a. Determine the correlation coefficient between the number of periods and the sales. Is time series a good method of predicting the monthly sales for year 4?b. Determine the correlation coefficient between the sales and the number of tourists in the area. Is a linear regression a good method to predict the sales for a month? 2.In a mountainous tourist destination, a lady sells slippers. Her assistant recorded monthly sales.She then checked the record in the Tourist Community Center, which the record shows on thetable below:Year 1MonthJanFebMarAprMayJunJulAugSepOctNovDecSales525436458485467402388364351348412501(pairs ofslippers)No. of12539571116132011019638127987647458121201touristsYear 2MonthJanFebMarAprMayJunJulAugSepOctNovDecSales533443469492483410392374371358426517(pairs ofslippers)No. of126310081122133411119738248127837878331215tourists Year 3MonthJanFebMarAprMayJunJulAugSepOctNovDecSales548468482498495422401386387378436531(pairs ofslippers)No. of127910211136133211219848228117947858421219tourists

. Determine the correlation coefficient between the number of periods and the sales. Is time series a good method of predicting the monthly sales for year 4? b. Determine the correlation coefficient between the sales and the number of tourists in the area. Is a linear regression a good method to predict the sales for a month?

. Determine the correlation coefficient between the number of periods and the sales. Is time series a good method of predicting the monthly sales for year 4? b. Determine the correlation coefficient between the sales and the number of tourists in the area. Is a linear regression a good method to predict the sales for a month?

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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The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers): A.What would be the typical linear regression equation for the number of sales? B.Make the sales forecast for period 15 of next year. C. Make the sales forecast for period 17 of next year. D. What is the standard error for the data?
The equation of a regression line, unlike the correlation, depends on the units we use to measure the explanatory and response variables. Here is the data on percent body fat and preferred amount of salt. Preferred amountof salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 20 31 22 29 39 22 30 In calculating the preferred amount of salt, the weight of the salt was in milligrams. (a) Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in milligrams. (Round your answers to one decimal place.) ŷ = ? + ? x (b) A mad scientist decides to measure weight in tenths of milligrams. The same data in these units are as follows. Preferred amountof salt x 2 3 4 5 6 8 11 Percent body fat y 20 31 22 29 39 22 30 Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in tenths of milligrams. (Round your intercept to one decimal place and your slope…
Suppose that you are interested in the relationship between Reading and Writing scores. (c) Compute and interpret the R2 value for the relationship between Reading and Writing scores. (d) Interpret the value of the slope in the regression model predicting Reading scores from Writing scores. RDG WRTG 34 44 47 36 42 59 39 41 36 49 50 46 63 65 44 52 47 41 44 50 50 62 44 41 47 40 42 59 42 41 47 41 60 65 39 49 57 54 73 65 47 46 39 44 35 39 39 41 48 49 31 41 52 63 47 54 36 44 47 44 34 46 52 57 42 40 37 44 44 33 71 58 47 46 42 33 42 36 47 41 52 54 52 54 39 39 31 41 60 54 47 31 39 28 42 36 47 57 42 49 36 41 50 33 34 34 55 55 28 46 42 42 44 44 60 52 39 57 36 37 44 44 39 42 39 34 42 39 42 44 52 54 65 65 42 26 47 62 52 62 44 54 52 54 47 44 65 57 63 65 60 59 47 44 65 57 53 61 68 63 55 56 39 53 52 52 65 67 52 44 57 67 63…
am. 13.
Can you please check my work
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city.
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5. Explain what the correlation coefficient tells you about the relationship between your variables. 7. Interpret the slope of the regression equation in terms of the variables in your data set.
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