita declining at a rate of 0.31 litres per year. Part 2: The independent variable is “Year” because it does not rely on any other data and the dependent variable is the population aged 20-24 as a percent because it relies on the year. The relationship shown in this scatter plot is a strong, linear correlation...
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...The student is asked to graphically analyze the price data. Astute readers will understand that dividends are not the only element at work on the firm's stock price. The case highlights the difficulty of determining an optimal policy since other factors cannot be held constant while dividends are manipulated in the current point in time, and that even if that were possible, the variety of opinions and hypotheses surrounding dividend policy does not indicate any concrete conclusion as to what is an ideal policy. 1. Enter the data from tables 1 and 2 into a spreadsheet program. Graph a scatter plot of the dividend with the stock price. Does there appear to be a correlation? 2. Plot the change in price (from t-1 to t+1) with the dividend amount. Does there appear to be a correlation? Plot the change in price with the year to year change in the dividend. Does there appear to be a correlation? 3. Summarize the implications of each dividend hypothesis/theory found in your financial management textbooks. Which one explains what is going on with Haveloche? 4. What would you suggest to Phil concerning what type of dividend policy to pursue? Justify your...
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...one uses scatter plots, and data analysis, one can determine if a correlation exists between two data sets, or if there is actually very little. This can help when it comes to seeing for example, if job satisfaction overall is related to benefits, and if so how to change that in the favor of the business. Introduction In the following information, we will show regression outputs for data sets from the AIU data set. We will determine correlation and what it means, as well as show scatter graphs that can help determine if there is any correlation to be shown. One has to be careful to input the proper data if they want the analysis to come out correctly. Benefits and Intrinsic Job Satisfaction Regression output from Excel |SUMMARY OUTPUT | | | | | |Intrinsic |0.326704508 |3.438142011 |Y=0.0034x+4.5491 |0.0012 | |Extrinsic |-0.134516538 |6.034361553 |Y=1.6912x+13.859 |0.2275 | |Overall |0.101037811 |4.712869316 |Y=1.0105x+0.5195 |0.1021 | Similarities and Differences In all three graphs the r2 output is nearly the same. The difference is that all three graphs have extremely different scatter plot patterns. Correlation coefficients ...
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...times where those genius ideas are shelved because no one has an interest in that patent. The ever changing cash flows prove to be difficult for decision making, especially when it comes to whether the company should give back to its investors or not. Haveloche is constantly faced with the predicament of deciding what dividend policy is best for the organization and the investors. The company’s CEO listed the stock prices and dividends for us to take a look at, so let us do just that. Below are the two scatter plots created from the information given in the case. The first scatter plot charts the dividend and the stock price. As you can see from the scatter plot, there is no obvious correlation between the two. The dividend does not necessarily move in the same direction or the opposite direction of the stock price. The second scatter plot charts the change in the stock price with the dividend. As you can also see with this scatter plot, there is no real correlation between these two. There are 3 theories of investor preference for dividend versus capital gains: (1) Dividend Irrelevance Theory or Modigliani Miller (2) “Bird-in-the-hand” Theory (3) Tax Preference Theory. According to Modigliani Miller (MM), the dividend policy has not effect on the stock price of the firm or the cost of capital. This theory states that investors reinvest the dividends back into the firm and the firm’s value is only based on the income produced from its assets, and not the dividends...
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...world of statistics from a business perspective this week, allowing you to practice your skills with descriptive statistics, formatting, graphs, and regression analysis. As discussed in the lesson, the value of statistics lies in the ability to analyze data more effectively for the purpose of improving decision making. You might have heard the expression that “statistics never lie, and only liars use statistics”. There is an obvious truth in this statement, in that, depending on the questions being asked and the data collected, the statistics can skew reality. For example, it is true that as ice cream sales increase, accidents at swimming pools increase. Does this mean that the more ice cream that is sold, the more accidents it causes (correlation/causation)? Of course not, but the data, if not interpreted correctly, could lead to false conclusions. It just so happens that both are correlated to a rise in temperature in the summertime. The hotter it is outside, the more kids flock to swimming pools, leading to more accidents, and the more ice cream is sold. So you see, although statistics are vital in the world of decision making, you have to be wise, and ask the right questions. Software Citation Requirements This course uses open-source software, which must be cited when used for any student work. Citation requirements are on the Open Source Applications page. Please review the installation instruction files to complete your assignment. Deliverables NOTE Submit your assignment...
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...Maurice S. Butler Math533—Applied Managerial Statistics Course Project: Part A Introduction This project is based upon statistical data compiled concerning AJ Davis Department Stores, specific to a sample of its customer base. It is with intent of establishing relationship between location, gross income, and credit balances carried by customers that the following statistical analysis has been performed. It is assumed that information obtained as well as the interpretation of statistical analysis will enable credible recommendations in regard to future revenues or continued handling and/or maintenance of its receivables. Variables The first variable is the gross income of the stores’ customers. The data set includes 50 customers with gross income ranging from $20,000 to $79,000 per year. Compilation of the data into a frequency/relative frequency table (see below) reveals that the greatest frequency and relative frequency of the store’s customers is found within the $30,000 to $49,000 range. Fifty-two percent of the store’s customer base gross income is found within this range. First and third quartiles have been calculated to be 33 and 57 respectfully. However, no outliers have been identified within the data set. Income ($1000) | Frequency | Relative Frequency | 20-29 | 5 | 10% | 30-39 | 13 | 26% | 40-49 | 13 | 26% | 50-59 | 8 | 16% | 60-69 | 9 | 18% | 70-79 | 2 | 4% | | 50 | 100% | My second variable is the outstanding credit balances of...
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...example, a circle of radius 2 may be described as the set of all points whose coordinates x and y satisfy the equation x2 + y2 = 4. Question: Can one line have two slopes? Explain how or why not. If the line is a straight line, meaning 180degrees, it can only have one slope. If it is a function (f(x) = or y=) then the line may have more than one, one, or an undefined slope. Find the first differential of the function and plug in your given x value to find the slope at any given point. Question: What is the difference between a scatter plot and a line graph? Provide an example of each. Does one seem better than the other? In what ways is it better? They generally refer to the same things, although some terminology may sound better with either one. For example, plot and graph, both represent data, however box plots or scatter plots or line graphs are generally referred to as such as opposed to saying box graphs, scatter graphs or line plots. The latter...
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...distributed data. The highest value was at the bin rage of 12.1 with the lowest value on the -5.9. Five year return The five year return on the other hand revealed a bell-shape histogram showing that the data was normally distributed. The highest value was at the bin range of 12.4 with the lowest being at the -11.6 bin range. Contingency table The large cap revealed a total of 450 with 251 being on the “No” and 199 on the “yes”. On the mid cap, the total value was equivalent to 174 with the “no” having a value of 97 while the “yes” had a value of 77. The small cap had a total value of 244 with “no” having 152 and the “yes” having a value of 92. This information is represented in the contingency table below. Assets scatter plot The scatter plot to show the relationship between the assets and the expense ratio showed that most of the points were scattered on the right. These points were between 0 and 20000. A line drawn on the graph revealed that there was negative...
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...downloadable Excel file with the data can be found at this link: http://www.farmdoc.illinois.edu/manage/uspricehistory/us_price_history.html. The link for the price data is at the bottom of the purple area of the tool. 1. Produce a line plot of the entire data series on one page. Do your best to format the chart in a useful manner. 2. Produce a scatter plot where x is the previous month hog price and y is the current month hog price. Note you will lose one observation when you construct the series for this plot. In other words, generate a second price column which is the original data lagged by one month, e.g. row #1 Feb 60 Jan 60; row #2 Mar 60 Feb 60, and so on. Show the regression of y on x on the chart along with the equation and R2. 3. Generate the monthly change in hog prices. Simply subtract last month’s price from this month’s price. 4. Produce a scatter plot where x is the previous change in the monthly hog price and y is the current change in the monthly hog price. Note you will lose two observations when you construct the series for this plot. Show the regression of y on x on the chart along with the equation and R2. 5. Discussion: a. What does the plot in #2 suggest about the predictability of monthly hog prices? b. What does the plot in #4 suggest about the predictability of monthly hog prices? c. How can the two predictability results be reconciled? Do some digging on the random walk model. One well written...
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...Delta Song Case Analysis Possible cost drivers that will allow us to estimate a salary cost function for Delta are: available seat miles, number of departures, available ton miles, revenue passenger miles, and revenue ton miles. The two cost drivers we chose were revenue passenger miles and available ton miles. The salaries consist of payments to pilots, flight attendants and ticket agents. Their salaries are determined by the number of passengers and cargoes and the miles or hours flown. This is why we chose revenue passenger miles and available ton miles. After calculation we found that the R2 of revenue passenger miles is .1764, and the R2 of available ton miles is .5577. We used scatter plots to show this: The available ton miles scatter plot shows a more linear relationship between the two variables. Low point (3132, 1145), high point (4029, 1514) Salary=0.4114xavailable ton miles-143.50 The greatest advantage about this technique is that it only uses two data so it is convenient. The disadvantages are that the data is inefficient. This is because the data is based on cost function for only two periods, meaning it is less accurate. Simple Regression Using simpler regression to estimate the salary cost with available ton miles as the cost driver. These are the results: Coefficients Intercept X Variable 1 -682.643 0.551693 Standard deviation 282.6033 0.79698 Salary= 0.5517x available ton miles- 682.63 R2=0.5577, and the coefficients are larger than the deviations...
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...Solutions to Case Problems Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations Case Problem 1: Pelican Stores 1. There were 70 Promotional customers and 30 Regular customers. Because there are 100 observations in the sample, the frequency and percent frequency distribution are the same. Percent frequency distributions for many of the variables are given. No. of Items | Percent Frequency | 1 | 29 | 2 | 27 | 3 | 10 | 4 | 10 | 5 | 9 | 6 | 7 | 7 or more | 8 | Total: | 100 | Net Sales | Percent Frequency | 0.00 - 24.99 | 9 | 25.00 - 49.99 | 30 | 50.00 - 74.99 | 25 | 75.00 - 99.99 | 10 | 100.00 - 124.99 | 12 | 125...
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...downloadable Excel file with the data can be found at this link: http://www.farmdoc.illinois.edu/manage/uspricehistory/us_price_history.html. The link for the price data is at the bottom of the purple area of the tool. 1. Produce a line plot of the entire data series on one page. Do your best to format the chart in a useful manner. 2. Produce a scatter plot where x is the previous month hog price and y is the current month hog price. Note you will lose one observation when you construct the series for this plot. In other words, generate a second price column which is the original data lagged by one month, e.g. row #1 Feb 60 Jan 60; row #2 Mar 60 Feb 60, and so on. Show the regression of y on x on the chart along with the equation and R2. 3. Generate the monthly change in hog prices. Simply subtract last month’s price from this month’s price. 4. Produce a scatter plot where x is the previous change in the monthly hog price and y is the current change in the monthly hog price. Note you will lose two observations when you construct the series for this plot. Show the regression of y on x on the chart along with the equation and R2. 5. Discussion: a. What does the plot in #2 suggest about the predictability of monthly hog prices? b. What does the plot in #4 suggest about the predictability of monthly hog prices? c. How can the two predictability results be reconciled? Do some digging on the random walk model. One well written...
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...and subtle changes over time must be highlighted. This presentation will show, by example, how such graphs can easily be created using the SAS/GRAPH® SG procedures. The techniques that will be emphasized in this presentation include: • • • • • • Creation of a dose response plot by overlaying multiple plots in one graph Construction of a hematology panel using treatment regimen and visit numbers as the classification variables Presentation of a matrix of liver function tests (LFTs) for at-risk patients Aggregation of data into on-the-fly classification variables using user-defined formats Getting the axis you want using built-in best fit algorithms Generation of publication-ready graphs in color and in black and white INTRODUCTION The new SAS/GRAPH procedures—SGPLOT, SGPANEL, and SGSCATTER—provide new tools for viewing and reporting data collected during clinical trials. The SG procedures are an extension of the ODS Graphics framework, providing access to the Graph Template Language (GTL) in the familiar syntax of the SAS/GRAPH procedure. The concept behind the SG procedures is simple in theory, yet powerful in execution. Each GRAPH contains one or more CELLS, and each CELL contains one or more overlaid PLOTS. Both the GRAPH and CELL can contain supporting elements such as titles and...
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...graph that better illustrates the relationship between the number of registered boats and the number of manatee deaths from boats? Yes, a scatterplot (or scatter diagram) is a graph in which the paired (x, y) sample data are plotted with a horizontal x-axis and a vertical y-axis. Each individual (x, y) pair is plotted as a single point. The scatterplot does a much better job of visually depicting the association between registered boats and manatee deaths than the side-by-side bar charts. When you look at a scatterplot, you should study the pattern and take note of the direction of the pattern. In the example shown below, there does appear to be a pattern: an increase in registered boats appears to be associated with an increase in manatee deaths by boats. 2. How can methods of statistics be used to objectively determine whether there is a relationship between two variables, such as the number of registered boats and the numbers of manatees killed by boats? In statistics, correlation is used to analyze a collection of paired sample data and determine whether there appears to be a relationship between the two variables. Because visual examinations of scatterplots are largely subjective, more precise and objective measures are needed. The use of the linear correlation coefficient r (represents the linear correlation coefficient for a sample) is useful for detecting straight-line patterns. The formula for computing r is: 3. If there is a relationship between the numbers...
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...SIMPLE LINEAR REGRESSION EXAMPLE Butler’s Trucking Company is an independent trucking Company in southern California. A major portion of Butler’s business involves deliveries throughout its local area. To develop better work schedules, the managers want to estimate the total daily travel time for their drivers. Initially the managers believed that the total daily travel time would be closely related to the number of miles traveled in making the daily deliveries. A simple random sample of 10 driving assignments is provided in Table 1. Use Excel to make a scatter diagram of these deliveries (to verify that a linear relationship does exist) and develop a regression equation expressing this relationship. |Table 1 | | | | | |Driving Assignment |X1=Miles Traveled |Y=Travel Time (hrs.) | | |1 |100 |9.3 | | |2 |50 |4.8 | | |3 |100 |8.9 | | |4 |100 |6.5 | | |5 ...
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