The marketing manager of a large supermarket chain would like to determine the effect of shelf space on the sales of pet food. A random sample of 10 stores was selected and the results are presented below:e StoreShelf Space (feet) Weekly Sales (Thousands)51622238144819510246.102671423142729192831019265 1. Draw the scatter diagram. What is the relationship between the variables?2. Draw a trend line and use it to estimate the weekly sales when the shelf space is 20 feet/7 3. Determine the least square regression equation8 4. Using the least square regression equation, compute the weekly sales when the shelf space is 20 feet. Compare it to your answer in #29 5. Predict the weekly sales when the shelf sapce is 21.5 feet? 25 feet?

As per our guidelines, we are allowed to answer first three sub-parts only. Thanks Given Data : X…

Answered: Store Shelf Space (feet) Weekly Sales…

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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what is the answer?
you are going to buy some calves. What should a healthy weight be? let x be the age of the calf (in weeks), and let y be the weight of the calf (in kg) the calves you want to buy are 12 weeks old. Predict the healthy weight you should expect. x=1 3 10 16 26 36 y=42 50 75 100 150 200 state the correlation coefficient and regression line equation.
The price (in Tk.) and demand (in kg) of a product is given in the following table: Price (Tk.) 25 37 40 45 48 50 55 70 Demand (kg) 10 8 9 7.5 4.5 3 2 a. Determine the coefficient of correlation between the two variables. Answer rounded to three decimal places. b. Determine the slope coefficient B1 for the regression of Demand on Price. Answer rounded to three decimal places. c. Determine the y-intercept Bo for the regression of Demand on Price. Answer rounded to two decimal places. d. What will be the demand when price is Tk. 52? Answer rounded to two decimal places. LO
Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative? A. The slope is negative. As the independent variable increases the dependent variable also tends to increase. B. The slope is negative. As the independent variable increases the dependent variable tends to decrease. C. The slope is positive. As the independent variable increases the dependent variable also tends to increase. D. The slope is positive. As the independent variable increases the dependent variable tends to decrease.
Show your work please.
Please help, this is for financial mathematics, thank you so much!!!!
X 33 46 72 105 114 Y 209 228 170 127 109 1) What is slope of the regression line predicting Y from X,rounded to 2 decimal places? 2) What is the intercept of the regression line predicting Y from X, rounded to 2 decimal places? 3) What is the correlation between X and Y, rounded to 2 decimal places?
5. The table below shows varying prices of music sold on a streaming service. Determine the linear regression to estimate the weekly sales of albums sold, use your regression to estimate the weekly sales if the price is $9 per album, and state the correlation coefficient, r, and interpret its meaning in the context of this problem. Price ($) Weekly sales (in thousands) $8 440 $8.50 380 $10 $11.50 250 180
Interpret the intercept and the slope coefficient of the estimated linear trend line equation and comment on the fitness of the model.
Prices and mileage of 48 Dodge 2500 Pickup trucks with Cummins diesel engines. Prices ranged from $28,925 to $68,650. Mileage ranged from 315 to 186,466. While I did not share the trucks were 2013 to 2020 models. Please show all work. Interpret b0 in the context of this problem. Interpret b1 in the context of this problem. What percent of total variation in prices is explained by the regression model? Provide evidence to support your answer. Price Mileage 52293 34450 50987 23372 62988 21738 37500 81176 66000 23118 47601 71143 47750 18214 42000 32303 50987 23372 28925 143737 42050 58561 51738 9691 51500 23969 44988 48781 29500 114809 46975 16918 63999 15507 46975 16918 43222 50459 38386 87192 42000 68722 62250 14042 62998 21738 32130 103644 52293 34450 52900 68883 53172 3656 53494 315 39594 88441 63246 23631 58250 34974 59391 18383 50650 42737 54976 84200 68650 14401 51114 47433 43900 74106 37450…
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
9 students were surveyed to see what their age is and what their income level is. Find the equation of the line using linear regression. We want to predict their age using their income. age 18 24 38 22 19 35 28 19 27 income 456 786 835 855 645 244 587 1400 975 Just a side note, the 19 year old is making 1400, would be considered an outlier since they are making way more than everyone else. (y=-.0061x+34.9874 4 decimals) y=30.3709-.0064

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