The following data shows the increase in Halloween candy sales over a 7-year period.Halloween Candy Sales (in million of dollars)Year1995 1998 1997Candy Sales 1.474 1.66 1.708a. Describe the association between candy sales and year.2type your answer...b. Write the regression equation for predicting candy sales from year.candy = type your answer...GAc. Explain the meaning of the slope.type your answer...10d. Predict the amount of sales for 2004type your answer...WSX#mdyearmillion dollarsजी पं11do in%5V9y7h1998 1999 2000 20011.787 1.896 1.985 2.035*00mK

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Answered: The following data shows the increase…

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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Real-life Problem Apply the concept of Regression Analysis, present your ANSWER with COMPLETE SOLUTION to this problem. The following are the number of hours that 11 students spent studying for a midterm examination and the grades they obtained in Mathematics. a. Construct a scatter plot diagram. For th le b. Describe the association or correlation between the variables. Number of Hours Studied (x) Midterm Grades 6 60 11 79 20 90 19 85 20 92 10 80 12 82 8 65 15 85 18 94 10 70
Regression and correlation analysis both describe the strength of linear relationships between variables. Consider the concepts of education and income. Many people would say these two variables are related in a linear fashion. As education increases, income usually increases (although not necessarily at the same rate). Can you think of two variables that are related in such a way that their relationship changes over their range of possible values (i.e., in a curvilinear fashion)? How would you analyze the relationship between two such variables?
The values of advertisement expense (X) and sales (Y) are shown in the table below. Find the regression line. Estimate the value of sales when advertisement expense is 20.
Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 20 minutes. How does the result compare to the actual tip amount of $4.55? Distance 0.68 2.47 12.71 1.80 1.02 8.51 1.32 1.40 6.00 18.00 27.00 25.00 8.00 31.00 8.00 18.00 6.30 14.30 36.80 16.30 7.80 31.75 7.80 12.30 1.89 4.29 0.00 1.50 2.34 2.98 0.00 2.46 Time Fare Tip The regression equation is y=+ X (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.) The best predicted tip for a ride that takes 20 minutes is $ (Round to the nearest cent as needed.) How does the result compare to the actual tip amount of $4.55? O A. The best predicted value is close to the actual tip of $4.55. O B. The best predicted value…
onsider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume(units) Total Cost($) 400 3,900 450 4,900 550 5,500 600 5,900 700 6,400 750 7,100 This data was used to develop an estimated regression equation, ŷ = 970.67 + 8.08x, relating production volume and cost for a particular manufacturing operation. Use ? = 0.05 to test whether the production volume is significantly related to the total cost. (Use the F test.) Set up the ANOVA table. (Round your p-value to three decimal places and all other values to two decimal places.) Sourceof Variation Sumof Squares Degreesof Freedom MeanSquare F p-value Regression Error Total Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = What is your conclusion? Reject H0. We conclude that the relationship between production…
10) Using advertised prices for used Ford Escorts a linear model for the relationship between a car's age and its price is found. The regression has an R2-87.4%. Write a sentence summarizing what R2 says about this regression. A) The age of the car explains 9.35% of the variation in price. B) The price of the car explains 12.6% of the variation in age. C) The age of the car explains 87.4% of the variation in price. D) The price of the car explains 87.4% of the variation in age. E) The age of the car explains 12.6% of the variation in price. Solve. Round to two decimal places if necessary. 11) At one college, 68% of students have credit cards. A credit card company, looking for new customers, sets up a booth at the college. If they pick students at random, how many students should they expect to talk to before finding one who does not have a credit card? A) 32 B) 3.13 D) 68 C) 0.32 E) 1.47
You generate a scatter plot using Excel. You then have Excel plot the trend line and report the equation and the r value. The regression equation is reported as y = 75.56z + 78.71 and the r = 0.2209. What is the correlation coefficient for this data set?
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
A. run a simple regression- dependent variable is Weeks, independent variable is Age. B. run a multiple regression with dependent variable weeks and independent variable-age, married, head, manager and sales. C. Create the regular and standardized residual plots for both. Please show the tables when entering values of the regression for both the outputs and the scatter plots.
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The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
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