BUSINESS STATISTIC TOPIC Analysis of mass segment car sales in India and deducing the trends in monthly and yearly sales OVERVIEW The net sales of the passenger car segment in India stood at 2,686,429 units in 2013. Though there was a slump in the past due to the economic slowdown, this sector has started recording growth in the recent past. The car segment in India is spread out over various segments ranging from the compact segment to luxury segment. Key segments include compact, hatchback
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Linear Regression Analysis Aarian Charania Professor Habibullah Section: 8am Introduction The independent research company Consumer Research, Inc. conducted research and collected data of annual income and household size, and annual credit card charges. In this report I plan find how much our independent variables (household size and income) affects our dependent variable annual credit card charges. By using descriptive statistics such as mean and standard deviation in order to look at each
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3Q Max -4955.8 -1750.4 -232.4 1464.2 4730.6 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 22142.385 275.018 80.513 < 2e-16 *** TV 12.193 3.874 3.147 0.00219 ** Radio 5.195 2.700 1.924 0.05726 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2138 on 98 degrees of freedom Multiple R-squared: 0.2544, Adjusted R-squared: 0.2391 F-statistic:
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that the definition of the error term is the actual demand minus the forecasted demand. What is the mean deviation of the forecasts in this data sample? 1.1B. What is the mean absolute deviation (MAD) of the forecasts in this data sample? 1.1 C. What is the root mean square error (RMSE) of the forecasts in this data sample? 1.1 D. What is the mean percent error (MPE) of the forecasts in this data sample? 1.2 E. What is the mean absolute percent error (MAPE) of the forecasts
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-17.390 -6.612 -1.009 4.908 25.449 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -28.8768 19.7354 -1.463 0.15540 Acetic 0.3277 4.4598 0.073 0.94198 H2S 3.9118 1.2484 3.133 0.00425 ** Lactic 19.6705 8.6291 2.280 0.03108 * Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 10.13 on 26 degrees of freedom Multiple R-squared: 0.6518, Adjusted R-squared: 0.6116
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Math 1090 Chapter 1.3 Guided Notes Review… 1. Graph the piece-wise function. Identify at least 3 points on the graph with ordered pairs. 2. A city’s main well was recently found to be contaminated with trichloroethylene (a cancer-causing chemical) as a result of an abandoned chemical dump that leached chemicals into the water. A proposal submitted t the city council indicate that
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Call: glm(formula = count ~ persons + child + factor(camper), family = poisson) Deviance Residuals: Min 1Q Median 3Q Max -6.8096 -1.4431 -0.9060 -0.0406 16.1417 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.98183 0.15226 -13.02 <2e-16 *** persons 1.09126 0.03926 27.80 <2e-16 *** child -1.68996 0.08099 -20.87 <2e-16 *** factor(camper)1 0.93094 0.08909 10
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ANALYSIS OF REGRESSION Jessica Cain American InterContinental University Abstract The world today uses statistics in many different ways to understand numbers and possible outcomes. One way that this is by using regression analysis. The regression analysis which is based on a correlation between two variables can help us to better understand the relationship between the two variables. The process which is a valuable one has helped researchers, and businesses to grow based on information
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Print 3.6.4 Test (TS): Bivariate Data Test Probability and Statistics (S2863912) Michael Donaldson Points possible: 50 Date: _2/1/16___________ Answer the following questions using what you've learned from this unit. Write your answers in the space provided. Be sure to show all work. SCATTERPLOTS 1. Maria is a veterinarian. She wants to know if the weight of a puppy depends on its length. To find out, Maria randomly selected 10 puppies that were two months old. She recorded the
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Analysis for Case Jensen Shoes: Lyndon Brooks’ Story Are self-evaluations being hurt? Yes. This case gives us indications that Lyndon had negative core self-evaluations which are explained below: a. Self-esteem: Although not happy with this first assignment he decided to take it up so that it helped him broaden his background and move up in job roles by showing credibility. But he could not complete all his assignments in time. He was later told by Chuck that he would be moved to Strategic
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