МАТHSTATISTICSWe made a survey with the number ofpeople in a household and the numberof times they get sick throughout theyear. In a household, 3 people get sickonce, 4 people get sick twice, 2 peopleget sick three times and 1 person getssick 4 times. 3) Give the equation of the regressionline a) plug an acual x value into theequation and discuss the differencebetween the real Y and the predicted Yb) Use the equation to make a predictionc) Discuss the domain of your data

The equation of a regression line is of the form Y=aX+b where X is the independent variable and Y is…

Answered: 3) Give the equation of the regression…

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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Bookstore sales revisited Recall the data we saw inChapter 6, Exercise 3 for a bookstore. The manager wantsto predict Sales from Number of Sales People Working.Here is the regression analysis of Sales vs. Number ofSales People Working.a) Write the regression equation. Define the variablesused in your equation.b) What does the slope mean in this context?c) What does the y-intercept mean in this context? Is itmeaningful?d) If 18 people are working, what Sales do you predict?e) If sales for the 18 people are actually $25,000, what isthe value of the residual?f) Have we overestimated or underestimated the sales?
can anyone help with this please? Thank you.
A shoe store developed the following estimated regression equation relating sales to inventory investment and advertising expenditures. ŷ = 25 + 12x, + 7x2 where X1 = inventory investment ($1,000s) X2 = advertising expenditures ($1,000s) y = sales ($1,000s). (a) Predict the sales (in dollars) resulting from a $15,000 investment in inventory and an advertising budget of $11,000. $ (b) Interpret b, and b, in this estimated regression equation. Sales can be expected to increase by $ for every dollar increase in inventory investment when advertising expenditure is held constant. Sales can be expected to increase by $ for every dollar increase in advertising expenditure when inventory investment is held constant.
A shoe store developed the following estimated regression equation relating sales to inventory investment and advertising expenditures. ŷ = 25 + 10x1 + 8x2 where x1 = inventory investment ($1,000s) x2 = advertising expenditures ($1,000s) y = sales ($1,000s). (a) Predict the sales (in dollars) resulting from a $15,000 investment in inventory and an advertising budget of $11,000. $ ----------------- (b) Interpret b1 and b2 in this estimated regression equation. Sales can be expected to increase by $ ---------------for every dollar increase in inventory investment when advertising expenditure is held constant. Sales can be expected to increase by $-----------------for every dollar increase in advertising expenditure when inventory investment is held constant.
The data below is the amount of tar and nicotine in a sample of different brands of 100 mm, non-menthol, filtered cigarettes. a) Using the calculator, find a linear regression model that gives the amount of nicotine, N, as a function of the amount of tar, t. Write the function below. N(t)= b) use this model of nicotine to estimate the amount of nicotine in a cigarette if the amount of tar is listed as 21 mg. c) What is the correlation coefficient, r, for this model? r= Tar 5 16 17 13 13 14 9 15 2 15 13 14 15 16 7 17 Nicotine 0.4 1 1.2 0.8 0.8 1 0.8 1 0.2 1.1 0.8 1 0.9 1.1 0.6 1.3
Gas mileage As the example in the chapter indicates,one of the important factors determining a car’s FuelEfficiency is its Weight. Let’s examine this relationshipagain, for 11 cars.a) Describe the association between these variablesshown in the scatterplot.b) Here is the regression analysis for the linear model.What does the slope of the line say about thisrelationship?c) Do you think this linear model is appropriate? Use theresiduals plot to explain your decision.
please do d)
A market researcher found a correlation between advertising budgets for fliers (a, in thousands of dollars) and sales (s, in thousands of dollars) at local stores of a national pharmacy chain. Linear regression produced the equation s = 25.4a + 4.5, with r = 0.57. A local pharmacy in the chain had sales of $875 000 with a flier advertising budget of $50 000. The store manager was concerned about the accuracy of the model. a) The manager should not be concerned because the model predicts this level of sales. b) The correlation coefficient indicates a moderate correlation, so variation should be expected. c) The correlation coefficient indicates a moderate correlation, so all predictions with the model will be inaccurate. d) The manager should be concerned
You are conducting a multiple regression analysis to determine if there is a linear relationship between y, the number of applications to in-state universities and two factors: ×1, the state's unemployment rate and ×2, the average grant provided to in-state students. Ten observations are available. The analysis produced the estimated regression equation below. y = 6.5 + 2.2×1 + 4.6×2 The standard error for the intercept, unemployment rate are 9.34, and 1.23, respectively. Is the coefficient for the unemployment rate variable statistically significant at the 5% significance level? Explain. A) No, since stat = 1.39 for this coefficient, which is less than 2.365. B) No, since the p-value for this coefficient is .072, which is greater than 05 C) None of these D) No, since the p-value for this coefficient is .072, which is greater than .05
Below is a scatterplot of a student’s SAT score and their college GPA. Suppose we want to decide if a student’s SAT score and their GPA are linearly related. What does this scatterplot tell us of the relationship? Support your answer. SAT score_GPA scatterplot.pdf
The following data show the brand, price (S), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ý = 22.522 + 0.335x, where x = price ($) and y = overall score. Brand Price ($) Score A 180 78 B 150 73 95 63 70 58 E 70 38 35 26 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination rt. (Round your answer to three decimal places.) 12 = Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line did not provide a good fit as a small proportion of the variability in y…
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3) Give the equation of the regression line a) plug an acual x value into the equation and discuss the difference between the real Y and the predicted Y b) Use the equation to make a prediction c) Discuss the domain of your data