Question 1) Suppose you are interested in studying the relationship between the temperature of a particular day and the number of customers that a large ice cream shop serves on that day. To do this you randomly choose 11 days to visit the shop and record both the high temperature of that day and how many customers it served for the entire day. You get the following data: High Temperature (in ° F) 75 83 92 77 92 86 73 96 85 88 80 Number of Customers 430 480 600 400 510 520 370 590 590 520 430 (a) Draw a scatterplot for this data. Make sure to label your axes. (b) Calculate the correlation coefficient, and decide if there is enough evidence for a correlation. (c) Construct the LSR for this data. Keep one decimal place for the slope and round the intercept to the nearest whole number. (d) Graph the LSR on your scatterplot. (e) Write a sentence interpreting the slope of the LSR. (f) How many customers does your model predict will show up on day where the high temperature is 50° F? Would you trust this prediction? (g) How many customers does your model predict will show up on day where the high temperature is 90° F? Would you trust this prediction? (h) Construct the residual plot for this data. Is a linear model appropriate? Why or why not?
Question 1) Suppose you are interested in studying the relationship between the temperature of a particular day and the number of customers that a large ice cream shop serves on that day. To do this you randomly choose 11 days to visit the shop and record both the high temperature of that day and how many customers it served for the entire day. You get the following data: High Temperature (in ° F) 75 83 92 77 92 86 73 96 85 88 80 Number of Customers 430 480 600 400 510 520 370 590 590 520 430 (a) Draw a scatterplot for this data. Make sure to label your axes. (b) Calculate the correlation coefficient, and decide if there is enough evidence for a correlation. (c) Construct the LSR for this data. Keep one decimal place for the slope and round the intercept to the nearest whole number. (d) Graph the LSR on your scatterplot. (e) Write a sentence interpreting the slope of the LSR. (f) How many customers does your model predict will show up on day where the high temperature is 50° F? Would you trust this prediction? (g) How many customers does your model predict will show up on day where the high temperature is 90° F? Would you trust this prediction? (h) Construct the residual plot for this data. Is a linear model appropriate? Why or why not?
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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Step 1: Write the given information.
VIEWStep 2: Construct the scatter plot for the given data set.
VIEWStep 3: Determine the correlation coefficient for the given data set.
VIEWStep 4: Perform the hypothesis test for the significance of the correlation coefficient.
VIEWStep 5: Determine the least square estimator regression line for the given data.
VIEWStep 6: Graph the least square estimator regression line on the scatterplot and interpret the slope.
VIEWStep 7: Predict the number of costumers using our model for the given high temperature.
VIEWStep 8: Determine the residuals and construct the residual plot.
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