A grocery store is interested in how customers' income affects the amount of moneythey spend each week. A regression analysis is performed to test this analysis. Theresults of the analysis are that the intercept is $17.80 and the slope is 0.0012. Theresidual of one customer's weekly spending was $54.12. Is this household anexample of over or under predictionand expenditures inverse or directand is the relationship between incomeunder-prediction, inverseunder-prediction, directover-prediction, directover-prediction, inverseevidence of diet equity for this grocery store's customers

Given : Regression equation : Y^ = 17.80 + 0.0012 x

Answered: A grocery store is interested in how…

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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The following multiple linear regression depicts the sales of Unilever Corporation: S = 10- 0.20P + 0.06TA + 0.08RA Where:S: Sales (Unit);P: Price ($/Unit);TA: Television advertising ($)RA: Radio advertising ($). 1. Interpret the slopes;2. Is television advertising more effective than radio advertising? Why or why not?3. What is the effect of a $1 increase in price, a $3 increase in TV advertising, and a $2decrease in radio advertising on sales?4. What is the effect of a $2 decrease in price, a $2 decrease in TV advertising, and a $4increase in radio advertising on sales?
The table gives the amount of money (in dollars) spent on football by a major university. Letting a represent the number of years since 2005, and letting y represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2005 2006 2007 2008 2009 2010 Dollars spent on football 165,000 196,000 210,000 226,000 242,000 279,000 The equation of the line of best fit is: Hint Predict the amount of money that will be spent on football in the year 2024. +A Predict the amount of money that will be spent on football in the year 2041.
The number of megapixels in a digital camera is one of the most important factors in determining picture quality. But, do digital cameras with more megapixels cost more? The following data show the number of megapixels and the price ($) for 10 digital cameras(Consumer Reports, March 2009). Use these data to develop an estimated regression equation that can be used to predict the price of a digital camera given the number of megapixels. Brand and Model Megapixels Price (S) Canon PowerShot SD1100 IS Casio Exilim Card EX-510 8 180 200 230 10 Sony Cyber-shot DSC-T70 Pentax Optio M50 Canon PowerShot G10 120 470 15 8 Canon PowerShot A590 IS Canon PowerShot El 140 180 10 12 Fujifilm FinePix FOOFD Sony Cyber-shot DSC-W170 Canon PowerShot A470 310 10 250 110
#b. Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables (to 2 decimals).
The owner of Showtime Movie Theatres Inc. would like to predict weekly gross revenue as a function of advertising expenditures. Use 0.05 level of significance.Historical data for a sample of eight weeks follow: Weekly Gross Renvenue Telelvision Newspaper Adveritising ($1000s) Adveritising a) Develop an estimated regression equation to predict weekly gross revenue as a function of advertising expenditures. ($1000s) ($1000s) 96 5.0 1.5 90 20 2.0 b) Explain in simg when 1000s are spent on television and newspaper then the revenue will in 95 4.0 1.5 92 2.5 2.5 c) Predict weekly 95 3.0 3,3 94 3.5 2.3 d) What is the R2 value? 0.9190 94 2.5 4.2 94 3.0 2.5 e) What is the Hypothesis Test? Use the t test to determine the significance of each independent variable. State the t test, p-values, and your conclusion. g) What is the cor Reject Ho SUMMARY OUTPUT pression Statstica Mutiple R 0.958663444 0.9190356 R Square 0.88664984 Adusted R Square Standard Eror 0.642587303 Obervations ANOVA Syaicance…
Data was collected for a regression analysis where sleep quality (as a percentage) depends on the amount of caffeine consumed in a day (measured in mg). bo was found to be 92.7, b₁ was found to be -0.76, and R² was found to be 0.86. Interpret the slope of the line. O On average, each one mg increase in caffeine consumed increases a person's sleep quality by 92.7%. O On average, when x = = 0, a person has a sleep quality of -0.76%. O On average, each one mg increase in caffeine consumed decreases a person's sleep quality by 0.76%. On average, when x = 0, a person has a sleep quality of 92.7%. O We should not interpret the slope in this problem. O We should interpret the slope in this problem, but none of the above are correct.
answer both please and explain well. Anna company sells coffee products to various customers. In recent years, profits have been declining. The CFO of the company investigated the reasons for the profit decline and performed regression analysis for sales and costs. The CFO determined that sales depend on product price, delivery speed, customer services, and marketing expenses. She also determined that total costs consist of variable costs of $25 per unit and fixed costs of $56,000. Marketing expenses have a coefficient of determination of 75% related sales. Questions 1. Define the coefficient of determination and explain what it means in this scenario. 2. Express the relationship between total costs and variable costs for Anna Company using a regression equation. Explain each element of the equation.
Medical records indicate that people with more education tend to live longer; the correlation is 0.91. The slope of the linear model that predicts lifespan from years of education suggests that on average people, tend to live 1.46 extra years for each additional year of education they have. The slope of the line that would predict lifespan from years of education is: 1.46 91 146 0.91
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child ( height), height of the mother ( mothersheight ), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 2 104.004228 8.49446952 37 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE = = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What are the null and alternative hypotheses…
A researcher is interested in seeing if there is a relationship the amount of time in minutes a person spends in their car during the day and the number of minutes that person spends listening to music. Car Time 63 14 241 93 140 3 91 45 22 Music Time 73 16 260 142 206 31 78 47 29 a. Find the equation of the regression line and use it to make a prediction of the dollars purchased by someone who visits the website 4 times.b. Interpret the slope in the context of the problem.c. Interpret the y-intercept in the context of the problem or state why it is not applicable.d. State the null and alternative hypotheses for the hypothesis test of correlation e. Find the pvalue and state your conclusion in the context of the study.
A researcher wanted to predict the sodium content (in milligrams) of beef hot dogs by looking at the calorie content. His findings from a sample of 10 beef hot dogs are summarized below. The regression equation for his data is ŷ= -299.48 + 4.35 x Calories 186 181 176 149 184 190 158 139 175 148 Sodium 495 477 425 322 482 587 370 322 479 375 Interpret the slope in terms of this problem. If appropriate, find the sodium content if the calorie content is 170. If not, why? If appropriate, find the sodium content if the calorie content is 136. If not, why?
2. The table below lists the annual land-line phone cost per costumer: Year 2012 2013 2014 2015 Cost ($) a. 692 610 Find a linear regression model for this data b. Interpret the slope of the model 580 C. Predict the annual land-line phone cost per customer in 2022 495 2016 434

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