A linear model was fit to predict weekly Sales of frozen pizza (in pounds) from theaverage Price ($/unit) charged by a sample of stores in a city in 39 weeks over a three-year period. The average Sales were 63, 200 pounds (SD = 10, 670 pounds), andthe correlation between Price and Sales was -0.529. If the Price in a particular weekwas two SD higher than the mean Price, how much pizza would you predict was soldthat week?

In this case, a linear model was fit to predict the weekly sales of frozen pizza from the average…

Answered: A linear model was fit to predict…

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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Describe the relationship between x and y in a positive correlation and in a negative correlation. In the calculations for the correlation coefficient, what does the sum of products of deviations measure and how is this number used in the calculation of r?
A correlation of 0.95 for two variables X and Y implies that if X increases, there is a 95% chance that Y will also increase. Is this statement true or false?
An economist conducted a study of the possible association between weekly income and weekly grocery expenditures. The particular interest was whether higher income would result in shoppers spending more on groceries. A random sample of shoppers at a local supermarket was obtained, and a questionnaire was administered asking about the weekly income of the shopper’s family and the grocery bill for that week. The correlation was found to be r=0.675. The percentage (to two decimal places) of variation in grocery bills explained by the family's weekly income regression line is:
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
For a sample of 41 California cities, a sociologist studies the crime rate in each city (crimes per 100,000 residents) as a function of its poverty rate (in %) and its median income (in $1,000s). The sociologist finds that SSE = 4,182,663 and SST= 7,732,451. Round all answers to 4 decimal places. What is the standard error of the estimate? What proportion of the sample variation in crime rate is explained by variability in the predictor variables? What proportion is unexplained?
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.8 277.9 253.3 259.2 279.0 258.0 274.0 262.4 Height 1784.7 1771.2 1676.0 1645.9 1858.9 1710.1 1788.9 1737.0 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.8 mm is enter your response heremm. (Round to the nearest integer as needed.)
Click this Video for Finding Correlation Coefficient by Using Formula. Yessica thought that there was a correlation between the amount of time students slept the evening before their final exam and their final exam grade. She took an SRS of 5 students in her class and recorded the sleep time the evening before their final exam, and their final exam score. Let the sleep time the evening before the final exam represents the explanatory variable, and the final exam score be the response variable. Below is a sample data set. x (hour) 8 9 3 9 1 2 3 4 5 Calculate the sample correlation coefficient (r) to three decimal places. r= 9 Why would we expect the correlation coefficient to be positive? As the sleep time increases, exam score increases As the sleep time increases, exam score decreases As the sleep time decreases, exam score increases There is no change in exam score to sleep time C E y (score) 91 94 51 100 90 C A
we can conclude from correlation calculation that one variable caused the other variable. True or False.
In a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.7 -0.4 -0.79 0.4
The correlation between a variable and itself is always equal to 0.5 -1 1
A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a. For each 1-cm increase in height, weight tends to increase by about 0.32 kg b. There is no association between weight and height c. Height accounts for about 32% of the total variability in weight d. The correlation between weight and height is about 0.32
Summarize the definitions for linear correlation, quadratic correlations and exponential correlations. Provide an example graph of each of the types of correlation along with your summarized definitions.

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