You are investigating the link between the number of customers visiting a website (X) and the number ofproducts sold (Y). You get the following results in Excel:Sales vs Visitors100959085807570y = 0.0088x + 18.48R2 = 0.70266560555050005500600065007000750080008500Visitors(a) What is the slope of the regression line?(see information in the picture -equation located below the regression line)(b) Write the regression equation, y =(see information in the picture -equation located below the regression line)(c) If the website has 6,000 visitors this month, how many sales per month would you expect?Sales

Answered: Image /qna-images/answer/b63ebd48-4bb8-4378-b8b6-5625fad375a9.jpg

Answered: bu are investigating the link between…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
bThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.
In a fisheries researcher's experiment, the correlation between the number of eggs in the nest and the number of viable (surviving) eggs for a sample of nests is r = 0.67. The equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y = 0.72x + 17.07. For a nest with 140 eggs, what is the predicted number of viable eggs?
A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model: E(y) = β0 + β1x, where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 74 houses in East Meadow, the following results were obtained: = 74.80 + 19.84 xGive a practical interpretation of the estimate of the slope of the least squares line. For a house with 0 rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the appraised value to increase $74,800. For each additional room in the house, we estimate the appraised value to increase $19,840. For each additional dollar of…
When is the standard error of the estimate (in linear regression) large? When the X values are very close to the Y values. When the residuals are large on average. When the average distance between actual Y scores and predicted Y scores is small. When the coefficient of determination is small.
Suppose Connie used regression to predict the height of a woman’s current boyfriend by using her own height as the explanatory variable. Height was measured in feet from a sample of 100 women undergraduates, and their boyfriends, at De La Salle Lipa. Now, suppose that the height of both the women and the men are converted to centimeters. The impact of this conversion on the slope is: the magnitude of the slope will change neither the sign nor magnitude of the slope will change the sign of the slope will change both the sign and magnitude of the slope will change
Different hotels in a certain area are randomly selected, and their ratings and prices were obtained online. Using technology, with x representing the ratings and y representing price, we find that the regression equation has a slope of 125 and a y-intercept of −391. What is the equation of the regression line?
Is the regression equation for predicting Y in terms of X the same as the regression line for predicting X in terms of Y?
A scientific foundation wanted to evaluate the relation between y= salary of researcher (in thousands of dollars), x1= number of years of experience, x2= an index of publication quality, x3=sex (M=1, F=0) and x4= an index of success in obtaining grant support. A sample of 35 randomly selected researchers was used to fit the multiple regression model. Parts of the computer output appear below. Based from the table, what is the constant term of the multiple linear regression?

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