na080825. Calculate the coefficient of correlation using the method of concurrent deviationbětween supply and demand of an item for a period of 10 years as given below :2012Year2010201120132014201520162017Supply125160164174155175Demand115125192190165174124127152169

Answered: Image /qna-images/answer/bb52f2bd-5de4-40d3-995b-7bd3c025cb4f.jpg

Answered: 08 25. Calculate the coefficient of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Report the correlations between the three independent variables (age, educ and Protestant) and your dependent variable (childs). Which category had the correlation that was the weakest?
14. The following table gives the miles per gallon and the load capacity for 6 SUV's. Miles per Gallons Load Сарacity 19 860 17 970 16 1035 16 1165 14 1180 13 1360 a. Develop a scatter diagram for the data. b. How would you characterize the relationship between miles per gallon and load capacity?
The weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use a =0.01. Welght, x Varlablity in braking distance, y 5960 5390 6500 5100 5870 4800 2 1.75 1.95 1.93 1.57 1.64 1.50 E Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test. Ho: P Vo Hạ: p Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) O A. The critical values are -to = and to = O B. The critical value is. Calculate the test statistic. t= (Round to three decimal places as needed.) What is your conclusion? a significant linear correlation between vehicle weight and variability in braking distance on a dry surface. There Venough evidence…
The weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use α=0.01. Weight, x 5920 5370 6500 5100 5890 4800 Variability in 1.76 1.91 1.50 braking distance, y EEE Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test. Ho: P Ha: P 0 0 1.86 1.62 1.67 M
5. Oxygen demand is a term blologists use to describe the axygen needed by fish and other aquatic organisms for survval. The Environmental Protection Agency conducted a study of a wetland area in Marin County, Californla. In this wetland environment, the mean oxygen demand was u=9.9 mg/L, and a= 1.7 mg/L. (Reference: EPA Report 832-R- 93- 005). Let x be a random varlable that represents oxygen demand in this wetland environment. Assume x has a probability distribution that is approximately normal. a. An oxygen demand below 8 mg/L Indicates that some organisms In the wetland environment may be dying. What is the probability that the oxygen demand will fall below 8 mg/L? b. A high oxygen demand can also indicate trouble. An oxygen demand above 12 mg/L may indicate an overabundance of organisms that endanger some types of plant life. What is the probability that the oxygen demand will exceed 12 mg/L?
A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a. For each 1-cm increase in height, weight tends to increase by about 0.32 kg b. There is no association between weight and height c. Height accounts for about 32% of the total variability in weight d. The correlation between weight and height is about 0.32
In a random sample of sixteen different industries the percentage employment x in STEM (science, technology, engineering, mathematics) and mean annual compensation y(in thousands of dollars) were recorded, with the following results. The scatter diagram showed a linear trend. SSxx = 1303.9 SSy= 1503.0 SSy = 2660.2 Ex = 150 .1 Ey = 915 n= 16 %3D %3D 1sxs 34 36.7 s y s 90.5 Calculate the linear correlation coefficient for percent employment in STEM and annual compensation and enter your answer below accurate to two decimal places.
1945 is paired with cancer rates for 1975. The cigarette consumption data is from 1945 to 1975 in 5-year increments. There is a strong positive linear association between annual cigarette consumption and lung cancer rates. The correlation is 0.81. If we use the line to make predictions of the number of lung cancer deaths per 100,000 people, which of the following is an example of extrapolation? a. Predict Y when cigarette consumption is 3,500 cigarettes per person. b. Predict Y when cigarette consumption is 3,800 cigarettes per person. c. Predict Y when cigarette consumption is 4,600 cigarettes per person.
A 7. The analyst in the Dorben Company made 10 independent time studies in the hand paint- spraying section of the finishing department. The product line under study revealed a direct relation between spraying time and product surface area. The following data were collected. Study No. 1 2 3 4 5 6 8 9 10 Surface Area 170 12 150 41 130 50 70 105 95 (sq. inches) Standard Time (hours) 0.32 0.11 0.31 0.14 0.27 0.18 7 120 0.24 0.23 0.25 (7a) Compute the slope and intercept constant using regression line equations. (7b) What is the correlation coefficient? (7c) How much spray time would you allow for a new part with a surface area of 250 sq. inches? 0.22
Can you please check my work
5. Which of the following is the assumption that the best way to describe the pattern of data is using a straight line? homoscedasticity normality linearity restriction of range
From the graphs displayed, is there evidence to suggest that there is a positive linear relationship between Square Feet and Bathrooms, for the population of real estate represented by this sample? Be sure to provide numeric support in your answer.

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