X15.52.58332.98946.55.55653.5X2315567503871305642736044503955X310812781212585nn6mm1010X4869161517810416712644Y79.3200.1163.2200.1146177.730.9291.9160339.4159.686.3237.5107.2155 To answer item 17 to 23, use MS Excel data analysisConsider the following data set, in which a maker of asphalt shingles isinterested in the relationship between sales for a particular year andfactors that influence sales.X1 - promotional accounts ; X3 - Competing brandsX2 - Active accounts; X4 - Potential and Y - sales in thousands of $The correlation between sales andpotentialO negligible positiveO no correlationO low positiveO very high positiveO moderate positiveO high positive

The correlation coefficient is used to determine the strength of the association between the…

Answered: To answer item 17 to 23, use MS Excel…

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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Each year forbes ranks the world’s most valuable brands. A portion of the data for 82 ofthe brands in the 2013 forbes list is shown in Table 2.12 (forbes website, february, 2014).The data set includes the following variables:brand: The name of the brand.Industry: The type of industry associated with the brand, labeled Automotive& Luxury, Consumer Packaged Goods, financial Services, Other, Technology.brand Value ($ billions): A measure of the brand’s value in billions of dollarsdeveloped by forbes based on a variety of financial information about the brand.1-Yr Value Change (%): The percentage change in the value of the brand over theprevious year.brand Revenue ($ billions): The total revenue in billions of dollars for the brand.a. Prepare a crosstabulation of the data on Industry (rows) and brand Value ($ billions).Use classes of 0–10, 10–20, 20–30, 30–40, 40–50, and 50–60 for brand Value($ billions).b. Prepare a frequency distribution for the data on Industry.
Jim and Larry run a soup stand. The data below represents the sales of soup that they had on 8 randomly selected days, along with the high temperature on that day. a) Create a scatter diagram for this data set and comment on whether there is a positive or negative relationship between these variables.b) Calculate the correlation coefficient between temperature and sales.c) Does a linear relationship exist between these variables? Explain to me why or why not this is the case.d) Using excel, calculate the least squares regression equation for this data set. e) Using your equation, predict the soup sales on a day where the temperature is 29 degrees.f) Interpret the slope of this equation.g) Can we use this data set and regression equation to predict the sales on a day when the average temperature is 102 degrees? Why or why not?h) What is the R-squared for this equation? How do you interpret that R-squared?
The data below was collected from manufacturer advertisements of their vehicles horsepower (x) and highway gas mileage (mpg=y). Use this data to answer the following questions. horsepower 146 250 340 350 390 190 220 mpg 33 28 15 17 11 35 42 1. Find the p-value to determine if there is a linear correlation between horsepower and highway gas mileage (mpg). Record the p-value below. Round to four decimal places.p-value==2. Is there a linear correlation between horsepower and highway gas mileage (mpg)? 3. If there is a linear correlation, write the correlation coefficient below. Otherwise, leave it blank. Round your final answer to four decimal places. Be careful with your sign.r=r= 4. If there is a linear correlation, write the regression equation below. Otherwise, leave it blank. Round all numbers to four decimal places.ˆy=y^=5. Using the data shown above, predict the the highway gas mileage (mpg) for a car that has a horsepower of 225. Round your final answer to two decimal…
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Given the scatter plot below, which of the following describes the strength and direction of the data? 10 Weak negative correlation Strong negative correlation Strong positive correlation Weak positive correlation
Susan, a personal trainer, was interested in whether or not there was a linear relationship between the number of visits her clients made to the gym each week and the average amount of time her clients exercised per visit. She took the following data. Client 1 2 3 4 5 6 Number of visits per week 1 3 4 2 3 5 Average time spent exercising per visit (hours) 2 1.5 1 2 2 0.30 Using the best fit line, estimate the average time spent exercising per visit for 4 visits per week. Group of answer choices 1.03 hours 1 hour 10.3 hours 2 hours

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