A local store kept track of the number of advertisements it placed in local newspapers and the number of stereo systemsit sold each week. Week 1 2 3 4 5 6 7 8 Advertisements, x 7 5 4 2 8 5 5 3 Stereos Sold, y 23 15 12 8 2 7 9 6 a) Create ascatter plot for the data showing Advertisements Vs. Stereos Sold. b) Determine the value of r, the correlation coefficientfor the set of data and classify the type of correlation. c) What does this correlation coefficient suggest about theeffectiveness of the advertisements? Explain

The data on the number of advertisements placed in local newspapers and the number of stereo systems…

Answered: A local store kept track of the number…

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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Listed below are the data for a local television station. This shows the number of weekly advertisements and the projected weekly sales resulting (adjusted by industry). Weekly Radio Spots Sales ($100s) 4 75 10 85 2 62 6 81 8 81 2 57 10 95 6 72 8 89 4 69 a. Show the data in a scatterplot, to demonstrate the relationship between the two variables. b. Compute and interpret the correlation coefficient.
1. A medical student is studying heart rate. The student wants to know if there relationship between age and heart rate. She collects the following data from children selected at random. Heart rate Age (yrs) 92 89 90 84 79 102 108 12 86 14 a) Make a scatter plot of the data. b) Describe the relationship(positive, negative and none) you see between heart rate and age and determine the type of correlation (linear or non-linear) that exists. Relationship Туре» c) Draw a line of best fit. d) What do you think is the heart rate of a newborn baby? Justify your answer. e) What do you think is the heart rate of someone who is 20 years old? What type of prediction is this?
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?
3. The speed (in miles per hour) of 8 vehicles and their braking distance (in feet) when stopping on a dry surface speed, x braking distance, y 20 30 40 50 50 60 65 70 55 72 75 110 138 162 193 225 type of correlation: Conclusion
Use the data in the table, which shows the average annual salaries (both in thousands of dollars) for public school principals and teachers in the U.S. for 11 years. Apply what you have learned in the previous tab to complete this graded lab questions. 1. Repeat the steps from tab #1 using this data to construct a scatter plot graph and calculate the correlation coefficient R. (Recall the corelation coefficient is R and not R Teachers y Principalsx 77.8 43.7 78.4 43.8 80.8 45.0 R= 80.5 45.6, 2. Does the R value represent a positive, negative or no correlation between the salaries ol the teachers and principals? As teacher salaries increase, what happens to prinicipal salaries? 10 11 81.5 45.9 84.8 48.2 12 13 87.7 49,3 3. Write the equation of the regression line for the data below. The equation is y= 14 91.6 51.3 52.9 Teachers y 15 16 95.7 54.4 COD 54.2 =D0.5873x-2.0s07o R=0.995, 17 95.7 4. Use the regression equation that you found in exercise #3 to predict the average annual salary…
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A nutritionist collects data from 25 popular breakfast cereals. For each cereal, the number of calories per serving is plotted on the x-axis against the number of milligrams of sodium on the y-axis. The value of r for the resulting scatterplot is 0.83. How would the value of the correlation coefficient, r, change if sodium was plotted on the x-axis and calories plotted on the y-axis? The value of r would increase. The value of r would not change. The value of r would change to –0.83. The value of r could increase or decrease, depending of the strength of the new relationship.
Write a report using Word. Cover the following topics. This file contains some information about different cars. Car Weight (pounds), x Miles per Gallon, y A 2750 31.5 B 3120 27.9 C 3385 23.7 D 3740 23.3 E 4225 21 Create a scatterplot for the data in the Weight and Braking columns. Paste it here. a) Using StatCrunch or TI 83/84 to calculate the linear correlation between the data in the Weight and MPG columns. b) Explain the mathematical relationship between Weight and MPG based on the linear correlation coefficient. Be certain to include comments about the magnitude and the direction of the correlation. c) Write the equation for the least-squares regression line if there is one. d) Predict the miles per gallon of car C and compute the residual.
After gathering data about the number of starfish and measuring the pollution in areas of the ocean you find a negative linear correlation between pollution levels and number of starfish. What can you conclude based on this information? a. There is a confounding variable that is affecting both pollution and starfish. b. As pollution rises the number of starfish falls c. That pollution is causing starfish to die, leading to the negative correlation d. That pollution is supporting starfish, leading to the negative correlation
A study was made by a retail merchant to determine the relation between weekly advertising expenditures ($x) and sales ($y). The following data were recorded. Advertisement 40 20 25 20 30 Sales 385 400 395 365 475 (i) construct scatter plot between sales and advertisement expenditures. (ii) Compute the correlation coefficient between sales and advertisement expenditures. (iii) Interpret the correlation coefficient between sales and advertisement expenditures. (iv) Compute the coefficient of determination between sales and advertisement expenditures. (v) Interpret, in context, the coefficient of determination.

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